Category Archives: Data Science/Artificial Intelligence

Data Science/Artificial Intelligence

Where To Get Free GPU Cloud Hours For Machine Learning

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An Introduction To The Need For Free GPU Cloud Compute

GPUs were once used solely for video games. Now, they power machine learning models around the world with their unique configuration and processing power. Getting free GPU cloud hours has become a need for many machine learning practitioners and hobbyists.

In brief summary, your traditional CPUs are good for complex calculations performed sequentially, while GPUs are excellent for many simple parallel calculations performed across multiple cores. GPUs take advantage of the fact that their hardware structure and architecture is meant to do shallow calculations in parallel faster than a CPU can do them in sequence.

That makes them the perfect fit to train deep neural networks. The new RAPIDS framework also allows us to extend this to regular machine learning work and to data visualization tasks. This has led to speedups that can take algorithms that normally take upwards of 30 minutes, and reduce them to speeds of 3 seconds.

How do we take best advantage of this scenario? Fortunately, there are many GPU cloud providers that are offering free GPU cloud compute time so you can run experiments and try out these new processes.

1 – Google Colab

Google Colab offers you the opportunity to easily upload Python Notebooks into the cloud and interact with Github/Git to pull repositories to modify or to push work in Colab files to Github. If you have a Google Drive account, you can easily access your Colab notebooks in your Google Drive. You’ll be able to easily switch into GPU runtime mode by clicking Runtime on the top of the menu bar.

Specs:

  1. Free access to Tesla K80 GPU
  2. Up to 12 hours of consecutive runtime per day
  3. 12 GB of RAM

2- Kaggle GPU (30 hours a week)

Kaggle is a platform that allows data scientists and machine learning engineers the ability to demonstrate their capabilities with creating accurate models.

They offer 30 hours a week of free GPU time through their Kernels. The hardware they use are NVIDIA TESLA P100 GPUs. The intent of Kaggle is to offer them for deep learning, and they don’t accelerate workflows with other processes — though it’s possible you might try using RAPIDS with pandas and sci-kit learn like functions.

While the GPU time is offered for free, they do offer certain recommendations. You should, as with Google Compute, monitor when you’re using GPU time and switch it off when you’re not. Even if it’s monetarily free, you’ll want to be careful with the time you’re allotted. The limit of six hours of consecutive runtime means that you won’t be able to train complex state-of-the-art models that often take days to fully train.

Specs:

  1. Free access to NVIDIA TESLA P100 GPUs
  2. Up to 30 hours a week of free GPU time, with six hours of consecutive runtime
  3. 13 GB of RAM

3- Google Cloud GPU

For each Google account that you register with Google Cloud, you can get $300 USD worth of GPU credit. That can get you over 850 hours of GPU training time on their Nvidia Tesla T4. In practice though, you’ll want to try more powerful GPU instances with Google Cloud since you can get a baseline free with Google Colaboratory. You’d be able to train relatively powerful models in that time, or use it to practice machine learning work with RAPIDS. This tutorial goes over the setup of the GPU.

Note that when you set up the virtual machine, if you don’t turn it off when you’re not using it, you’ll still get billed, and you’ll get billed if you go past the $300 USD quota, so be careful to avoid unneeded charges.

4- Microsoft Azure

Microsoft Azure also offers a $200 credit when you sign up, which you can use for Azure’s GPU options. This blog post explains how you can get up to $500 a year in credits.

5- Gradient (Free community GPUs)

Tired of using Google/Microsoft infrastructure or want to try something new? Gradient offers free community GPU cloud usage attached to their notebooks. This blog post offers a more in-depth perspective on their community notebooks.

6- Twitter Search for Free GPU Cloud Hours

You can always keep an eye out for promo codes and other cloud providers offering free GPU Cloud Hours by looking at Twitter and searching for relevant keywords.

With the right search query, you’ll be alerted to the latest offerings. I’ll try to retweet a few if you want to follow my personal Twitter account.

7-An alternative: build your own machine learning computer with GPU

If you’re tired of more limited cloud compute constraints, from cost to execution time limits, one solution might be to go as far as building our your own machine. Your only constraint is the power cost, which can be higher than expected with these powerful machines.

Still, you’ll be able to fully control your configuration and the hardware you use. It can be very cost-efficient, since you can run your own machine 24/7 — and you can build your own machine learning GPU rig for less than $1,000.

Data Science/Artificial Intelligence, Learning Lists, Resources Lists

49 Essential Resources To Learn Python

BY

Hi, I’m Roger, and I’m a self-taught data analyst/scientist (but only on my good days). I spent a lot of time thinking about Python — and here’s a compilation of resources that helped me learn Python and can hopefully help you.

I’ve broken it down to:

Beginner resources for those just starting with programming and Python

Intermediate resources for those looking to apply the basics of Python knowledge to fields like data science and web development

Advanced resources for those looking to get into concepts like deep learning and big data with Python

Exercises that help practice and cement Python skills in practice

Beginner Resources To Learn Python

learn python

1- Welcome to Python.org

The official Python site offers a good way to get started with the Python ecosystem and to learn Python, including a place to register for upcoming events, and documentation to get started.

2-Learn Python the Hard Way

An online book with a paid and a free version. The free version goes into an outline of the content and can be a useful to-do list.

3-Basic Data Types in Python – Real Python

RealPython dives into the different data types in Python in detail. Learn the difference between floating point and integers, what special characters can be used in Python and more.

4-How to Run Your Python Scripts – Real Python

This simple intro to Python scripts through the command line and text editors will get you up and running for your first Python experiments — a handy tool to get you started as you learn Python.

5-Python Tutorial: Learn Python For Free | Codecademy

Codecademy offers a free interactive course that helps you practice the fundamentals of Python while giving you instant, game-like feedback. A great device for learning Python for those who like to practice their way to expertise.

6-Google’s Python Class | Python Education | Google Developers

The official Python development class from Google’s developers. This tutorial is a mix of interactive code snippets that can be copied and run on your end and contextual text. This is a semi-interactive way to learn Python from one of the world’s leading technology companies.

7-Learn Python – Free Interactive Python Tutorial

This interactive tutorial relies on live code snippets that can be implemented and practiced with. Use this resource as a way to learn interactively with a bit of guidance.

8-Jupyter Notebook: An Introduction – Real Python

Want an easy, intuitive way to access and work with Python functions? Look no further than Jupyter Notebook. It’s much easier to work with than the command line and different cobbled together scripts. It’s the setup I use myself. This tutorial will help you get started on your path to learn Python.

9-Python Tutorial – W3Schools

W3Schools uses the same format they use to teach HTML and others with Python. Practice with interactive and text snippets for different basic functions. Use this tutorial to get a firm grounding in the language and to learn Python.

10-Python | Kaggle

Kaggle is a platform which hosts data science and machine learning competitions. Competitors work with datasets and create as accurate of a predictive model as possible. They also offer interactive Python notebooks that help you learn the basics of Python. Choose the daily delivery option to have it become an email course instead.

11-Learning Python: From Zero to Hero – freeCodeCamp.org

This text-based tutorial aims to summarize all of the basic data and functional concepts in Python. It dives into the versatility of the language by focusing on the object and class portions of the object-oriented part of Python. By the end of it, you should have a neat summary of objects in Python as well as different data types and how to iterate or loop over them.

12-BeginnersGuide – Python Wiki

This simple tutorial on the official Python Wiki is chock-full of resources, and even includes a Chinese translation for non-English speakers looking to learn Python.

13-Python Tutorial – Tutorialspoint

Set up in a similar fashion to W3Schools, use Tutorialspoint as an alternative or a refresher for certain functions and sections.

14-Python (programming language) – Quora

The Quora community is populated with many technologists that learn Python. This section devoted to Python includes running analysis and pressing questions on the state of Python and its practical application in all sorts of different fields, from data visualization to web development.

15-Python – DEV Community – Dev.to

Dev.to has user-submitted articles and tutorials about Python from developers who are working with it every day. Use these perspectives to help you learn Python.

16-Python Weekly: A Free, Weekly Python E-mail Newsletter

If you’re a fan of weekly newsletters that summarize the latest developments, news, and which curate interesting articles about Python, you’ll be in luck with Python Weekly. I’ve been a subscriber for many months, and I’ve always been pleased with the degree of effort and dedication placed towards highlighting exceptional resources.

17-The Ultimate List of Python YouTube Channels – Real Python

For those who like to learn by video, this list of Youtube channels can help you learn in your preferred medium.

18-The Hitchhiker’s Guide to Python

Unlike the rest of the resources listed above, the Hitchhiker’s guide is much more opinionated and fixated on finding the best way to get set up with Python. Use it as a reference and a way to make sure you’re optimally set up to be using and learning Python.

19-Python: Online Courses from Harvard, MIT, Microsoft | edX

edX uses corporate and academic partners to curate content about Python. The content is often free, but you will have to pay for a verified certificate showing that you have passed a course.

20-Python Courses | Coursera

Coursera’s selection of Python courses can help you get access to credentials and courses from university and corporate providers. If you feel like you need some level of certification, similar to edX, Coursera offers a degree of curation and authentication that may suit those needs.

Intermediate Resources

learn python

21-Getting started with Django | Django

The official Django framework introduction will help you set up so that you can do web development in Python.

22-LEARNING PATH: Django: Modern Web Development with Django

This resource from O’Reilly helps fashion a more curated path to learning Django and web development skills in Python.

23-A pandas cookbook – Julia Evans

I learned how to clean and process data with the Pandas Cookbook. Working with it enabled me to clean data to the level that I needed in order to do machine learning and more.

It works through an example so you can learn how to filter through, group your data, and perform functions on it — then visualize the data as it needs be. The Pandas library is tailor-built to allow you to clean up data efficiently, and to work to transform it and see trends from an aggregate-level basis (with handy one-line functions such as head() or describe).

The Pandas cookbook is the perfect intro to it.

24-Newest ‘python’ Questions – Stack Overflow

The Stack Overflow community is filled with pressing questions and tangible solutions. Use it a resource for implementation of Python and your path to learn Python.

25-Python – Reddit

The Python subreddit offers a bunch of different news articles and tutorials in Python.

26-Data Science – Reddit

The Data Science subreddit offers tons of resources on how to use Python to work with large datasets and process it in interesting ways.

27-Data science sexiness: Your guide to Python and R

I wrote this guide for The Next Web in order to distinguish between Python and R and their usages in the data science ecosystem. Since then, Python has pushed ever-forward and taken on many of the libraries that once formed the central basis of R’s strength in data analysis, visualization and exploration, while also welcoming in the cornerstone machine learning libraries that are driving the world. Still, it serves as a useful point of comparison and a list of resources for Python as well.

28-Data Science Tutorial: Introduction to Using APIs in Python – Dataquest

One essential skill when it comes to working with data is to access the APIs services like Twitter, Reddit and Facebook use to expose certain amounts of data they hold. This tutorial will help walk you through an example with the Reddit API and help you understand the different code responses you’ll get as you query an API.

29-Introduction to Data Visualization in Python – Towards Data Science

Once you’re done crunching the data, you need to present it to get insights and share them with others. This guide to data visualization summarizes the data visualization options you have in Python including Pandas, Seaborn and a Python implementation of ggplot.

