Preface

The fundamental aim of this book is help its readers quickly deploy, optimize, and evaluate every kind of machine learning algorithm that scikit-learn provides in an agile manner.

Readers will learn how to deploy supervised machine learning algorithms, such as logistic regression, k-nearest neighbors, linear regression, Support Vector Machines, Naive Bayes, and tree-based algorithms, in order to solve classification and regression machine learning problems.

Readers will also learn how to deploy unsupervised machine learning algorithms such as the k-means algorithm in order to cluster unlabeled data into groups.

Finally, readers will be provided with different techniques to visually interpret and evaluate the performance of the algorithms that they build.

Who this book is for

This book is for data scientists, software engineers, and people interested in machine learning with a background in Python who would like to understand, implement, and evaluate a wide range of machine learning algorithms using the scikit-learn framework.

What this book covers

Chapter 1, Introducing Machine Learning with scikit-learn, is a brief introduction to the different types of machine learning and its applications.

Chapter 2, Predicting Categories with K-Nearest Neighbors, covers working with and implementing the k-nearest neighbors algorithm to solve classification problems in scikit-learn.

Chapter 3, Predicting Categories with Logistic Regression, explains the workings and implementation of the logistic regression algorithm when solving classification problems in scikit-learn.

Chapter 4, Predicting Categories with Naive Bayes and SVMs, explains the workings and implementation of the Naive Bayes and the Linear Support Vector Machines algorithms when solving classification problems in scikit-learn.

Chapter 5, Predicting Numeric Outcomes with Linear Regression, explains the workings and implementation of the linear regression algorithm when solving regression problems in scikit-learn.

Chapter 6, Classification and Regression with Trees, explains the workings and implementation of tree-based algorithms such as decision trees, random forests, and the boosting and ensemble algorithms when solving classification and regression problems in scikit-learn.

Chapter 7, Clustering Data with Unsupervised Machine Learning, explains the workings and implementation of the k-means algorithm when solving unsupervised problems in scikit-learn.

Chapter 8, Performance Evaluation Methods, contains visual performance evaluation techniques for supervised and unsupervised machine learning algorithms.

To get the most out of this book

To get the most out of this book:

• Prior knowledge of Python is assumed at a basic level.
• Jupyter Notebook as a development environment is preferred but not necessary.

Download the example code files

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-with-scikit-learn-Quick-Start-Guide. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Code in action

Visit the following link to check out videos of the code being run:
http://bit.ly/2OcWIGH

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

from sklearn.naive_bayes import GaussianNB

#Initializing an NB classifier

nb_classifier = GaussianNB()

#Fitting the classifier into the training data

nb_classifier.fit(X_train, y_train)

#Extracting the accuracy score from the NB classifier

nb_classifier.score(X_test, y_test)

Warnings or important notes appear like this.

Tips and tricks appear like this.

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