Thoughtful Machine Learning with Python: A Test-Driven Approach

by Matthew Kirk

By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment.

You ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for testing machine learning code. The author provides real-world examples to demonstrate the results of using machine-learning code effectively.

Featuring graphs and highlighted code throughout, Thoughtful Machine Learning with Python guides you through the process of writing problem-solving code, and in the process teaches you how to approach problems through scientific deduction and clever algorithms.

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Genres: Programming, Nonfiction, Artificial Intelligence, Computers, Reference, Technology, Computer Science

220 pages, Paperback

First published August 25, 2016

About the author

Hey, how’s it going? I’m Matthew Kirk, a software engineer based out of Seattle, WA. I’m also the author of Thoughtful Machine Learning, where I present doing test-driven software development with data in Ruby, and Thoughtful Machine Learning in Python (which is available for sale on February 4th, 2017).

I’ve been building web apps since 2009 and have always been “the data guy”, thanks to my applied math degree and my previous life as a financial quant.

In my career, I’ve been fortunate enough to speak around the world about software and work on interesting projects with later-stage startups. I’ve built social media sentiment engines, diamond recommendation tools, and e-commerce search algorithms...and always got frustrated with how my data projects never seemed to follow best development practices.

So instead of preaching to my coworkers, I wrote Thoughtful Machine Learning as a way to share my knowledge with the world.

There’s lots of machine learning books out there, and I recommend you take a look at all of them. But if you want the skinny, my books are for developers who don’t care about the nuances of academic theories of machine learning…and just want to implement something in their daily work.

If you ever want to chat machine learning, my email is matt@matthewkirk.com. I look forward to hearing from you.

Reviews

[Mahmoud Rabie · Rating 3 out of 5]

The book is a good summary on different machine learning algorithms (i.e. KNN, KMeans, EM Clustering, SVM), the author didn't use hard words in the explanations and covered the most basic information related to every algorithm covered in the book

What I didn't like in the book is the code samples, I don't know why the author decided to give focus on OOP and Unit testing concepts in a machine learning book, even if OOP and Unit testing can be used while solving learning problems, there are a lot of books that just focus on these concepts and there was no need to consume almost half of the book to cover such topics

Focusing on the OOPS/Unit testing in the code samples made them very hard to follow and most of the time I was just skipping the code samples part from every chapter

[Lindsey Maddox · Rating 2 out of 5]

This is admittedly a lot to cover in one book. Most of the conceptual overviews of different statistical methods were solid, but it was often hard to understand what the code was doing (cursory explanations and many python packages used). Not sure if it wasn't aimed at complete beginners but the stats methods seemed pretty beginner friendly. Decent introduction to the topic.

[Luis · Rating 2 out of 5]

Superficial explaination of algorithms and unpractical code applications

[Petr · Rating 3 out of 5]

It is a nice summary of basic machine learning concepts demonstrated in Python.

The concepts are sometimes introduced a little in a hurry but if you are looking for fast application and not a deep explanation of the theory then I think it has a sufficient level of depth. I liked how the code is always, as the title mentions, test driven and thus all the approaches are immediately followed by tests to verify that the algorith does what it is supposed to do. The code examples, although sometimes a little inconsistent in style, are quite clear for someone who is familiar with Python at least on a beginner level. The book, however, introduces a lot of new libraries and might at some points seem overwhelming to someone who is still very new to Python itself.

[Rick Sam · Rating 3 out of 5]

I took this book, while I am struggling to finish Pattern Recognition by Bishop.

Overall, an Excellent Gentle Introduction to Machine Learning.

I think, I'd recommend this as your first Machine Learning book if you want to know the basics. I have a summary of the book, if you want, do message me. Here is the outline

Outline:

### 1 Probably Approximately Correct Software

### 2 A Quick Introduction to Machine Learning

### 3 K-Nearest Neighbors

### 4 Naive Bayesian

### 5 Decision Trees and Random Forests

### 6 Hidden Markov Models

### 7 Support Vector Machines

### 8 Neural Networks

### 9 Clustering

### 10 Improving Models and Data Extraction

### 11 Putting it Together: Conclusion

Deus Vult,
Gottfried

[Jeff Gabriel · Rating 2 out of 5]

The TDD approach just didn't really work for me. It made the author come at the subject from a somewhat odd angle and didn't allow me as a reader to focus on the implementation of the ML algorithm as straightforwardly as I would like. I think teaching different concepts at the same time perhaps OK - but you probably get to choose just one that your reader will walk away with a strong grasp of. In this case I didn't feel like I walked away with any particular gems in python, ML, or TDD.

[Fakhredin Khorasani · Rating 1 out of 5]

This is just a summary.

[Antti · Rating 1 out of 5]

DNF. I'm probably lacking some prerequisite machine learning fundamentals, but the text was so difficult to follow that I felt I was getting nothing out of it.