30-Top Python Web Development Frameworks to Learn in 2019

If you want a suite of options beyond Django to develop in Python and learn Python for web applications, look no further than this compilation. The Hacker Noon publication will often feature useful resources on Python outside of this article as well. It’s worth a follow.

Advanced Resources

learn python

31-Beginner’s Guide to Machine Learning with Python

This text-based tutorial helps introduce people to the basics of machine learning with Python. Towards Data Science, the Medium outlet with the article in question, is an excellent source for machine learning and data science resources.

32-Free Machine Learning in Python Course – Springboard

This free learning path from Springboard helps curate what you need to learn and practice machine learning in Python.

33-Machine Learning – Reddit

The Machine Learning subreddit oftentimes focuses on the latest papers and empirical advances. Python implementations of those advances are discussed as well.

34-Python – KDnuggets

KDNuggets offers advanced content on data science, data analysis and machine learning. Its Python section deals with how to implement these ideas in Python.

35-Learn Python – Beginner through Advanced Online Courses – Udemy

Udemy offers a selection of Python courses, with many advanced options to teach you the intricacies of Python. These courses tend to be cheaper than the certified ones, though you’ll want to look carefully at the reviews.

36-A Brief Introduction to PySpark – Towards Data Science

This introduction to PySpark will help you get started with working with more advanced distributed file systems that allow you to deal and work with much larger datasets than is possible under a single system and Pandas.

37-scikit-learn: machine learning in Python

The default way most data scientists use Python is to try out model ideas with scikit-learn: a simple, optimized implementation of different machine learning models. Learn a bit of machine learning theory then implement and practice with the scikit-learn framework.

38-The Next Level of Data Visualization in Python – Towards Data Science

This tutorial walks through more advanced versions of data visualizations and how to implement them, allowing you to take a preview of different advanced ways you can slice your data from correlation heatmaps to scatterplot matricies.

39-Machine Learning with Python | Coursera

Coursera’s selection of courses on machine learning with Python are veryw well-known. This introduction offered with IBM helps to walk you through videos and explanations of machine learning concepts.

40-Home – deeplearning.ai

Deeplearning.ai is Andrew Ng’s (the famous Stanford professor in AI and founder of Coursera) attempt to bring deep learning to the masses. I ended up finishing all of the courses: they offer certification and are a refreshing mix of both interactive notebooks where you can work with the different concepts and videos from Andrew Ng himself.

41-fast.ai · Making neural nets uncool again

This curated course on deep learning helps break down section-by-section aspects of machine learning. Best of all, it’s completely free. I often use fast.ai as a refresher or a deep dive into a deep learning idea I don’t quite understand.

42-Learn and use machine learning | TensorFlow Core | TensorFlow

This tutorial helps you use the high-level Keras component of TensorFlow and Google cloud infrastructure to do deep learning on a set of fashion images. It’s a great way to learn and practice your deep learning skills.

Exercises To Learn Python

learn python

43-Datasets | Kaggle

Kaggle offers a variety of datasets with user examples and upvoting to guide you to the most popular datasets. Use the examples and datasets to create your own data analysis, visualization, or machine learning model.

44-Practice Python

Practice Python has a bunch of beginner exercises that can help you ease into using Python and practicing it. Use this as an initial warmup exercise before you tackle different projects and exercises.

45-Python Exercises – W3Schools

The Python exercises on W3Schools follow the sections in their tutorials, and allow you to get some interactive practice with Python (though the exercises are in practice very simple).

46-Solve Python | HackerRank

HackerRank offers a bunch of exercises that require you to solve without any context. It’s the best way to practice different functions and outputs in Python in isolation (though you’ll still want to do different projects to be able to cement your Python skill.) You’ll earn points and badges as you complete more challenges. This certainly motivates me to learn more. A very useful sandbox for you to learn Python with.

47-Project Euler: About

Project Euler offers a variety of ever-harder programming challenges that aim to test whether you can solve mathematical problems with Python. Use it to practice your mathematical reasoning and your Pythonic abilities.

48-Writing your first Django app, part 1 | Django documentation | Django

This documentation helps you get on the ground with your first Django app, allowing you to use Python to get something up on the web. Once you’ve started with it, you can build anything you want.

49-Top 100 Python Interview Questions & Answers For 2019 | Edureka

Should you ever be in an interview where your Python skills are at question, this list of interview questions will help as a useful reminder and refresher and a good way for you to practice and cement different Python concepts.

Cryptocurrency/Blockchain, Data Science/Artificial Intelligence, Learning Guides, Web Development/UX Design

The Best Programming Language to Learn: a Definitive Guide.

BY

Most people approach me often ask the same question: what’s the best programming language to learn? The answer is: it depends. I wrote an article that declared the mathematical and analytical skills behind programming are what really matter. Now, I’m a bit wiser –so I’ve had the time to break it down into a more tangible and useful answer.

What is the best programming language to learn? It depends, and you can be much more efficient with your time by knowing which programming language is the best for what you want.

So I’ve broken down the best programming language to learn for a variety of needs. I took into consideration the amount of time you need to invest in a programming language and the power you need for different tasks.

You want a versatile, general-purpose language that can be narrowed to different tasks without too much hassle.

The best programming language to learn, source: Pixabay

Python is a programming ecosystem with a vast array of communities and libraries for different use cases. From Django for web development to Pandas for data, Python is the Swiss-army knife of programming languages. Its syntax is also very approachable, and there are tons of tutorials and documentation for beginners. These libraries tend to be almost like learning a new syntax or paradigm.

Still, the ability to import libraries of different kinds and have a relatively consistent experience puts Python up here. If you want a simple intro-level programming language, Python is a great choice. With the second most active community on Github (at about slightly under 15% of all active users), you’re sure to find many projects and usable components to play with in Python.

Python Resources:

Learn Python

This step-by-step tutorial teaches Python in an accessible manner. It makes it easy for you to go through the basics of everything from data structures to how to structure functions. That makes it ideal for people who don’t have programming experience.

11 Beginner Tips for Learning Python

This set of tips is a handy primer for not only learning Python, but really a generalizable way to learn and practice all kinds of different programming languages.

Zen of Python

The Zen of Python is more philosophical than practical. Still, it serves as a useful reminder of the ideals of Python programming and the ideals one should strive for. Simple, after all, is better than complex.

Codecademy: Learn Python for Free

This free interactive Codecademy course is a great way to start with Python basics and syntax. Use it to cement the theory you’ve learned and start practicing with Python.

Web Development Using Python and Django

Python is versatile mostly because there are tons of documentation and frameworks. Django is a content management system built on Python. This curated curriculum will help you learn what you need to build fully-fledged websites with Python by tapping into Django.

You’re interested in working with data, in a data analysis or data science capacity or as a data engineer/machine learning engineer

The best programming language to learn, source: Pixabay

When it comes to the data ecosystem, you’ll want to learn SQL as a domain-specific way to work with data. However, SQL is not a general purpose programming language, but merely a utility to deal with one data type over another. You can think of it as a complex interface to the .sql data format.

There are two obvious choices here, R or Python. Academics tend to use R. It used to have the bulk of good data visualization and analytics libraries. Now, however, the open-source Python community has sprinted to catch up. With the advent of machine learning, the balance has shifted towards Python.

Previously, I wrote about both R vs. Python a few years ago. I came out with the conclusion that both had their uses. It was perhaps best to learn both. Practically speaking however, if you’re dealing with large amounts of data ,Python gets the slightest of edges here as the best programming language to learn for data purposes, especially if you’re coming from a programming background in the first place — it’ll be easier for you to work with Python’s syntax than R.

Python and Data Resources:

Data Science Sexiness, R vs Python

I wrote this guide describing the differences between R and Python, and listed a bunch of learning resources for both. I concluded it might be best to learn both, but I’ve since become immersed in the Python ecosystem when it comes to data.

Introduction to the Machine Learning Stack

I wrote this tutorial which summarized the frameworks and libraries you need to know to get started doing machine learning with different frameworks, most of which have Python ports or APIs so you can write code in Python (or in any case, Pythonic syntax) and get started.

Pandas Cookbook

Pandas was where I really started practicing programming: wrangling datasets is a passion of mine. This tutorial walks through how to use Pandas with an example dataset. You’ll learn how to import data of different formats, transform it in different ways, and then extract and export it.

How to do Common Excel and SQL Tasks in Python

Another tutorial I wrote helps you port some of the logic and functions in both Excel and SQL to Python. Do everything from importing data to analyzing it in the summary form or filtered form you’ve come to expect.

Machine Learning in Python

This curated curriculum takes your Python skills and helps you learn machine learning theory. By pairing the two, you can start working on machine learning projects by the end.

You want to build mobile applications that require access to native functionalities such as a phone camera

The best programming language to learn, source: Pixabay

Depending on what ecosystem you want to build in, the best language is quite selective. If it’s the Android ecosystem, you’re going to have to learn Java.

Meanwhile, if you’re interested in building for the iOS ecosystem and getting placed on Apple’s App Store, you’ll have to learn Swift. Swift is Apple’s official programming language for its laptops based on MacOS, iOS, or for Apple Watch apps.

There are other ecosystems such as Microsoft, which needs C#. There are also cross-platform programming languages such as React Native. Microsoft doesn’t have as much market share as either Android phones or iPhones. Reach Native doesn’t have access to as many of the specific native functions on either device (and you’ll have to compile down to Swift or Java to get those features). Still, they’re handy languages to know about, even if they might not be the best — unless you were trying to launch on as many platforms as possible.

Resources:

Android Application Development

Learn the ins and outs of Android application development, from building an application to how debug common issues.

Introduction to Swift

This interactive set of courses will help you get through the basics of Swift and building iOS applications. You’ll pass to an intermediate stage/course once done.

400+ Swift Language Video Tutorials

If video learning is more of your thing, look no further than this series of video tutorials on Swift topics. They’re broken down into sets of continuous playlists, so you can pick and choose a particular curated playlist or choose a particular topic to focus on.

React Native Tutorial

This React Native tutorial and documentation from Facebook is a fairly comprehensive overlook on how the versatile cross-platform framework works.

Expo

If you want to speed up your app development cycle across multiple platforms and want to stick to using JavaScript for your mobile app coding, Expo can be a quick, iterable solution.

You want to build the latest web applications

The best programming language to learn, source: Pixabay

For web development, PHP used to be the default, powering everything from e-commerce sites to WordPress itself. Now though, most people have shifted to JavaScript and different frameworks within it. There’s a bit of a fight going on between the major tech companies on building web development interfaces, with Google sponsoring Angular.js while Facebook builds React.js. In practice, these mega-corporations are building the most recent web development frameworks for their needs and then open-sourcing and supporting the developments.

Both are doing it on JavaScript, so if you want to build the latest and greatest in web applications and benefitting from their work and others, look no further. JavaScript is the best programming language to learn for cutting-edge web development applications. It boasts the most active community on Github with over 22% of all active users participating in the JavaScript community.

JavaScript Resources:

An Introduction to JavaScript

This wiki gives you a broad overview of JavaScript and how it serves web content. You’ll understand basic concepts like how JavaScript interacts with browsers once you’re done. You can then take the next step towards learning more powerful frameworks.

jQuery Intro

jQuery is a powerful JavaScript library that allows you to do powerful things such as animations with a one-word function. Use this tutorial to grasp the basics and combine it with HTML and CSS to serve dynamic web content easily.

How to Learn React — A roadmap from beginner to advanced

This roadmap will help you conceptualize your roadmap for learning JavaScript frameworks like React.js.

React.js, Codecademy

React.js is a powerful framework to create web interfaces. Practice with this Codecademy course.

MEAN Stack Tutorial

This tutorial will summarize all of your theory-based learning towards building a MEAN stack application that will serve as a Reddit clone. This is a full-featured web app that has user authentication, databases through MongoDB, routing and linking through Express and a back-end server through Node.js and a combination of Angular (though React can also be used in this situation). At the end of this tutorial, you should be able to extend your learnings and build full-fledged web apps.

You need to do something that requires very high performance (ex: cryptography)

The best programming language to learn, source: Pixabay

For tasks that require a lot of compute power and manipulation of lower-level processes such as dynamic memory allocation, it’s best to work in C++. Lower-level tasks require more efficient implementations of memory and space and involve working closer to hardware in order to get higher performance. C++ is lower-level than all of the languages discussed about yet is also still readable and compilable enough so that with some practice, you can be conversant in it.

Python can access lower-level functions with something called Cython using the C programming language. Bitcoin is coded in C++, including its advanced cryptographic features. In order to do something at a highly performant level, you’ll likely need to access C++ and its superior lower-level flexibility.

C++ Resources:

Introduction to C++

This edX course, provided by Microsoft, will help you get started with C++ and its basics.

C++ Language

This wiki helps you tackle C++ from A to Z. There’s different sections dedicated to everything from how to write functions in the language to how to deal with different variables and types.

C++ Codecademy

Consolidate all of the theory you’ve learned by practicing with this free C++ course with Codecademy.

Cython Tutorial

Cython allows you to access C++ functions while using Python, combining the versatility of the Python ecosystem with the power of C++.

C++ Cryptography Libraries

If you want to look into advanced functions such as cryptography, look through this list of C++ cryptography libraries to get you started.

—–

I hope this tutorial has helped you determine what the best programming language to learn for you. If you have any questions, feel free to ask me at [email protected]. Please leave a comment below if you want to give feedback or if you think I’m missing something 🙂

Data Science/Artificial Intelligence, Learning Lists

21 of the best free resources to learn SQL

BY

I self-taught myself SQL after I bombed a technical interview that involved SQL. It got me a bit mad at myself, so I went ahead and started looking for different resources to help me practice and learn SQL. I wasn’t looking to spend any money so I focused on getting the best free resources. The list below is the fruit of my efforts. I hope that it helps you on your journey to learn SQL.

It can be handy to learn SQL. You might be looking to do data analysis with it, or you might look to learn SQL to help answer different questions you have on your data. A quarter of data users use MySQL databases — to get access, you’ll need to learn SQL.

1- Codeacademy

Codeacademy is one of my go-to resources for learning programming because it involves interactive exercises where you get immediate feedback. This is what makes the practice and learning of programming so fluid and rapid. Their section on SQL meets the same bar as the rest of their courses, and of course, it’s free.

Learn SQL

Source: author screenshot

2- Stanford Databases Mini-Course

This free self-paced mini-lesson on databases and SQL allows you to follow along with what top computer science students at Stanford are learning — all this at a self-directed pace, and for the total cost of zero dollars.

3- Head-First SQL (O’Reilly)

This book by O’Reilly entitled SQL Head First helps you dive deep into SQL topics, with tons of examples and writing to help you learn SQL. It can be completely free with a free trial of their Safari product (though the trial will run out). I classified it as a free resource as it doesn’t seem like O’Reilly’s paywalled it.

4- SQL Tutorial – W3Schools

W3Schools offers a text + exercises tutorial that breaks down individual SQL functions. I like looking through individual-level examples of different functions, especially when I’ve forgotten the nuances of one or another. You’ll see that several other providers do the same thing as W3Schools, but for me, W3 is the best resource out of the lot.

5- SQLZoo

SQLZoo is one of the coolest free SQL resources out there. The set of rich, interactive exercises placed in real-world settings is about as close as you’ll get to working with SQL in complex production-level environments without actually being hired. I used it a lot to practice SQL as I was picking up data analysis skills and I still use it every once in a while as a refresher.

learn sql

Source: Author screenshot

6- Mode Analytics

Mode Analytics offers a cool, intuitive interface along with a case study like approach involving Crunchbase data. It can help you rapidly place your SQL skills in a context that is like real-world uses, and once again, it’s free. They’re trying to upsell you to their paid product in offering this educational resource. That type of content always does well however, because it’s never priced and the focus of the team in question is to deliver maximum quality and substance for your learning objectives.

7- Sololearn 

This Sololearn course is interesting because it has different quizzes to make sure you retain material as you’re learning it. It follows the same conventional structure of teaching you how to select and query data, then teaching you how to sort and manipulate it within a data table, finally moving to joining and modifying tables. Notably, there isn’t much here in the way of aggregation functions or subqueries, which other courses above will teach.

8- SQL for Beginners (Youtube)

If you prefer learning with video content, this 1-hour video tutorial on SQL might just do the trick for you. It starts going over the basics of SQL, then shifts into the end by talking through different functions and examples on video. It is however meant to be an introduction — don’t expect to become an expert in SQL just by watching this video.

9- SQL Tutorial

SQL Tutorial offers an interface like W3Schools without the exercises. In case none of the resources above it seem to be working for you, this is another free resource where you can break down and learn SQL functions one-by-one.

10- SQLCourse

SQLCourse offers a curriculum with specific exercises for different functions. It is another handy way to practice your SQL skills for free. The interface is a bit janky, but it’s workable.

11- Welcome to SQL (KhanAcademy)

If you prefer a set of curated video tutorials that cover different topics, then look no further than the KhanAcademy course that introduces SQL. It runs through to the basics until aggregating data — there are projects in between as well. If you’re already on KhanAcademy, this could be a perfect transition to you learning SQL.

learn sql

Source: author screenshot

12-TutorialsPoint

Yet another text + examples format of teaching SQL, TutorialsPoint does offer more of the same: but if you’re looking for more and more practice or different ways to digest SQL material, it might be handy enough for you.

13- SQLBolt 

SQLBolt is constructed a bit like a virtual, interactive book on SQL. You can read through it and then go through each exercise, feeling like you’re being guided on your journey by the book-like structure. If you want a more focused and curated approach to learning SQL — this might be it for you.

 

14- Galaxql

Another interactive SQL course, meant to run in the browser. It’s free-to-use, but may use quite a few intensive resources as it’s running. (not sure if that’s just how old this tutorial is, or an honest warning). It is free to try if you want to take a look.

15- Use the Index 

Use the Index is a very useful advanced training site for SQL. Once you have the basics down, check out this site to see how to optimize your SQL databases and your queries so you can get maximum performance from it.

learn sql

Source: Author screenshot

16 – Schemaverse 

Schemaverse is a game built entirely on SQL. I’m a big fan of using gaming to motivate people to learn. I think it could show an exponential effect on motivation and on acquiring SQL skills if you’re somebody who wants to learn, but might get bored out of their mind with conventional tutorials or videos.

17- SQL Server Microsoft Documentation

The Microsoft documentation around SQL Server can help you understand how to use their software to manage SQL databases and tables — it’s offered for free, so you can take advantage and have a look even if you’re just curious.

18- SQLFiddle

SQLFiddle allows you to upload data and play around with different SQL queries to see what data comes back. It is a great way to practice your SQL skills, especially if you don’t have a real-world database to play with. You can take .sql files from elsewhere, upload them, and then try to manipulate the data contained within or query certain selections of data. It is also free to use — which never hurts.

19- MySQL Sandbox

MySQL sandbox allows you to install several MySQL servers in sandbox mode, allowing you to rapidly experiment, test and learn with different data files. It can be a useful way for you to practice what you can do with multiple SQL instances.

20- Stack Overflow (SQL) 

One of my favorite free resources to learn SQL or really any programming language is StackOverflow. This is an online Q&A community focused on technical topics where you can ask experts questions or see solutions to existing problems. It’s a fantastic community to immerse yourself in if you’re learning SQL. The specific SQL tag makes sure that you can stay focused on SQL topics.

learn sql

Source: author screenshot

21- r/LearnSQL (Reddit)

Another great community for learning is Reddit — where you can see different resources being placed and ranked by users as the days go by. Take part in discussions here or take advantage of articles or discussions posted to spur your own learning exponentially forward.

Hopefully, all these free resources are helpful to you on your journey to learn SQL — they were helpful to me as I started learning. While I leaned on some a bit more than others, I’m always a big fan of curating as many resources as possible because my learning style (very text-heavy) may not be the same as yours.

A lot of research has been done on the predictive abilities of matching how a concept is presented with a preferred learning style. While few models meet the test, it does seem like digesting new materials under a whole bunch of different modes or media formats helps learning.

So if you want to learn SQL, it’s best to go through and try a few of these resources, then choosing and sticking to which ones you like and combating the learning problem from many different angles.  

Data Science/Artificial Intelligence, Learning Guides

Learn machine learning with Python: a free curated curriculum

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How to learn data science and deep learning in Python

I recently wrote a 80-page guide to how to get a programming job without a degree, curated from my experience helping students do just that at Springboard. This excerpt is a part where I focus on how to learn machine learning in Python.

How to learn machine learning in Python is a very popular topic: with the rise of artificial intelligence, programmers have been able to do everything from beating human masters at Go to replicating human-like speech. At the foundation of this fantastic technological advance are programming and statistics principles you can learn.

Here’s how to learn machine learning in Python:

Sponsored link: 

Excel can be a powerful tool for data exploration and analysis when dealing with small data sets, but for anything more complex it often makes more sense to use Python. PyXLL lets you keep the best of both by integrating Python into Excel. You can use Excel as an interactive user interface and use Python to do the data fetching, cleaning and computation.

Python Basics

learn machine learning

Before you learn how to run, you have to learn how to walk. Most people who start learning machine learning and deep learning come from a programming background: if you do, you can skip this section. However, if you’re new to programming or you’re new to Python, you’ll want to take a look through this section.

Codecademy for Python

Codecademy is an online platform for learning programming, with free interactive courses that encourage you to fully type out your code to solve simple programming problems.

Introduction to Python for Data Science

This interactive Python tutorial is created by Datacamp, and is more suited to introducing how Python basics work in the context of data science.

11 Great Resources to Learn and Work in Python

This list of resources will point you to great ways to immerse yourself in Python learning. It’s a broad list filled with different resources that will help you, no matter your learning style.

Installing Jupyter Notebook

These are instructions for installing Jupyter Notebook, an intuitive interface for Python code. You’ll have all of the important Python libraries you need pre-installed and you’ll be easily able to export out and show all of your work in an easy-to-visualize fashion. I strongly suggest that you use Jupyter as your default tool for Python, and the rest of this learning path assumes that you are.

Statistics Basics

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In order to learn machine learning in Python, you not only have to learn the programming behind it — you’ll also have to learn statistics. Here are some resources that can help you gain that fundamental knowledge.

Khan Academy, Math, and Statistics

Khan Academy is the largest source of free online education with an array of free video and online courses. This section on Khan Academy will teach you the basic statistics concepts you need to know to understand machine learning, deep learning and more — from mode, median, mean to probability concepts.

