Machine Learning with scikit-learn Quick Start Guide

Classification, regression, and clustering techniques in Python
 
 
Packt Publishing
  • erschienen am 30. Oktober 2018
 
  • Buch
  • |
  • Softcover
  • |
  • 172 Seiten
978-1-78934-370-0 (ISBN)
 
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.
 

<b>Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.</b>

Key Features<ul><li>Build your first machine learning model using scikit-learn</li><li>Train supervised and unsupervised models using popular techniques such as classification, regression and clustering</li><li>Understand how scikit-learn can be applied to different types of machine learning problems</li></ul>Book Description

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.

What you will learn<ul><li>Learn how to work with all scikit-learn's machine learning algorithms</li><li>Install and set up scikit-learn to build your first machine learning model</li><li>Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups</li><li>Perform classification and regression machine learning</li><li>Use an effective pipeline to build a machine learning project from scratch</li></ul>Who this book is for

This 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.

  • Englisch
  • Birmingham
  • |
  • Großbritannien
  • Höhe: 235 mm
  • |
  • Breite: 191 mm
  • |
  • Dicke: 9 mm
  • 332 gr
978-1-78934-370-0 (9781789343700)
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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.
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.
Table of Contents<ol><li>Introducing Machine Learning with scikit-learn</li><li>Predicting categories with K-Nearest Neighbours</li><li>Predicting categories with Logistic Regression</li><li>Predicting categories with Naive Bayes and SVMs</li><li>Predicting numeric outcomes with Linear Regression</li><li>Classification & Regression with Trees</li><li>Clustering data with Unsupervised Machine Learning</li><li>Performance evaluation methods</li></ol>

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