You're reading from Machine Learning with scikit-learn Quick Start Guide

Product typeBook

Published inOct 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781789343700

Edition1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1)

Kevin Jolly

Algorithms that you will learn to implement using scikit-learn

The algorithms that you will learn about in this book are broadly classified into the following two categories:

Supervised learning algorithms
Unsupervised learning algorithms

Supervised learning algorithms

Supervised learning algorithms can be used to solve both classification and regression problems. In this book, you will learn how to implement some of the most popular supervised machine learning algorithms. Popular supervised machine learning algorithms are the ones that are widely used in industry and research, and have helped us solve a wide range of problems across a wide range of domains. These supervised learning algorithms are as follows:

Linear regression: This supervised learning algorithm is used to predict continuous numeric outcomes such as house prices, stock prices, and temperature, to name a few
Logistic regression: The logistic learning algorithm is a popular classification algorithm that is especially used in the credit industry in order to predict loan defaults
k-Nearest Neighbors: The k-NN algorithm is a classification algorithm that is used to classify data into two or more categories, and is widely used to classify houses into expensive and affordable categories based on price, area, bedrooms, and a whole range of other features
Support vector machines: The SVM algorithm is a popular classification algorithm that is used in image and face detection, along with applications such as handwriting recognition
Tree-Based algorithms: Tree-based algorithms such as decision trees, Random Forests, and Boosted trees are used to solve both classification and regression problems
Naive Bayes: The Naive Bayes classifier is a machine learning algorithm that uses the mathematical model of probability to solve classification problems

Unsupervised learning algorithms

Unsupervised machine learning algorithms are typically used to cluster points of data based on distance. The unsupervised learning algorithm that you will learn about in this book is as follows:

k-means: The k-means algorithm is a popular algorithm that is typically used to segment customers into unique categories based on a variety of features, such as their spending habits. This algorithm is also used to segment houses into categories based on their features, such as price and area.

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Machine Learning with scikit-learn Quick Start Guide

Published in: Oct 2018Publisher: PacktISBN-13: 9781789343700

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Author (1)

Kevin Jolly

Kevin Jolly is a formally educated data scientist with a master's degree in data science from the prestigious King's College London. Kevin works as a statistical analyst with a digital healthcare start-up, Connido Limited, in London, where he is primarily involved in leading the data science projects that the company undertakes. He has built machine learning pipelines for small and big data, with a focus on scaling such pipelines into production for the products that the company has built. Kevin is also the author of a book titled Hands-On Data Visualization with Bokeh, published by Packt. He is the editor-in-chief of Linear, a weekly online publication on data science software and products.
Read more about Kevin Jolly

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