Probabilistic Programming & Bayesian Methods for Hackers

This book will delve into Bayesian methods and how to program with probabilities. Combined with your budding knowledge of Python, you’ll be quickly able to reason with different statistical concepts. It’s a book the author gave out for free — and its deeply interactive nature promises to engage you into these new concepts.

Pandas

learn machine learning

The main workhorse of data science in Python is the Pandas data science library, an open-source tool that allows for a tabular organization of large datasets and which contains a whole array of functions and tools that can help you with both data organization, manipulation, and visualization. In this section, you’ll be given the resources needed to learn Pandas which will help you to learn machine learning in Python.

Cooking with Pandas

Julia Evans, a programmer based in Montreal, has created this simple step-by-step tutorial on how to analyze data in Pandas using noise complaint and bike data. It starts with how to read CSV data into Pandas and goes through how to group data, clean it, and how to parse data.

Official Pandas Cookbook

The official Pandas cookbook involves a number of simple functions that can help you with different datasets and hypothetical transformations you might want to do on your data. Take a look and play with it to extend your knowledge of Pandas.

Data Exploration and Wrangling

learn machine learning

Before you can do anything with the data, you’ll want to explore it, and do what is called exploratory data analysis (EDA) — summarize your dataset and get different insights from it so you know where to dig deeper. Fortunately, tools like Pandas are built to give you relevant and surprisingly deep summary insights into your data, allowing you to shape which questions you want to explore next.

By looking through your dataset from afar, you’ll already be able to understand what faults the dataset might have that will keep you from completing your analysis: missing values, wrongly formatted data etc. This is where you can start processing and transforming the data into a form that you want to answer your questions. This is called “data wrangling” — you are cleaning the data and making sure that it is able to answer all of your questions in this step.

Python Exploratory Data Analysis with Pandas

This article from Datacamp goes through all of the nuts and bolts functions you need in order to take a slightly deeper look at your data. It covers topics ranging from summarization of data to understanding how to select certain rows of data. It also goes into basic data wrangling steps such as filling in null values. There are interactive embedded code workspaces so you can play with the code in the article while you are digesting its concepts.

A Comprehensive Introduction to Data Wrangling

This blog article from Springboard is filled with code examples that describe how you can filter data, detect and drop invalid/null values from your dataset, how to group data such that you can perform aggregated analyses on different groups of data (ex: doing an analysis of survival rate on the Titanic by gender or passenger class) and how to handle time series data in Python. Finally, you’ll learn how to export out all of your work in Python so that you and others can play around with it in different file formats such as the Excel-friendly CSV.

Pandas Cheat Sheet

This Pandas cheat sheet, hosted on Github, can be an easy, visual way to remember the Pandas functions most essential to data exploration and wrangling. Keep it as a handy reference as you go out and practice some more.

Data Visualization

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Data exploration and data visualization work together hand-in-hand. Learning how to visualize data in different plots can be important is seeing underlying trends.

Beginner’s Guide to Matplotlib

This legend of resources on the official matplotlib library (the workhorse library for Python data visualization) will help you understand the theory behind data visualization and how to build basic plots from your data.

Seaborn Python Tutorial

The Seaborn library allows people to create intuitive plots that the standard matplotlib library doesn’t cover easily: things like violin plots and box plots. Seaborn comes with very compelling graphics right out of the box.

Introduction to Machine Learning

learn machine learning

Machine learning is a set of programming techniques that allow computers to do work that can simulate or augment human cognition without the need to have all parameters or logic explicitly defined.

The following section will delve into how to use machine learning models to create powerful models that can help you do everything from translating human speech to machine code, to beating human grandmasters at complex games such as Go.

It’s important before we get started implementing ideas in code that you understand the fundamentals of machine learning. This section will help you understand how to test your machine learning models, and what statistics you should use to measure your performance. It is an essential cornerstone to your drive to learn machine learning in Python. 

A Visual Introduction to Machine Learning

This handy visualization will allow you to understand what machine learning is and the basic mechanisms behind it through a visual display of how machines can classify whether a home is in New York or in San Francisco.

Train/Test Split and Cross-Validation in Python

This article explains why you need to split your dataset into training and test sets and why you need to perform cross-validation in order to avoid either underfitting or overfitting your data. Does that seem like a lot of jargon to you? The article will define all of these different concepts, and show you how to implement them in code.

Sci-kit Learn

learn machine learning

Sci-kit learn is the workhorse of machine learning and deep learning in Python, a library that contains standard functions that help you map machine learning algorithms to datasets.

It also has a bunch of functions that will allow you to easily transform your data and split it into training and test sets — a critical part of machine learning. Finally, the library has many tools that can evaluate the performance of your machine learning models and allow you to choose the best for your data.

You’ll want to make sure you know how to effectively use the library if you want to learn machine learning in Python.

A Gentle Introduction to Scikit-Learn

This post introduces a lot of the history and context of the Sci-Kit Learn library and it gives you a list of resources and documentation you can pursue to further your learning and practice with this library.

Scikit-Learn Documentation

The official scikit-learn documentation is filled with resources and quick start guides that will help you get started with Scikit-Learn and which will help you entrench your learning.

Regression

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Regression involves a breakdown of how much movement in a trend can be explained by certain variables. You can think about it as plotting a Y or dependent variable versus a slew of X or explanatory variables and determining how much of the movement in Y is dependent on individuals factors of X, and how much is due to statistical noise.

There are two main types of regression that we’re going to talk about here: linear regression and logistic regression.  Linear regression measures the amount of variability in a dependent factor based on an explanatory factor: you might, for example, find out that poverty levels explain 40% of the variability in the crime rate. Logistic regression mathematically transforms a level of variability into a binary outcome. In that way, you might classify if a name is most likely to be either male or female. Instead of percentages, logistic regression produces categories.

You’ll want to study both types of regression so you can get the results you need.

Simple and Multiple Linear Regression in Python

This informative Medium piece goes into the theory and statistics behind linear regression, and then describes how to implement it in Sci-Kit Learn.

Building a Logistic Regression in Python, Step-by-Step

This Medium tutorial uses the Sci-Kit Learn tools available to implement a logistic regression model. The amount of detail in each step will help you follow along.

Clustering

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Another type of machine learning model is called clustering. This is where datasets are grouped into different categories of data points based on the proximity between one point and other groups of points. Mastering clustering is an important part of learning machine learning in Python. 

An Introduction to Clustering and different methods of clustering

Analytics Vidhya has presented this comprehensive introduction to clustering methods: it’s good to get a handle on this theory before you try implementing it in code.

Customer Segmentation using Python

This article from Yhat demonstrates how to do simple K-means clustering across different wine customers. It’ll take your learning in Pandas and Scikit-Learn and combine them into a useful clustering example.

Deep Learning/Neural Networks

learn machine learning

Neural networks are an attempt to simulate how the human mind works (on a very simplified level) in computational code. They have been a great advance in artificial intelligence — and while in some ways they are a black box of complex algorithms working in tandem to learn how data generalizes, their practical applications have exponentially multiplied in the last few years. Deep learning encompasses neural networks as well as other approaches meant to simulate human intelligence. They are an important part to learn if you want to learn machine learning in Python. 

In a huge breakthrough, Google’s AI beats a top player at the game of Go

This short Wired article isn’t a technical tutorial: it’s the recounting of an epic match between a human grandmaster at Go, a game that was supposed to be so complex for computers to win that technology to do so wasn’t supposed to come until around the 2030s. By leveraging the power of neural networks, Google was able to bring AI victory forward some two decades or so. This article should give you a great glimpse at the potential and power of neural networks.

A Beginner’s Guide to Neural Networks in Python and SciKit Learn 0.18

This example-laden tutorial uses the neural networks module in the Scikit-Learn library to build a simple neural network that can classify different types of wine. Follow along and play with the code so you can get a feel for how to build neural networks.

Develop Your First Neural Network in Python With Keras Step-By-Step

This tutorial from Machine Learning Mastery uses the Python implementation of the Keras library to build slightly more powerful and intricate neural networks. Keras is a code library built to optimize for speed when it came to experimenting with different deep learning models.

Big Data

learn machine learning

Big data involves a lot of volume and velocity of data. It’s an amount of data, measured in petabytes, that can’t be processed easily with tools like Pandas, which are based on the processing power of one laptop or computer.

You’ll want to scale out to controlling many processors and servers and passing data through a network to process data at scale. Tools that allow you to map and reduce data between multiple servers and others such as Spark and Hadoop play an important role here. It’s time to take the learning you’ve had before this and apply it to massive data sets! You can’t learn machine learning in Python without dealing with big data. 

Get Started With Pyspark and Jupyter Notebook in 3 Minutes

This blog post will help you get set up with PySpark, a Python library that brings the full power of Spark to you in the Jupyter Notebook format you’ve been used to working in. PySpark can be used to process large datasets that can go all the way to petabytes of data!

PySpark Video Tutorial

This video tutorial will help you get more context about PySpark and will provide sample code for tasks such as doing word counts over a large collection of documents.

Using Jupyter on Apache Spark: Step-by-Step with a Terabyte of Reddit Data

This tutorial from Insight goes a little further than installation instructions and gets you working with Spark on a terabyte (that’s 1024 gigabytes!) of Reddit comment data.

Machine Learning Evaluation

learn machine learning

Now that you’ve learned a baseline for all of the theory and code you need to learn machine learning in practice, it’s time to learn what metrics and approaches you can use to evaluate your machine learning models.  

Metrics to Evaluate Machine Learning Algorithms in Python

In this tutorial, you’ll learn about the different metrics used to evaluate the performance of different machine learning approaches. You’ll be able to implement them in Scikit-Learn and Jupyter right away!

Model evaluation, model selection, and algorithm selection in machine learning

This long six-part series (check the end of this blog post for more posts after) goes deep into the theory and math behind machine learning evaluation metrics. You’ll come out of the whole thing with a deeper knowledge of how to measure machine learning models and compare them against one another.

Suggested daily routine

Learning isn’t often a static thing. You need ongoing practice to master a skill. Here’s a suggested learning routine you can implement in your day to make sure you practice and expand your knowledge and learn machine learning in Python.

Here’s my suggested daily routine:

  1. Continue working on something in machine learning at all times
  2. Go to StackOverflow, ask and answer questions
  3. Read the latest machine learning papers, try to understand them
  4. Practice your code whenever you can by looking through Github machine learning repositories
  5. Do Kaggle competitions so you can extend your learning and practice new machine learning concepts

At the end, you’ll have effectively mastered how to learn machine learning in Python!

Want more material like this? Check out my guide on how to get a programming job without a degree.

Data Science/Artificial Intelligence, Learning Lists

The most popular deep learning libraries

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You might have heard of artificial intelligence, deep learning and neural networks, and wanted to get a path into this exciting new technology. This article will help you get a comprehensive overview of the tools and frameworks you can use to accelerate your impact and learning in artificial intelligence. It will help you understand what deep learning library you should use to accelerate your learning. 

This article assumes your familiarity with basic deep learning concepts — if you need to catch up on those, the following Wikipedia article will help you get to scratch.

This is a walkthrough of the most popular deep learning libraries, examples of clever projects and implementations that have used the different libraries to create something awesome.

I’ve ranked the deep learning libraries in question by the number of Github stars their repositories have collected as of June 2017 — a great way to see how much traction these different frameworks have with programmers.

