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Machine Learning with scikit-learn Quick Start Guide

By : Kevin Jolly

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Buy this Book

Machine Learning with scikit-learn Quick Start Guide

4.2 (5)

By: Kevin 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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Scaling for optimized performance

The k-NN algorithm is an algorithm that works based on distance. When a new data point is thrown into the dataset and the algorithm is given the task of classifying this new data point, it uses distance to check the points that are closest to it.

If we have features that have different ranges of values – for example, feature one has a range between 0 to 800 while feature two has a range between one to five – this distance metric does not make sense anymore. We want all the features to have the same range of values so that the distance metric is on level terms across all features.

One way to do this is to subtract each value of each feature by the mean of that feature and divide by the variance of that feature. This is called standardization: