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Machine Learning with scikit-learn Quick Start Guide
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Machine Learning with scikit-learn Quick Start Guide
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Overview of this book
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.
This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.
Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
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Current Title:
Machine Learning with scikit-learn Quick Start Guide
Table of Contents (10 chapters)
Preface
Free Chapter
Introducing Machine Learning with scikit-learn
Predicting Categories with K-Nearest Neighbors
Predicting Categories with Logistic Regression
Predicting Categories with Naive Bayes and SVMs
Predicting Numeric Outcomes with Linear Regression
Classification and Regression with Trees
Clustering Data with Unsupervised Machine Learning
Performance Evaluation Methods
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