Deep learning library

1- TensorFlow

Overview: TensorFlow is the open-source machine learning library developed by Google (and still used in both research and production level applications at the company). The library allows you to bring machine intelligence capabilities to all sorts of devices, from those equipped with GPUs to mobile devices such as the Raspberry Pi. With over open-source 6,000 repositories using TensorFlow, it has quickly become one of the most popular frameworks out there for those looking to build something with deep learning. TensorFlow is very accessible, with APIs for Python, C++, Haskell, Java, Go and Rust and a 3rd party package built in R.

Introductory Tutorial: Get introduced to TensorFlow with this official tutorial by Google by using TensorFlow with the famous MNIST dataset.

Use Cases: TensorFlow has become of the most popularized deep learning frameworks, and as such, it has seen a wide array of uses from powering cutting-edge machine learning work at different Silicon Valley companies to classifying cucumbers for farmers.

Resources:

  1. This Github repository TensorFlow-World contains a bunch of introductory tutorials with code compiled together to give you a better sense of how to do deep learning on TensorFlow.
  2. TensorFlow-101 contains tutorials on how to get started with TensorFlow in Jupyter Notebook with Python.
  3. This Github repository contains example code that will help you work through different analyses in TensorFlow.  

How to get started: Get all of the documentation and installation instructions here, then start practicing and training deep learning models on different datasets! TensorFlow is one of the most popular and powerful deep learning frameworks out there: take advantage! 

2- Scikit-learn

Overview: Scikit-learn is the versatile machine learning knife in Python, used for simple experimentation and iteration with different templated machine learning models. With modules like the MLPClassifier, you can easily bring deep learning approaches to your datasets and use the rest of the trusty scikit-learn ensemble (such as train_test_split) to validate and evaluate your model. You can also combine the scikit-learn interface with different deep learning libraries if you want to do more powerful analyses.

Introductory Tutorial: This tutorial runs through how to use scikit-learn as a deep learning library and a multilevel perceptron model to classify different types of wine. You can see how with a few lines of code, you can create a very accurate model with many variables.  

Use Cases: scikit-learn is often the first go-to deep learning tool for people working in the Python data science ecosystem: it comes pre-installed with Jupyter Notebook and it comes with powerful functions that are already-optimized versions of essential deep learning functions. You’ll be able to quickly build together machine learning models, evaluate them, and split different use cases into either test or training sets.

Resources:

  1. This cheatsheet by Datacamp will help you with many of the essential functions in scikit-learn.
  2. Springboard has a tutorial that will teach you how to build a simple neural network with scikit-learn and its MLPClassifier module.
  3. This gentle introduction into scikit-learn can help you ease into this machine learning package.

How to get started: Use jupyter notebook, which comes with scikit-learn installed by default. Start training machine learning models, then move into Scikit Flow, an interface that combines the code you’d use in scikit-learn augmented with functionality from Google’s TensorFlow library (we’ll dive into TensorFlow a little bit later in this article).

3- Caffe

Overview: Caffe is a deep learning framework built by Berkeley’s AI Research department (BAIR) and sustained through the use of community contributors. It features speedy application of deep learning approaches, with the ability to classify up to 60 million images a day on a single GPU. It powers a variety of deep learning projects in machine vision, speech and more — with projects ranging from fully fleshed out applications to academic papers.

Introductory Tutorial: This tutorial built by Berkeley AI researchers will help you get up to speed with this powerful deep learning framework.

Use Cases: Caffe is used for a variety of academic research — this web page has a ton of examples ranging from image classification to training the classic LeNet model.

Resources:

  1. This blog post will help you get up to scratch with using Caffe and Python with a relevant classification example from Kaggle on how to distinguish between dogs and cats.
  2. This tutorial will help you with loading Caffe onto iPython Notebook and also with C++ implementations of the library.
  3. This tutorial will get you to understand how you can build a layer of a neural network within Caffe.

How to get started: Use the command line and get started with different use cases of Caffe. You can use this tutorial to get installation instructions across a whole array of different platforms.

4- Keras

Overview: Keras is an open source deep learning library for neural networks written in Python. Authored by François Chollet, the library was meant to be a quick and easy way to experiment with different deep learning models — as a high-level API written entirely in Python, the library is easy to debug and navigate. It supports both convolutional and recurrent neural networks and it is designed to be as intuitive as possible for users to grasp.

Introductory Tutorial: This introductory tutorial will run over how to get started with Keras — all the way from installing the package to training a model with 99% predictive accuracy on the seminal MNIST dataset.  

Use Cases: Most of the time, Keras is used to build simple deep learning models as conceptual sketches: you would validate crude ideas using Keras, and get a first glance at whether or not you had a good idea for a deep learning architecture that can tackle a problem.

Resources:

  1. This course by DataCamp helps you dive deeper into Keras, even if you’re just a deep learning beginner!
  2. Here is a link to the official Keras documentation which allows you to get access to the inner working of the framework from the creators of it.
  3. If you’re more comfortable with the R programming language, you can use the R interface to Keras.

5- Torch

Overview: Torch is an open-source deep learning framework based on optimizing performance on GPUs based on the programming language Lua with an underlying C/CUDA implementation. It allows for the parallelization of neural networks across different CPUs and GPUs. Torch is used by a lot of organizations at the cutting edge of machine learning, from Google to Facebook. It has been extended for use in mobile settings, with the ability to perform on iOS and Android.

Introductory Tutorial: This 60 minute blitz into Torch will get you started and ready to use this powerful deep learning tool.

Use Cases: The deep learning library Torch is used for a lot of machine learning and deep learning research programs at leading companies such as Google, Twitter, and Facebook. Given its origins as Facebook Research’s default deep learning framework, you can be sure that it comes with all of the support it needs from one of the largest tech companies in the world.

Resources:

You’ll want to get started understanding Lua before you work with Torch. This handy guide will help you get up to scratch.

This Github repository contains a whole host of Torch tutorials.

The following blog article by Facebook Research contains a lot of related work done in Torch.

How to get started: Get the deep learning library Torch installed, then start running it on different deep learning problems. You might want to refer to the Facebook research blog for inspiration.

6- Theano

Overview: Theano is a deep learning framework built deeply into the Python data science ecosystem, with deep integration with the NumPy numerical computing library. You can use C to generate code as well to make it even speedier — and it is the default teaching tool used in deep learning founding father Yoshua Bengio’s lab.  

Introductory Tutorial: This Theano tutorial on Jupyter Notebook will help you understand the nuances of the deep learning library Theano, and will get you up to scratch with different conventions within the library.

Use Cases: This forum will allow you to understand different problems and use cases Theano users create with the deep learning library.

Resources:

  1. This documentation will help you understand more about the deep learning library Theano. 
  2. This blog post will help you understand the performance differences between Theano and Torch.
  3. Here is an introductory tutorial to Theano.

How to get started: Use the instructions here to get started with installing Theano.

7- Neon

Overview: Neon is an open-source deep learning platform built on Python that is committed to the most powerful implementation of deep learning possible, with a consideration towards simplicity.

Introductory Tutorial: Work with this Github repository which contains a Model Zoo that contains different scenarios of working with Neon.

Use Cases: This Youtube playlist walks through different use cases with Neon, including playing Pong, and speech recognition.

Resources:

  1. Learn how to do basic classification with the MNIST database and the deep learning library neon with this introductory-level tutorial.
  2. Use this video course to get you started in Neon.
  3. This compilation of Neon resources will help you learn the framework.

How to get started: Get the deep learning library Neon installed with the following instructions.

Data Science/Artificial Intelligence, Learning Lists

101+ Resources to Learn Data Science

BY

Many people are seeking to learn data science these days. It’s become a trendy topic associated with high salaries and some of the most interesting problems in the world. This demand has created many different resources in the data science space. People have curated their selection of favorite resources to learn data science, but I was seeking out something more comprehensive — so I built this list. Here’s my attempt at getting you my favorite resources in the data science space so you can understand what’s going on in the field — and how you can get your hands dirty and start learning right away.

Full disclosure: I work for Springboard (one of the data science education providers listed below). 

What is data science?

learn data science

First, let’s start with an overview of what seems to have become a popularized buzzword and defining exactly what you want to learn: data science. Data science is the combination of three kinds of skillsets: statistics, programming and business knowledge. It’s the interplay between these crafts where you’ll find a data scientist — somebody who will programmatically examine large data sets for precious business insights — somebody who can combine computer science knowledge with business insight.

You can use data science concepts and training to do data mining and get statistical inferences from large datasets. Using advanced techniques such as natural language processing and unsupervised learning, you can tame the power of computation and get precious data insights others simply cannot access. That will be attractive to all sorts of potential employers in the data science field, from Silicon Valley to Wall Street.

In order to get there though, you have to start with the basic techniques and basic concepts that underlie data science. Learning data science requires having an understanding of the process that goes behind it, and the various components that are required to bring everything together. Let’s get started on getting you know that knowledge. 

Overview

learn data science

You’ll want to get an overview of the field and the processes and concepts that make up data science so you can learn data science.

1- Data Scientist: The Sexiest Job of the 21st Century

In this seminal article, ex-Chief Data Scientist of the United States, DJ Patil, goes into exactly what makes a career in data science so compelling. It’s great fuel to the fire if you’re looking to learn data science. 

2– What is data science?

This overview of data science by Berkeley delves into how data science came to be — and the average salary you can expect in the field.

3– Data Science Salary Survey (2016) – O’Reilly

O’Reilly, a leading publication and media company on the cutting edge of technology, dives deeper into what tools and factors go into higher data science salaries. They’ve surveyed hundreds of data scientists in the field. Learn what pays and what doesn’t with data science careers through their research!

4– Data Science (Wikipedia)

Wikipedia’s overview of data science goes over the history of the field and points to many different resources in the field. It can be a handy jumping-off point for further research.

5– Building Data Science Teams

This piece by DJ Patil goes into the different roles inherent in a data scientist’s job — and exactly how best to build out a data science team.

6- Data Science Process

This piece by Springboard goes into what the day-to-day of data science looks like — tracing it all the way to a first principles view of exactly what steps effective data science requires.  

Interactive Tutorials

learn data science

Now that you’re done with an overview of the topic, it’s time to get your hands a bit dirty with interactive tutorials that will help you learn different parts of data science — whether that’s the statistical theories behind machine learning algorithms, or the programming skills you’ll need to implement those theories.

Statistics/Math

Understanding probability and the basics of statistics is essential to being able to understand machine learning methods and how to handle massive amounts of data. Linear algebra and the ability to manipulate different expressions of data (in matrix form or otherwise) will also be incredibly helpful in detailing what data scientists do. You’ll want to refresh your statistics knowledge and get a handle on the math you need to know to join their ranks.

7– KhanAcademy (Statistics/Probability)

This free course from KhanAcademy serves as a great catch-up on the basics of probability and statistics.

8– Introduction to Statistics in R (Datacamp)

Learn a bit of R (a programming language commonly used in data science) and statistics at the same time with this interactive walkthrough from Datacamp.

9– Statistics 101 

This Youtube playlist from the Harvard Extension School covers everything from random variables to different statistical distributions. 

SQL

Knowing SQL and how to query from relational databases is a skill that is one of the building blocks of data science. You’ll often use SQL to source your data for further analysis — or even to transform your data on the spot.

10– Mode Analytics SQL School

Mode Analytics teaches SQL through the use of case studies with real data. It’s an interactive experience that’ll teach you the basics of SQL by having you run through a dataset with some simple yet powerful commands.

