Free eBook: Python Machine Learning By Example

Python Machine Learning By Example
Take tiny steps to enter the big world of data science through this interesting guide

Yuxi (Hayden) Liu, 254 pages, May 2017

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


This book is your entry point to machine learning. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, Python. Interesting and easy-to-follow examples will keep you glued till you learn what machine learning is and how to implement it.

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Chapter 1


Getting Started with Python and Machine Learning

Kick off your Python and machine learning journey with the basic, yet important concepts of machine learning. Starting with what machine learning is about, why we need it, and its evolution. We will then discuss typical machine learning tasks and explore several essential techniques of working wi...

Chapter 2


Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms

Learn the fundamental concepts of NLP as an important subfield in machine learning, including tokenization, stemming and lemmatization, POS tagging. We then look through clustering and implementations of k-means clustering and non-negative matrix factorization for topic modeling.

Chapter 3


Spam Email Detection with Naive Bayes

Begin a machine learning classification journey with spam email detection. It is a great starting point of learning classification with a real-life example-our email service providers are already doing this for us. Learn the fundamental and important concepts of classification, and focusing on so...

Chapter 4


News Topic Classification with Support Vector Machine

Work through an example focusing on the support vector machine classifier, acquire the mechanics of SVM, kernel techniques and implementations of SVM, and other important concepts of machine learning classification, including multiclass classification strategies and grid search, as well as useful...

Chapter 5


Click-Through Prediction with Tree-Based Algorithms

Solve one of the most important machine learning problems in digital online advertising, click-through prediction—given a user and the page they are visiting, how likely they will click on a given ad. Focusing on learning tree-based algorithms, decision tree and random forest, and utilizing them...

Chapter 6


Click-Through Prediction with Logistic Regression

Focusing on learning a preprocessing technique, one-hot encoding, logistic regression algorithm, regularization methods for logistic regression, and its variant that is applicable to very large datasets. Also looking at how logistic regression is used in picking significant features.

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