Mastering Machine Learning with Scikit-Learn

by Gavin Hackeling

Apply effective learning algorithms to real-world problems using scikit-learn About This Book Design and troubleshoot machine learning systems for common tasks including regression, classification, and clustering Acquaint yourself with popular machine learning algorithms, including decision trees, logistic regression, and support vector machines A practical example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learn Who This Book Is For

If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.

What You Will Learn
Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics
Predict the values of continuous variables using linear regression
Create representations of documents and images that can be used in machine learning models
Categorize documents and text messages using logistic regression and support vector machines
Classify images by their subjects
Discover hidden structures in data using clustering and visualize complex data using decomposition
Evaluate the performance of machine learning systems in common tasks
Diagnose and redress problems with models due to bias and variance
In Detail

This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.

You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices.

By the end of the book, you will be an expert in scikit-learn and will be well versed in machine learning

Genres: Programming, Artificial Intelligence, Technology, Computers, Technical

254 pages, Paperback

First published November 10, 2014

Reviews

[Jeff_abrahamson · Rating 4 out of 5]

The book is a reasonable soft introduction to machine learning concepts for practitioners whose goal is to understand just enough of the theory to use the tools. The explanations are sometimes imprecise analogies, but usually seem to communicate about the right intuition. The book could do with some exercises to guide new learners but is otherwise a quite good introduction.

That said, did anyone proof-read the mathematics? For example, on page 40 of the pdf, a missing absolute value suggests that L1 norms can be negative. At another point he explains that only square matrices are invertible (true) and so we multiply by the transpose in order to get a square matrix that we can invert. Mechanically correct, but otherwise wrong: just because we have a square matrix doesn't make it meaningful to invert it.

The text is rife with this sort of almost-correct stuff. "Occam's razor states that a hypothesis with the fewest assumptions is the best". Actually, it states that among competing hypotheses and in the absence of certainty, one should tentatively prefer the hypothesis with fewest assumptions. until we have more data. "Hyperparameters are parameters of the model that are not learned automatically and must be set manually". Actually, hyperparameters are parameters of the prior distribution rather than parameters of the model.

A few points are differently perplexing. Why in 2014 does he use python 2.7 instead of python 3.x to illustrate his examples? Why does he not even mention ipython? Why does he use manifestly bad variables names (for example, "xx")?

Some of my biggest niggles concerned the EPUB formatting. Reading on my Nexus 7, the mathematics scaled differently (smaller) than the rest of the text, even rendering unreadably small for inline symbols. Many of the figures were also too small to read without enlargement. The python code itself wrapped awkwardly. Numbers are often left-justified where convention would right-justify or justify on the decimal point. The book was almost unreadable for me in epub. The pdf was fine on a 10" tablet but not on a 7" tablet. Otherwise, stick with paper.

Overall, the book is a decent enough introduction to machine learning concepts for those who just need to use its techniques and who will have plenty of opportunity to test their results. This last is important, and largely absent in the book: machine learning techniques are not use and forget. We try things, we measure, and then we try some more. And we keep measuring, because the underlying data may change in subtle ways over time. Whatever theoretical mistakes result from a high-level-only view become even more important to measure.

[Okeyo Mayaka · Rating 5 out of 5]

Excellent book overall. I read this book as a complete beginner to Machine Learning, and I was more than satisfied with the content therein. I liked the structuring of the chapters where a model[s] would be discussed then after training, the emphasis is shifted to performance metrics to use to evaluate the model[s]. I found this informative since most books I'd read to this point only discussed the model, training the model, and nothing more. I would recommend this for a beginner in Machine Learning.

[Moustafa · Rating 4 out of 5]

This book presents a very gentle introduction to machine learning in python using scikit-learn. It differs a broad range of machine learning methods (linear regression, logistic regression, svm, clustering, neural networks, etc.) with real examples and Complete Code to run those tasks by your self. This is very certainly an effective way to teach to use the scikit-learn framework.

On the other hand, I have some comments about how the book could have been better:
1. It assumes a good knowledge of python and numpy (which is used extensively by scikit-learn implementation), adding an introduction chapter (or perhaps an appendix) with quick tutorials about both would have made the meal more complete.

2. This is book is NOT machine learning book !!, it provides quick overview about the methods but it focuses more on the scikit-learn API and the code examples. If you are eager to learn more about machine learning then you still need to read a real machine learning book to understand the theory and limitations and of the various methods.

3. Some minor typos exist :)

My overall recommendation is that the book achieves it goal in providing quick hands-on experiments of machine with sklearn to those who look up to !

[Phil Moyer · Rating 4 out of 5]

Another excellent book from this series, it deals exclusively with the scikit-learn library in Python. This is more advanced than Python Machine Learning, but it is a very good book. It does not delve too deeply into the mathematics of machine learning systems, so it is much more "applied science" than, for example, Machine Learning: A Probabilistic Perspective, which is extremely technical and mathematical (or rather, statistical) in nature. You'll learn how to build machine learning systems from this book, while you'll learn (or be seriously challenged to learn) the back theory in Murphy's book. If you want to build ML systems In Real Life, this is one of the books to grab.

[Franck Chauvel · Rating 4 out of 5]

As the title suggests, this is about machine learning algorithm using Python scikit-learn library.

As for algorithms, the content is very similar to other machine learning books such as Machine Learning for Hackers, but also includes decision trees and random forests. I find the text really easy to read, and I appreciated the effort made to convey the intuition beyond the formulas.

As for Python code examples, although some are out-of-date, I found the documentation of scikit-learn detailed enough and I managed to reproduce the ones I was interested in without any problem.

[Stein Karlsen · Rating 4 out of 5]

A good overview over some of the machine learning algorithms in scikit learn.

[Takedown · Rating 3 out of 5]

Expand on documentation but not as practical as i would expected.