11– Learn SQL (Codecademy)

Codecademy, well known for its basic curated tutorials in different programming languages, has this simple interactive module that will help you learn SQL.

12– SQLCourse

This is an older tutorial, but one that still holds up as an example of an organized approach to learning SQL.

Python

Python is one of the workhorse languages of data science — one of the most popular along with R. The large open-source community that powers Python enables it to be a powerful, versatile programming language that can help facilitate everything from data wrangling to training powerful machine learning models. It’s a powerful tool you’ll want to learn as you learn data science. 

13– Pandas Cookbook

This interactive set of code examples walks you through how to get started with Pandas, the data processing library most commonly used in Python. It’s built by Julia Evans. 

14–Intro to Python for Data Science (DataCamp)

This interactive course will walk you through the basics of the data science libraries for Python.

15– Gentle introduction to scikit-learn

This gentle introductory tutorial will help you understand one of the most powerful machine learning and data science libraries out there: Python’s scikit-learn. You’ll be able to train simple, off-the-shelf data models in a matter of minutes.

16– A dramatic tour through Python’s data visualization landscape

This somewhat witty and whimsical walkthrough will help you explore the difference between the major data visualization tools in the Python ecosystem — including some options that were ported from R!

17- Web scraping with BeautifulSoup

This short guide will teach you how to take information from different websites and render it into a format that is easy for machines to process — a handy skill for anybody looking to work with many different datasets. I often use the set of techniques described to scrape tables from Wikipedia so I can process that data in Python.

R

R is another popular programming language used for data science — in fact, it’s often pitted against Python as a comparable tool. The truth is that you can use both — and in fact, being conversant in both can only help you progress faster and further as a data scientist.

18– Introduction to R (Datacamp)

Here is the equivalent of the Datacamp introduction to Python — except this time for R, another common data science programming language.

19– A complete tutorial to learn R from scratch

This tutorial, rendered as a blog post, offers a comprehensive A to Z guide to getting started in R. It covers everything from importing data into R to creating predictive models with it.

20 – Try R

Sponsored by O’Reilly Media, this interactive course will reward you with a badge for each fundamental building block of R you learn.

Hadoop

Hadoop is a big data framework meant to facilitate the treatment and storage of large data sets that have be processed in parallel by many different servers in order to yield actionable insights. 

21– Hadoop Tutorial (Tutorialspoint)

This set of tutorials on Hadoop will help you understand how big data frameworks work — and how you can apply Hadoop to your data.

22– Hortonworks Sandbox tutorial on Hadoop

This interactive Hadoop sandbox by Hortonworks lets you play with Hadoop code.

Spark

Spark helps solve some speed, flexibility and efficiency issues with Hadoop through the use of a new data structure: the RDD or resilient distributed dataset.

23– Apache Spark Tutorial (TutorialsPoint)

TutorialsPoint offers a similar tutorial to Spark as it does for Hadoop.

24– Hands-on introduction to Spark

Hortonworks has a sandbox that will let you play around with Spark code.

Courses/Workshops

learn data science

The following courses and online workshops will help you learn data science in an organized fashion. Use these resources to accelerate your learning of data science if you need to. A lot of these courses will help you find data science work, and you’ll likely be able to do data science projects after finishing them. 

25– Fast.ai

This massive online course, built by a Kaggle champion in machine learning, will help you learn about neural networks and how to train machine learning models.

26– Foundations of Data Science (Springboard)

This course offered by Springboard features a curated selection of resources in R, SQL and the basics of machine learning, as well as personalized mentoring from data science experts who work in the field.

27– Data Science Intensive (Springboard)

Yet another course offered by Springboard, though this one is more advanced. Focused on Python and teaching the intricacies of machine learning methods, this course will help you use different machine learning techniques with ease.

28–Data Science Career Track (Springboard)

Springboard’s Data Science Career Track is the first online bootcamp to offer a data science job or your tuition back. With personalized career coaching, mentorship from data scientists and exclusive employer partnerships, Springboard is putting it all on the line to help you get a job in data science.

29– Data Science (Coursera)

Coursera partnered with Johns Hopkins University to deliver this nine-course series on data science, covering everything from tools to advanced machine learning methods.

30– Machine Learning (Coursera)

This curated set of machine learning courses taught by Andrew Ng (the famous Stanford professor who founded Coursera in the first place) is one of the best resources to consult as you start understanding data science.

31– Thinkful Data Science Bootcamp

Thinkful, an online education provider, provides a data science bootcamp that will curate your learning of data science and Python.

32– Intro to Machine Learning (Udacity)

Udacity offers a free mini-course curated by Facebook and Tableau to help guide you through to doing analysis of the Enron email database.

33– Data Science Certificate (Harvard Extension School)

This data science certificate offered by the Harvard Extension School can help you learn data science while getting credits and credibility from one of the leading universities in the world.

34– Statistics with R (Coursera)

This selection of courses created in partnership with Duke University will help you understand basic probability and the use of Bayes’ Rule through the use of R.

35– Data Science (EdX)

This set of curated learning paths in data science can help you get accreditation in the field — if you’re willing to pay for it.

36– Insight Data Science Fellowship

The Insight Data Science Fellowship is a special type of data science education program — it takes talented PhD. students who have already demonstrated technical skills and aptitude, and helps them bridge the gap between academia and industry with a postgraduate fellowship that combines the best of academic rigor with industry knowledge.

37–Data Science (General Assembly)

General Assembly, one of the largest online education providers in the world, offers courses in data science.

38– Galvanize

If you’re looking for an in-person experience to learn data science instead of something online, Galvanize can help. This link leads to the San Francisco experience — however, Galvanize itself is present in many different other cities.

39– Coursereport Data Science Reviews

Here are some reviews of different data science courses in Coursereport — this will allow you to pick and choose between many different options with fair reviews from previous students on display.

40– Switchup Data Science Reviews

Here are some more reviews of different data science courses, this time from Switchup, another course review site.

Books

learn data science

Oftentimes it’s not a great course that helps you learn the most — it can be one single resource within that course — say a particularly well-written book. This selection of data science books can help you understand data science in detail.

41– Bayesian Methods for Hackers

This book, delivered as an extended Github repository, can help you understand Bayesian inference and how to think about probabilities by working through them in code. 

42– Think Stats

This O’Reilly book helps you conceptualize statistical concepts by having you work with them in Python.

43– Think Bayes

This book combines Python programming with Bayesian inference, and can be a handy resource in case the books above aren’t enough.

44– Deep Learning

This free technical book by some of the scions of deep learning and artificial intelligence (Ian Goodfellow, Yoshua Bengio and Aaron Courville) will help you understand exactly how to think about deep learning and neural networks.

45– Learn Python the Hard Way

In case you need a refresher on Python, Learn Python the Hard Way will help you break down exactly what you need to do to master Python. While it focuses on an older version of Python, the first principles taught here can be useful to those looking to freshen up their knowledge of Python — though you shouldn’t become overly dependent on this book as it has quite a rigid philosophy on one particular version of Python. 

46– The Data Science Handbook

This Data Science Handbook curates insights from 25 data science leaders and distills what it truly means to work in this exciting new field.

47– Data Science from Scratch

This book from O’Reilly goes into the first principles of data science, looking beyond the programming tools and frameworks.

48– Storytelling with Data

This book will help you visualize insights that you find within your data and teach you how to communicate them effectively so that you can drive impact with your data findings.

49–Exploratory Data Analysis with R

Roger D. Peng, an expert in statistics, has written this book to teach how to look through datasets with the R programming language.

50–Interactive Data Visualization for the Web

This online book will teach you how to use frameworks such as D3.js to make your visualizations fully interactive on the web.

51–Machine Learning Yearning

This book by Andrew Ng, the famous artificial intelligence leader who founded Coursera, is going to be released soon — sign up to get drafts of new chapters as they come in!

Curated Collections

learn data science

I know you’re looking for curated resources to learn data science. There’s more than just this list right here — and each collection will help you expand your knowledge and collection of great data science resources even further.

52– Awesome Machine Learning

This Github repository follows the “Awesome” method of curating the best resources in a particular space — in this case, all the different resources you’d need to learn machine learning.  

53– Awesome Deep Learning Papers

In case you ever wanted to get a handle on the science behind the amazing technology being built out of artificial intelligence, this awesome curation of deep learning papers will help you continually be on top of exciting new developments.

54– Awesome TensorFlow

TensorFlow is an awesome deep learning framework: this Github repository will have everything you need to learn more.

55– Awesome Data Science

This repository is everything it promises: an awesome curation of different data science resources.

56– Data Analysis Learning Path (Springboard)

This learning path curates different resources in an intuitive fashion so that you can learn the data analysis skills required for data science.

57–The Open Source Data Science Masters

This is a curated curriculum of free, open-source resources to learn data science — consider it a masters’ degree for a fraction of the price.

General Resources

learn data science

58– A visual intro to machine learning

This interactive, visual view of data science in action can help you conceptualize data science, especially if you prefer to learn visually. 

59– Deep Learning Review (Nature)

This paper summarizes some of the latest findings in deep learning and artificial intelligence and it is written by one of the founding fathers of modern artificial intelligence research: Geoffrey Hinton.

60– Build a deep learning machine

This fun little tutorial by O’Reilly will teach you how to build a computer that you can use specifically for data science purposes.

61- How can I become a data scientist (Quora)

This Quora thread contains different thoughtful replies on how to become a data scientist — and includes a bevy of free resources to boot!

62– Becoming a Data Scientist

This blog charts the author, Renee, and her path from being a SQL analyst to becoming a full-fledged data scientist.

Career Advice

learn data science

Becoming a data scientist is now a career path that many envy — however, getting started and placing yourself in a position where you are paid to practice data science doesn’t start and end with technical skills. Here’s a set of resources that will help spell out exactly what you need to do to have a successful data science career.

63– 2015 Data Science Salary Survey (O’Reilly)

This salary survey by O’Reilly was curated from about 600 respondents who divulged their salary and what they did at work. It’s an informative read on what the average salaries are like in data science and what factors or technical skills can either increase your data science salary — or set it on the path to stagnating.

We already highlighted the 2016 survey as part of our general overview of data science, but the 2015 survey will add even more context on how the data science industry works — and how much you should expect to be paid.

64– Guide to Data Science Jobs (Springboard)

This guide to Data Science Jobs by Springboard curates a variety of job seeker and hiring manager stories and seeks to inform you on every element of what it takes to get a data science job: from how to get hiring managers to notice your profile, to advice on what technologies and skills you should practice before doing a data science interview. 

65– Guide to Data Science Interviews (Springboard)

This companion guide to the Guide to Data Science Jobs by Springboard runs you through different interview questions and exactly what hiring managers are thinking when they are on the other side of the table. It’s a comprehensive overview of the data science interview process — and it provides you actionable tips on how to ace the data science interview.

66– Getting your first job in data science

This blog post goes over different general tips on how to get that first job in data science.

67– Data Science Career Paths

This blog post by Springboard breaks down the difference between data analysts, data scientists and data engineers.

Datasets

learn data science

In order to really get started and to learn data science, you have to have datasets to play with. The following resources will link you to different datasets you can experiment with as you’re learning data science techniques and putting them into practice.

