Machine Learning with scikit-learn Quick Start Guide
Classification, regression, and clustering techniques in Python
Published on 30. October 2018
Book
Paperback/Softback
172 pages
978-1-78934-370-0 (ISBN)
Description
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.
Key Features
Build your first machine learning model using scikit-learn
Train supervised and unsupervised models using popular techniques such as classification, regression and clustering
Understand how scikit-learn can be applied to different types of machine learning problems
Book DescriptionScikit-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.
What you will learn
Learn how to work with all scikit-learn s machine learning algorithms
Install and set up scikit-learn to build your first machine learning model
Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups
Perform classification and regression machine learning
Use an effective pipeline to build a machine learning project from scratch
Who this book is forThis book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.
Key Features
Build your first machine learning model using scikit-learn
Train supervised and unsupervised models using popular techniques such as classification, regression and clustering
Understand how scikit-learn can be applied to different types of machine learning problems
Book DescriptionScikit-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.
What you will learn
Learn how to work with all scikit-learn s machine learning algorithms
Install and set up scikit-learn to build your first machine learning model
Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups
Perform classification and regression machine learning
Use an effective pipeline to build a machine learning project from scratch
Who this book is forThis book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 10 mm
Weight
334 gr
ISBN-13
978-1-78934-370-0 (9781789343700)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kevin Jolly
Machine Learning with scikit-learn Quick Start Guide
Classification, regression, and clustering techniques in Python
E-Book
09/2024
Packt Publishing
€23.49
Available for download
Person
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.
Content
Table of Contents
Introducing Machine Learning with scikit-learn
Predicting categories with K-Nearest Neighbours
Predicting categories with Logistic Regression
Predicting categories with Naive Bayes and SVMs
Predicting numeric outcomes with Linear Regression
Classification & Regression with Trees
Clustering data with Unsupervised Machine Learning
Performance evaluation methods
Introducing Machine Learning with scikit-learn
Predicting categories with K-Nearest Neighbours
Predicting categories with Logistic Regression
Predicting categories with Naive Bayes and SVMs
Predicting numeric outcomes with Linear Regression
Classification & Regression with Trees
Clustering data with Unsupervised Machine Learning
Performance evaluation methods