Python Machine Learning by Example 2nd Edition

Implement machine learning algorithms and techniques to build intelligent systems

TLDR

This is the second edition of my best-selling book Python Machine Learning by Example. In this book, you can grasp machine learning concepts, techniques, and algorithms with the help of real-world examples and projects using Python libraries such as TensorFlow and scikit-learn.

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Preview or get it in Amazon US, India, UK, Canada, or your local Amazon store
Preview or get it in O’Reilly, Packt
Preview or get it in Google Play
There is also a Japanese version

Key Features

Exploit the power of Python to explore the world of data mining and data analytics
Discover machine learning algorithms to solve complex challenges faced by data scientists today
Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects

Book Description

A surging interest in machine learning is due to the fact that it evolutionizes automation by learning patterns in data and using them to make predictions and decisions. Your ML journey starts with this book, as the second edition of the bestseller, Python Machine Learning By Example.

Hayden’s unique insights and expertise introduce you to important ML concepts and implementations of algorithms in Python both from scratch and with libraries. Each chapter of the book walks you through an industry adopted application. With the help of realistic examples, you will find it intriguing to acquire mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP - they are no more obscure as you thought.

This critically extended and updated edition now includes implementation with trendy libraries including TensorFlow, gensim and Keras. The scikit-learn codes are also fully modernized. Even if you’ve read the last edition, you’ll still be delighted to find plenty of new content, for example, neural network, dimensionality reduction, topic modeling, large-scale learning with Spark and word embedding.

Toward the end, you will gather a broad picture of the ML ecosystem and best practices of applying ML techniques to meet new opportunities in today’s world.

What you will learn

Understand the important concepts in machine learning and data science
Use Python to explore the world of data mining and analytics
Scale up model training using varied data complexities with Apache Spark
Delve deep into text and NLP using Python libraries such NLTK and gensim
Select and build an ML model and evaluate and optimize its performance
Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn

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 Machine Learning and Python
Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
Detecting Spam Email with Naive Bayes
Classifying News Topic with Support Vector Machine
Predicting Online Ads Click-through with Tree-Based Algorithms
Predicting Online Ads Click-through with Logistic Regression
Scaling Up Prediction to Terabyte Click Logs
Stock Price Prediction with Regression Algorithms
Machine Learning Best Practices

What readers said

There are currently 6 reviews in Amazon globally, for example:

Great book to review some machine learning algorithms. – From Sunil Thapa (US)

Great machine learning book with easy to follow examples! Would definitely recommend this book to anyone who is looking to gain a deeper understanding of machine learning modeling and algorithms. – From Qiao Wang (Canada)
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.