68– 19 Free Public Datasets (Springboard)

This curated list of 19 free public datasets will help you get started on your path to learn data science!

69– Kaggle Datasets

This list of datasets curated by Kaggle comes with upvote functionality as well as comments, so you can exactly which datasets are the most exciting — and what work has already been done with them.

70– Reddit Datasets

This subreddit can be a handy way to pick out new datasets, and see some of the most popular ones.

71– Data.world

This new social network has evolved around sharing great datasets and bringing data fans together!

72– Google BigQuery Datasets

Google BigQuery has open-sourced some interesting big data sets–from Reddit comments to Github activity.

73– Quandl

Quandl is a search engine mostly used for financial and economic data. Comb through if you’re looking in that space for data to play with. 

74– Public Big Datasets

This curated list of big datasets can help you practice with Hadoop or Spark.

75– Wikipedia dumps

Wikipedia dumps data from its database and makes it free to analyze every so often. Sift through here if you want to query the world’s largest collection of knowledge on your quest to learn data science.

76– Open Street Map

This collection of open-source geographic data extends around the world in its reach!

Resources/Blogs to Follow

learn data science

You’ll want to keep an eye on different resources and blogs that update frequently as you learn data science. This ensures that you’re always on top of the latest developments — and it can be a stimulating way to keep your data science skills sharp.

77– Top data scientists to follow on Twitter

This is a list of data science influencers you’ll want to consider following to get to know more about the industry.

78– 50 of the best data science blogs

This curated list of data science blogs will help you find the best blogs to follow as you learn data science.

79– Ultimate guide to data science blogs

This larger, extended guide to data science blogs has a lot more entries — feel free to take a look if you feel like you want something comprehensive to digest.

80– KDNuggets

KDNuggets is one of the largest data science communities on the web, and their blog regularly posts interesting data science content.

81– R-bloggers

R Bloggers is a data science blog focused on tutorials to learn R and different resources in the R ecosystem. 

82– Dataconomy

Dataconomy focuses on larger trends in data science rather than many technical tutorials. It’s the data science blog with the largest focus on the European data science scene as well.

83– Analytics Vidhya

Analytics Vidhya contains plenty of technical tutorials on many data science topics.

84– Big Data Made Simple

Big Data Made Simple is a relatable blog that conveys different topics in data science in an approachable manner.

85– Yhat blog

The Yhat blog is always filled with interesting tutorials and data science case studies.

86– Machine Learning Mastery

Machine Learning Mastery focuses on the intricacies of machine learning.

87– Learndatasci

Learndatasci is a blog that offers a broad overview of different data science topics.

88– Mastersindatascience

Mastersindatascience is the resource to consult if you wanted to look at paid offerings to learn data science.

Newsletters

learn data science

If you want regular updates in your inbox on the latest news in data science, there’s no better way to do that than to subscribe to the following data science newsletters.

89 – Data Science Weekly

This weekly newsletter summarizes the latest tutorials and resources in data science. It’s a very useful resource if you’re looking to learn data science. 

90– Data Elixir

Another data science newsletter that will keep you informed on the latest happenings in data science. 

91– Python Weekly

This weekly Python newsletter curates a selection of the finest Python resources, many of them related to data science.

92– Datafloq

This handy newsletter promises to be a one-stop shop for you when it comes to big data trends.

93- The Analytics Dispatch

Mode Analytics provides a dispatch to keep you informed on all things analytics and BI-related.

94- Postgres Weekly

This Postgres Weekly newsletter keeps you informed on the latest Postgres updates.

95- O’Reilly Data Newsletter

A premium data science newsletter, O’Reilly will often curate the best data science resources that have popped up.

Communities

learn data science

While newsletters and blogs are great, interactive communities where participants share articles and comment on them together can truly help you entrench your data science knowledge. Here are just a few of those communities where you can learn data science and interact with different data science practitioners.

96– Datatau

Datatau is a sort of Hacker News for data science resources where data science practitioners discuss the latest news and upvote the best articles.

97– Reddit Datascience

This subreddit deals with general data science topics.

98– Reddit Machine Learning

This subreddit deals with more in-depth machine learning materials and discussions.

99– Reddit Deep Learners

This subreddit deals with how to learn artificial intelligence and deep learning.

100– Reddit Data is Beautiful

This subreddit contains impactful data visualizations that are visually appealing — and a true set of examples if you want to display your data in a beautiful manner.

101– Data Science Stack Exchange

This subcomponent of the Stack Exchange network deals with technical questions and solutions in data science.

102– Quora Data Science

This section of Quora is composed of many of the questions posed about data science — it is an awesome resource for those looking to learn data science. 

Hopefully the resources above have been helpful for you to learn data science: let me know in the comments below what you think about them or whether you think there are some I missed!

Data Science/Artificial Intelligence, Learning Guides

How to do common Excel and SQL tasks in Python

BY

How to do common Excel and SQL tasks in Python

The code and data for this tutorial can be found in this Github repository. For more information on how to use Github, check out this guide. 

Data practitioners have many tools that they use to slice and dice data. Some people use Excel, some people use SQL — and some people use Python. The advantages of using Python are obvious when it comes to certain tasks. You can process much bigger datasets at much faster speeds. You can use open source machine learning libraries built on top of Python. You can easily import and export data in different formats. 

Python can become an essential part of any data analyst’s toolbox due to its versatility. However, it can be hard to get started. Most data analysts are probably familiar with either SQL or Excel. This tutorial is structured to help you transfer over skills and techniques from those two programs to Python.

First, let’s get you set up on Python. The easiest way to get started is to use Jupyter Notebook and Anaconda. This visual interface will allow you to plug Python code in and immediately see the output of your results. It’ll make it easy for you to follow along with the rest of this tutorial as well.

I highly recommend using Anaconda, but this beginners guide will also help you with installing Python directly — though that’ll make following this tutorial harder. 

Let’s start with the basics: opening up a dataset.

IMPORTING DATA

You can import .sql databases and process them in SQL queries. On Excel, you could double-click a file and then start working with it in spreadsheet mode. In Python, there’s slightly more complexity that comes at the benefit of being able to work with many different types of file formats and data sources.

Using Pandas, a data processing library, you can import a variety of file formats using the read function. A full list of the file formats you can import using this function is in the Pandas documentation. You can import everything from CSV and Excel files to the whole content of HTML files!

One of the biggest advantages of using Python is the ability to be able to source data from the vast confines of the web instead of only being able to access files you’ve downloaded manually. The Python requests library can help you sort through different websites and take data from them while the BeautifulSoup library can help you process and filter the data so you get exactly what you need. Be careful of usage rights issues if you’re going to go down this route.

(Don’t worry if you want to skip this part, you can! The raw csv file is here, and you can download it at will if you’d rather start this exercise without taking data from the web. Or you can git clone the entire repository.)

In this example, we’re going to take a Wikipedia table of countries by their nominal GDP per capita (a technical term that means an amount of income a country earns divided over the number of its population), and use the Pandas library in Python to sort through the data.

First, let’s import the different libraries we need. For more information on how imports work in Python, click here.

import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
import re

We’ll need the Pandas library to process our data. We’ll need the numpy library to perform manipulations and transformations of numeric data. We’ll need the requests library to get HTML data from a website. We’ll need BeautifulSoup to process that data. Finally, we’ll need the regular expression library of Python (re) to change certain strings that will come up as we process the data. 

It’s not necessary to know much about regular expressions in Python, but they are a powerful tool you can use to match and replace certain strings or substrings. Here’s a tutorial if you wanted to learn more. 

r = requests.get('https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita')

gdptable = r.text
soup = BeautifulSoup(gdptable, 'lxml')
table = soup.find('table', attrs = {"class" :"wikitable sortable"})

theads=[]
for tx in table.findAll('th'):
    theads.append(tx.text)

data =[]
for rows in table.findAll('tr'):
        row={}
        i=0
        for cell in rows.findAll('td'):
            row[theads[i]]=re.sub('\xa0', '',cell.text)
            i+=1
        if len(row)!=0:
            data.append(row)
print(data)

Credit to this website for some of the code.

Here’s a more technical explanation of how to grab HTML tables with Python code with more step-by-step instructions.

You can copy + paste the code above into your own Anaconda setup, and iterate with it if you want to play with some Python code!

The output from the code below, if you don’t modify it, is what is known as a list of dictionaries.

You’ll notice commas separating bracketed lists of key-value pairs. Each bracketed list represents a row in our dataframe, and each column is represented by the keys within: we are working with a country’s rank, its GDP per capita (expressed as US$), and its name (in ‘Country’).

For some more information on how data structures such as lists and dictionaries work in Python, this tutorial will help as well as this course: Intermediate Data Science Course by Springboard.

Thankfully, we don’t need to understand much of that in order to move this data into a Pandas dataframe, a similar way of aggregating data to a SQL table or an Excel spreadsheet. With one line of code, we’ve assigned and saved this data into a Pandas dataframe — as it turns out to be the case, lists of dictionaries are the perfect data format to be converted to a dataframe. 

gdp = pd.DataFrame(data)

With this simple Python assignment to the variable gdp, we now have a dataframe we can open up and explore anytime we write out the word gdp. We can add Python functions to that word to create curated views of the data within. For a bit more of an in-depth look at what we just did with the equal sign and assignment in Python, this tutorial is helpful.

TAKING A QUICK LOOK AT THE DATA

Now, if we want to take a quick look at what we’ve done, we can use the head() function, which works very similarly to selecting a few rows in Excel or the LIMIT function in SQL. Use it handily to take a quick look at datasets without loading the whole thing! You can also insert a number within the head function if you want to look at a particular number of rows. 

gdp.head()

The output we get are the first five rows of the GDP per capita dataset (the default value of the head function), which we can see are neatly arranged into three columns as well as an index column. Be aware that Python starts indexes at 0 and not 1, such that if you wanted to call up the first value in a dataframe, you’d use 0 instead of 1! You can change the number of rows displayed by adding a number of your choice within the parentheses. Try it out!

RENAMING COLUMNS

One thing you’ll quickly realize in Python is that names with certain special characters (such as $) can become very annoying to handle. We’ll want to rename certain columns, something you can do easily in Excel by clicking on the column name and typing over the old name and something you can do in SQL either with the ALTER TABLE statement or sp_rename in SQL server.

In Pandas, the way to do it is with the rename function. 

gdp = gdp.rename(columns = {'US$':'gdp_per_capita'})

In implementing the above function, we’ll be replacing the column header ‘US$’ with the column header ‘gdp_per_capita’. A quick .head() function call confirms that this change has been made.

DELETING COLUMNS

There’s been some data corruption! If you look at the Rank column, you’ll notice that there are random dashes scattered throughout it. That’s not good, and since the actual number order is disrupted, this makes the Rank column quite useless, especially with the numbered index column that Pandas gives you by default.

Fortunately, deleting a column is easy with a built-in Python function: del. By selecting columns through the use of square brackets appended to the dataframe name. 

del gdp['Rank']

Now, with another call to the head function, we can confirm that the dataframe no longer contains a rank column.

CONVERTING DATA TYPES WITHIN COLUMNS

Sometimes, a given data type is hard to work with.This handy tutorial will break down the differences between the different data types in Python in case you need a refresher.

In Excel, you could right-click and find ways of converting columns of data to a different type of data quite easily. You could copy a set of cells rendered by formulas and paste special as values, and you can use formatting options to quickly switch between numbers, dates, and strings. 

