Machine Learning with scikit-learn Quick Start Guide: Classification,regression,and clustering techniques in Python
by: Kevin Jolly
Print Length 页数: 172 pages
Publisher finelybook 出版社: Packt Publishing (30 Oct. 2018)
Language 语言: English
ISBN-10: 1789343704
ISBN-13: 9781789343700
Book Description
By finelybook
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification,regression,and clustering.
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.
What you will learn
Learn how to work with all scikit-learn’s machine learning algorithms
Install and set up scikit-learn to build your first machine learning model
Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups
Perform classification and regression machine learning
Use an effective pipeline to build a machine learning project from scratch
Table of Contents
Preface
Chapter 1: Introducing Machine Learning with scikit-learn
Chapter 2: Predicting Categories with K-Nearest Neighbors
Chapter 3: Predicting Categories with Logistic Regression
Chapter 4: Predicting Categories with Naive Bayes and SVMs
Chapter 5: Predicting Numeric Outcomes with Linear Regression
Chapter 6: Classification and Regression with Trees
Chapter 7: Clustering Data with Unsupervised Machine Learning
Chapter 8: Performance Evaluation Methods
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