Python Machine Learning by Example

The easiest way to get into machine learning

TLDR

This is my first published book and my best-selling book. The book takes a practical approach while explaining the machine learning concepts. You can grasp machine learning concepts, techniques, and algorithms with the help of real-world examples and projects in Python from scratch and using libraries such as scikit-learn.

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There is also a Korean version

Key Features

Learn the fundamentals of machine learning and build your own intelligent applications
Master the art of building your own machine learning systems with this example-based practical guide
Work with important classification and regression algorithms and other machine learning techniques

Book Description

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning.

This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms - they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.

Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.

What you will learn

Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
Use Python to visualize data spread across multiple dimensions and extract useful features
Dive deep into the world of analytics to predict situations correctly
Implement machine learning classification and regression algorithms from scratch in Python
Be amazed to see the algorithms in action
Evaluate the performance of a machine learning model and optimize it
Solve interesting real-world problems using machine learning and Python as the journey unfolds

Who this book is for

If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.

Getting Started with Python and Machine Learning
Exploring the 20 newsgroups data set
Spam email detection with Naïve Bayes
News topic classification with Support Vector Machine
Click-through prediction with tree-based algorithms
Click-through rate prediction with logistic regression
Stock prices prediction with regression algorithms
Best practices

What readers said

There are currently 46 reviews in Amazon globally, for example:

I have gotten very good ideas and examples from reading and doing practicing the scripts of python.
– From David E. (US)

As someone who is trying to deepen his knowledge on Machine learning, I appreciate the logical and easy to follow thought process by the author. The book articulates the concept very well through both theory and real world application of python in machine learning. Many examples contained in the book owes to the authors experience working in the field with real data and challenges. This is shown by the author through viewing at problems through multiple angles, discussions and possibilities which provide reader an extensive learning experience.

I would recommend to aspiring data scientist of all levels especially to those who are eager to get more exposure to machine learning. This book is one of the better written machine learning book who should be a mandatory read on the subject.

After finishing the book, the only thing that comes to find would be for the author to hopefully release a future series on the subject.
– From A. Albert (US)
Review in article The Best Machine Learning Books for All Skill Levels :

What makes it the best: As the name suggests, the book takes a practical approach while explaining the Machine Learning concepts to readers. The book also helps the reader with Python concepts, enabling them to implement their knowledge using the rich set of libraries offered by Python frameworks. It covers many ML concepts, such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation.

Author Yuxi engages readers into various exercises and helps them at every step to implement some of the important ML models.

Overall, the book offers a broader coverage as well as in-depth understanding of Machine Learning as a field. The excellent reader reviews and user ratings proves this fact. And best of all, it’s reasonably priced compared to other practical ML books.

In the end…

Let me know if you are interested in reading or reviewing this book, or you want to chat about machine learning.