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Machine Learning with scikit-learn Quick Start Guide

You're reading from Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781789343700

Length 172 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Kevin Jolly

View More author details

Table of Contents (10) Chapters

Preface

1. Introducing Machine Learning with scikit-learn FREE CHAPTER

2. Predicting Categories with K-Nearest Neighbors

3. Predicting Categories with Logistic Regression

4. Predicting Categories with Naive Bayes and SVMs

5. Predicting Numeric Outcomes with Linear Regression

6. Classification and Regression with Trees

7. Clustering Data with Unsupervised Machine Learning

8. Performance Evaluation Methods

9. Other Books You May Enjoy

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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.

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Authors (1)

Jolly

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.

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