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Machine Learning with scikit-learn Quick Start Guide

By : Jolly

4.2 (5)

Buy this Book

Machine Learning with scikit-learn Quick Start Guide

4.2 (5)

By: Jolly

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Introducing Machine Learning with scikit-learn

A brief introduction to machine learning

What is scikit-learn?

Installing scikit-learn

Algorithms that you will learn to implement using scikit-learn

Summary

Predicting Categories with K-Nearest Neighbors

Technical requirements

Preparing a dataset for machine learning with scikit-learn

The k-NN algorithm

Implementing the k-NN algorithm using scikit-learn

Fine-tuning the parameters of the k-NN algorithm

Scaling for optimized performance

Summary

Predicting Categories with Logistic Regression

Technical requirements

Understanding logistic regression mathematically

Implementing logistic regression using scikit-learn

Fine-tuning the hyperparameters

Scaling the data

Interpreting the logistic regression model

Summary

Predicting Categories with Naive Bayes and SVMs

Technical requirements

The Naive Bayes algorithm

Support vector machines

Summary

Predicting Numeric Outcomes with Linear Regression

Technical requirements

The inner mechanics of the linear regression algorithm

Implementing linear regression in scikit-learn

Model optimization

Summary

Classification and Regression with Trees

Technical requirements

Classification trees

Regression trees

Ensemble classifier

Summary

Clustering Data with Unsupervised Machine Learning

Technical requirements

The k-means algorithm

Implementing the k-means algorithm in scikit-learn

Feature engineering for optimization

Cluster visualization

Going from unsupervised to supervised learning

Summary

Performance Evaluation Methods

Technical requirements

Why is performance evaluation critical?

Performance evaluation for classification algorithms

Performance evaluation for regression algorithms

Performance evaluation for unsupervised algorithms

Summary

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The k-NN algorithm

Mathematically speaking, the k-NN algorithm is one of the most simple machine learning algorithms out there. See the following diagram for a visual overview of how it works:

How k-NN works under the hood

The stars in the preceding diagram represent new data points. If we built a k-NN algorithm with three neighbors, then the stars would search for the three data points that are closest to it.

In the lower-left case, the star sees two triangles and one circle. Therefore, the algorithm would classify the star as a triangle since the number of triangles was greater than the number of circles.

In the upper-right case, the star sees two circles and one circle. Therefore, the algorithm will classify the star as a circle since the number of circles was greater than the number of triangles.

The real algorithm does this in a very probabilistic manner and picks the...

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