It’s not as easy in Python to switch between one data type to the other sometimes, but it’s certainly possible.

Let’s first use the re library in Python. We will regular expressions to replace the commas within the gdp_per_capita column so we can more easily work with that column.

gdp['gdp_per_capita'] = gdp['gdp_per_capita'].apply(lambda x: re.sub(',','',x))

The re.sub function essentially takes every comma and replaces it with a blank space. This following tutorial goes into each function of the re library in detail.

Now that we’ve gotten rid of the commas, we can easily convert the column into a numeric one. 

gdp['gdp_per_capita'] = gdp['gdp_per_capita'].apply(pd.to_numeric)

Now we can calculate a mean for the column.

We can see that the mean of the GDP per capita column is about $13037.27, something we couldn’t do if the column were classified as strings (which you can’t perform arithmetic operations on). We can now do all sorts of calculations on the GDP per capita column that we weren’t able to do before — including filtering the columns by different values and determining what percentile rank values are for the column.   

SELECTING/FILTERING DATA

The basic need of any data analyst is to slice and dice a large dataset into actionable insights. In order to do that, you have to go through a subset of the data you have: this is where selecting and filtering data is very helpful. In SQL, this is accomplished with a mix of SELECT and different other functions, while in Excel, this can be done by dragging and dropping through data and implementing filters.

Using the Pandas library, you can quickly filter down with different functions or queries.

Let’s, as a quick proxy, only show countries that have a GDP per capita above $50,000.

This is how to do it:

gdp50000 = gdp[gdp['gdp_per_capita'] > 50000]

We assign a new dataframe with a filter that takes a column and creates a boolean variable — this function above essentially says “create a new dataframe for which there is a GDP per capita above 50000”. Now we can display gdp50000.

And now we see that there are 12 countries with a GDP above 50000!

Now let’s select only rows that belong to a country that start with s.

We can now display a new dataframe containing only countries that start with s. A quick check with the len function (a life-saver for counting the number of rows in a dataframe!) indicates that we have 25 countries that fit the bill.

Now what if we want to chain those two filter conditions together?

Here’s where chained filtering comes in handy. You’ll want to understand how this works before filtering with multiple conditions. You’ll also want to understand the basic operators in Python. For the purposes of this exercise you just need to know that ‘&’ stands for AND — and that ‘ | ‘ stands for OR in Python. However, with a deeper understanding of all basic operators, you can easily manipulate data with all sorts of conditions. 

Let’s go ahead and work on filtering countries that both start with ‘S’ AND that have a GDP per capita above 50,000. 

sand500gdp = gdp[(gdp.gdp_per_capita > 50000) & (gdp.Country.str.startswith('S'))]

Now let’s work on those that start with S OR have over 50000 GDP per capita. 

sor500gdp = gdp[(gdp.gdp_per_capita > 50000) | (gdp.Country.str.startswith('S'))]

There we go! We’re well on our way to working with filtered views in Pandas.

MANIPULATE DATA WITH CALCULATIONS

What would Excel be without functions that help you calculate different results?

Pandas in this case leans heavily on the numpy library and general Python syntax to put calculations together. We’re going to go through a simple series of calculations on the GDP dataset we’ve been working on. Let’s for example, calculate the sum total of all GDP per capita countries that are over 50,000. 

gdp50000.gdp_per_capita.sum()

That’ll give you the answer of 770046. Using that same logic we can calculate all sorts of things — the full list can be located at the Pandas documentation under the computation/descriptive statistics section located on the menu bar at the left.

DATA VISUALIZATION (CHARTS/GRAPHS)

Data visualization is a very powerful tool — it allows you to share insights you’ve gained with others in an accessible format. A picture, after all, is worth a thousand words. SQL and Excel both have the capability to translate queries into charts and graphs. With the seaborn and matplotlib libraries, you can do the same with Python.

There are far more comprehensive tutorials on data visualization options — a favorite of mine is this Github readme document (all in text) which explains how to build probability distributions and a wide variety of plots in Seaborn. That should give you an idea of how powerful data visualization can be in Python. If you’re ever feeling overwhelmed, you can use a solution such as Plot.ly which might be more intuitive to grasp.

We’re not going to go through each and every data visualization option — suffice it to say that with Python, you’re going to have a lot more power to visualize things than anything SQL can offer, and you’ll have to trade-off the additional flexibility you gain with Python for how easy it is in Excel for generating charts from templates.

In this case, we’re going to build a simple histogram to show the distribution of GDP per capita for those countries that have more than $50,000 in GDP per capita. 

gdp50000.hist()

With this powerful histogram function (hist()) we can now generate a histogram that shows that most of the countries with a high GDP per capita cluster around the $50000 to $70000 range!

GROUPING AND JOINING DATA TOGETHER

Within Excel and SQL, powerful tools such as the JOIN function and pivot tables allow for the rapid aggregation of data.

Pandas and Python share many of the same functions that have been ported over from both SQL and Excel. You’ll be able to group data within datasets and join different datasets together. You can take a look here at the documentation. You’ll find that the join functionality offered by the merge function in Pandas is very similar to the one offered by SQL through the join command, while Pandas also offers pivot table functionality for those who are used to it in Excel.

We’re going to do a simple join here between the table we’ve developed with GDP per capita, and a list of world development indices from the World Bank.

Let’s first import the csv of country-level indicators. 

country = pd.read_csv("Country.csv")

Let’s do a quick .head() function to take a look at the different columns in this dataset.

Now that we’re done, we can take a quick look and see that we’ve added a few columns that we can play with, including different years where data was sourced.

Now let’s merge the data: 

gdpfinal = pd.merge(gdp,country, how = 'inner', left_on='Country', right_on = 'TableName')

We can now see the table incorporates elements of both our GDP per capita column and our new country-wide table with different data columns. For those familiar with SQL joins, you can see that we’re doing an inner join on the Country column of our original dataframe. 

Now that we have a joined table, we may want to group countries and their GDP per capita by the region of the world they’re in.

We can now use the group by functions in Pandas to play around with the data grouped by region. 

gdpregion = gdpfinal.groupby(['Region']).mean()

What if we want to see a permanent view of groupby summation? Groupby operations create a temporary object that can be manipulated, but they don’t create a permanent interface to aggregated results that can be built upon. For that, we’ll have to go through an old favorite of Excel users: the pivot table. Fortunately, pandas has a robust pivot table function.

gdppivot = gdpfinal.pivot_table(index=['Region'], margins=True, aggfunc=np.mean)

gdppivot

You’ll see we’ve picked up some extra columns we don’t need. Fortunately, with the drop function in Pandas, you can easily delete several columns.

gdppivot.drop(['LatestIndustrialData', 'LatestTradeData', 'LatestWaterWithdrawalData'], axis=1, inplace=True)

gdppivot

Now we can see that the GDP per capita differs depending on the regions in different parts of the world. We have a clean table with the data we want.

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This is a very superficial analysis: you’d want to actually do a weighted mean since a GDP per capita for each nation is not representative of the GDP per capita of every nation in a group since populations differ across the nations within a group.

In fact, you’ll want to redo all of our calculations involving means to reflect a population column for each country! See if you can do that within the Python notebook you’ve just started. If you can figure it out, you’ll have been well on your way to transferring your SQL or Excel knowledge to Python. 

Got any comments or questions? Please leave them in the comments section on this blog post 🙂 

Data Science/Artificial Intelligence, Learning Lists, Uncategorized

Learn Machine Learning With These Six Great Resources

BY

Learn Machine Learning 

A friend of code(love), Matt Fogel is doing awesome things with machine learning at fuzzy.io. He’s shared this valuable list of resources to learn machine learning that he usually gives his friends who ask him for more information.

You’ll see his original post here: https://medium.com/@mattfogel/master-the-basics-of-machine-learning-with-these-6-resources-63fea5a21c1c#.ta2bhsq8y

Learn machine learning with code(love)

Learn machine learning with code(love)

Great blog posts, podcasts and online courses to help you get started

It seems like machine learning and artificial intelligence are topics at the top of everyone’s mind in tech. Be it autonomous cars, robots, or machine intelligence in general, everyone’s talking about machines getting smarter and being able to do more.

Yet for many developers, machine learning and artificial intelligence are dense terms representing complex problems they just don’t have time to learn.

I’ve spoken with lots of developers and CTOs about Fuzzy.io and our mission to make it easy for developers to start bringing intelligent decision-making to their software without needing huge amounts of data or AI expertise. A lot of them were curious to learn more about the greater landscape of machine learning.

You can describe machine learning as using techniques to help computers learn new ways of uncovering insights from data. This deep dive into the topic will explore many elements outside of this short guide if you’re interested in learning more.

What you need to understand before you learn machine learning is that it’s not a magic buzzword that will help solve every problem with you. Machine learning is a practical way to get more data insights with less work. Nothing more, nothing less. 

To quote a professor in the field, “Machine learning is not magic; it can’t get something from nothing. What it does is get more from less. Programming, like all engineering, is a lot of work: we have to build everything from scratch. Learning is more like farming, which lets nature do most of the work. Farmers combine seeds with nutrients to grow crops. Learners combine knowledge with data to grow programs.”

If that excites you, here are some of the links to articles, podcasts and courses about machine learning that I’ve shared with my friends who were eager to learn more. I hope you enjoy!

Learn machine learning with code(love)

Learn machine learning with code(love)

1– A Gentle Guide to Machine Learning

This guide, written by the awesome Raul Garreta of MonkeyLearn, is perhaps one of the best I’ve read. In one easy-to-read article, he describes a number of applications of machine learning, the types of algorithms that exist, and how to choose which algorithm to use.

2– A Visual Introduction to Machine Learning

This piece by Stephanie Yee and Tony Chu of the R2D3 project gives a great visual overview of the creation of a machine learning model that determines whether an apartment is located in San Francisco or New York based on the traits they hold. It’s a great look into how machine learning models are created and how they work in practice.

Podcasts

3– Data Skeptic

A great starting point on some of the basics of data science and machine learning. Every other week, they release a 10–15 minute episode where the hosts (Kyle and Linhda Polich) give a short primer on topics like k-means clustering, natural language processing and decision tree learning. They often use analogies related to their pet parrot, Yoshi. This is the only place where you’ll learn about k-means clustering via placement of parrot droppings.

4– Linear Digressions

This weekly podcast, hosted by Katie Malone and Ben Jaffe, covers diverse topics in data science and machine learning. They teach specific advanced concepts like Hidden Markov Models and how they apply to real-world problems and datasets. They make complex topics extremely accessible, and teach you new words like clbuttic.

Online Courses

5– Intro to Artificial Intelligence

Plan for this online course to take several months, but you’d be hard-pressed to find better teachers than Peter Norvig and Sebastian Thrun. Norvig quite literally wrote the book on AI, having co-authored Artificial Intelligence: A Modern Approach, the most popular AI textbook in the world. Thrun’s no slouch either. He previously led the Google driverless car initiative.

6– Machine Learning

This 11-week long Stanford course is available online via Coursera. Its instructor is Andrew Ng, Chief Scientist at Chinese internet giant Baidu and one of the pioneers of online education. 

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This list is really only scratching some of the complex and multifaceted topic that is machine learning.  If you have your own favorite resource, please suggest it in the comments and start a discussion around it!