Mastering Machine Learning With scikit-learn

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.

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

Table of Contents

Chapter 1: The Fundamentals of Machine Learning
Chapter 2: Linear Regression
Chapter 3: Feature Extraction and Pre-Processing
Chapter 4: From Linear Regression to Logistic Regression
Chapter 5: Non-linear Classification and Regression with Decision Trees
Chapter 6: Clustering with K-Means
Chapter 7: Dimensionality Reduction with PCA
Chapter 8: The Perceptron
Chapter 9: From the Perceptron to Support Vector Machines
Chapter 10: From the Perceptron to Artificial Neural Networks