Category: python > video tutorials for open source tools > video tutorials for open source tools is an e-learning platform that I really really really recommend to programmers and data scientists:

It is free.

It involves open source tools.

It uses bite-sized tutorial videos.

It explains tools clearly.

It explains everything calmly.

There’s tons of content about computer programming, data science, and personal productivity.

On top of this all, it’s by Vincent Warmerdam, and everything he touches seems to turn to gold.

Check it out here:

You can subscribe to their newsletter here, to receive new content fresh from the presses, straight in your inbox!

There are just so many tutorials on so many different topics! Here are some quick glances at some topics and tools:

Using OpenCV to win Mobile games

Using OpenCV to win Mobile games

OpenCV logo

OpenCV is open-source library with tools and functionalities that support computer vision. It allows your computer to use complex mathematics to detect lines, shapes, colors, text and what not.

OpenCV was originally developed by Intel in 2000 and sometime later someone had the bright idea to build a Python module on top of it.

Using a simple…

pip install opencv-python

…you can now use OpenCV in Python to build advanced computer vision programs.

And this is exactly what many professional and hobby programmers are doing. Specifically, to get their computer to play (and win) mobile app games.


In ZigZag, you are a ball speeding down a narrow pathway and your only mission is to avoid falling off.

Using OpenCV, you can get your computer to detect objects, shapes, and lines.

This guy set up an emulator on his computer, so the computer can pretend to be a mobile device. Then he build a program using Python’s OpenCV module to get a top score

You can find the associated code here, but note that will need to set up an emulator yourself before being able to run this code.

Kick Ya Chop

In Kick Ya Chop, you need to stomp away parts of a tree as fast as you can, without hitting any of the branches.

This guy uses OpenCV to perform image pattern matching to allow his computer to identify and avoid the trees braches. Find the code here.

Whack ‘Em All

We all know how to play Whack a Mole, and now this computer knows how to too. Code here.


This last game also doesn’t need an introduction, and you can find the code here.

Is this machine learning or AI?

If you’d ask me, the videos above provide nice examples of advanced automation. But there’s no real machine learning or AI involved.

Yes, sure, the OpenCV package uses pre-trained neural networks under the hood, and you can definitely call those machine learning. But the programmers who now use the opencv library just leverage the knowledge stored in those network to create very basal decision rules.

IF pixel pattern of mole
THEN whack!
ELSE no whack.

To me, it’s only machine learning when there’s really some learning going on. A feedback loop with performance improvement. And you may call it AI, IMO, when the feedback loop is more or less autonomous.

Fortunately, programmers have also been taking a machine learning/AI approach to beating games. Specifically using reinforcement learning. Think of famous applications like AlphaGo and AlphaStar. But there are also hobby programmers who use similar techniques. For example, to get their computer to obtain highscores on Trackmania.

In a later post, I’ll dive into those in more detail.

Become a Data Science Professional

Become a Data Science Professional

Amit Ness gathered an impressive list of learning resources for becoming a data scientist.

It’s great to see that he shares them publicly on his github so that others may follow along.

But beware, this learning guideline covers a multi-year process.

Amit’s personal motto seems to be “Becoming better at data science every day“.

Completing the hyperlinked list below will take you several hundreds days at the least!

Learning Philosophy:


How a File Format Exposed a Crossword Scandal

Vincent Warmerdam shared this Youtube video which I thoroughly enjoyed watched. It’s about Saul Pwanson, a software engineer whose hobby project got a little out of hand.

In 2016, Saul Pwanson designed a plain-text file format for crossword puzzle data, and then spent a couple of months building a micro-data-pipeline, scraping tens of thousands of crosswords from various sources.

After putting all these crosswords in a simple uniform format, Saul used some simple command line commands to check for common patterns and irregularities.

Surprisingly enough, after visualizing the results, Saul discovered egregious plagiarism by a major crossword editor that had gone on for years.

Ultimately, 538 even covered the scandal:

I thoroughly enjoyed watching this talk on Youtube.

Saul covers the file format, data pipeline, and the design choices that aided rapid exploration; the evidence for the scandal, from the initial anomalies to the final damning visualization; and what it’s like for a data project to get 15 minutes of fame.

I tried to localize the dataset online, but it seems Saul’s website has since gone offline. If you do happen to find it, please do share it in the comments!

Bayesian Statistics using R, Python, and Stan

Bayesian Statistics using R, Python, and Stan

For a year now, this course on Bayesian statistics has been on my to-do list. So without further ado, I decided to share it with you already.

Richard McElreath is an evolutionary ecologist who is famous in the stats community for his work on Bayesian statistics.

At the Max Planck Institute for Evolutionary Anthropology, Richard teaches Bayesian statistics, and he was kind enough to put his whole course on Statistical Rethinking: Bayesian statistics using R & Stan open access online.

You can find the video lectures here on Youtube, and the slides are linked to here:

Richard also wrote a book that accompanies this course:

For more information abou the book, click here.

For the Python version of the code examples, click here.

Handling and Converting Data Types in Python Pandas

Handling and Converting Data Types in Python Pandas

Data types are one of those things that you don’t tend to care about until you get an error or some unexpected results. It is also one of the first things you should check once you load a new data into pandas for further analysis.

Chris Moffit

In this short tutorial, Chris shows how to the pandas dtypes map to the numpy and base Python data types.

A screenshot of the data type mapping.

Moreover, Chris demonstrates how to handle and convert data types so you can speed up your data analysis. Both using custom functions and anonymous lambda functions.

A snapshot from the original blog.

A very handy guide indeed, after which you will be able to read in your datasets into Python in the right format from the get-go!

Using data type casting, lambda functions, and functional programming to read in data in Python. Via