Hyatt Saleh - The Machine Learning Workshop: Get ready to develop your own high-performance machine learning algorithms with scikit-learn, 2nd Edition
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- Book:The Machine Learning Workshop: Get ready to develop your own high-performance machine learning algorithms with scikit-learn, 2nd Edition
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Take a comprehensive and step-by-step approach to understanding machine learning
Key Features- Discover how to apply the scikit-learn uniform API in all types of machine learning models
- Understand the difference between supervised and unsupervised learning models
- Reinforce your understanding of machine learning concepts by working on real-world examples
Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, youll master the scikit-learn library and become proficient in developing clever machine learning algorithms.
The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once youve got to grips with the basics, youll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, youll study the dataset of a banks marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. Youll also learn how to compare these models and select the optimal one.
By the end of The Machine Learning Workshop, youll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but youll also have developed the skills required to get started with programming your very own machine learning algorithms.
What you will learn- Understand how to select an algorithm that best fits your dataset and desired outcome
- Explore popular real-world algorithms such as K-means, Mean-Shift, and DBSCAN
- Discover different approaches to solve machine learning classification problems
- Develop neural network structures using the scikit-learn package
- Use the NN algorithm to create models for predicting future outcomes
- Perform error analysis to improve your models performance
The Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.
Table of Contents- Introduction to Scikit-Learn
- Unsupervised Learning Real-Life Applications
- Supervised Learning Key Steps
- Supervised Learning Algorithms: Predicting Annual Income
- Artificial Neural Networks: Predicting Annual Income
- Building Your Own Program
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Get ready to develop your own high-performance machine learning algorithms with scikit-learn
Hyatt Saleh
Copyright 2020 Packt Publishing
All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this course to ensure the accuracy of the information presented. However, the information contained in this course is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this course.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Author: Hyatt Saleh
Reviewers: John Wesley Doyle, Akshat Gupta, Harshil Jain, Vikraman Karunanidhi, Subhojit Mukherjee, Madhav Pandya, Aditya Rane, and Subhash Sundaravadivelu
Managing Editor: Rutuja Yerunkar
Acquisitions Editors: Manuraj Nair, Kunal Sawant, Sneha Shinde, Anindya Sil, and Karan Wadekar
Production Editor: Shantanu Zagade
Editorial Board: Megan Carlisle, Samuel Christa, Mahesh Dhyani, Heather Gopsill, Manasa Kumar, Alex Mazonowicz, Monesh Mirpuri, Bridget Neale, Dominic Pereira, Shiny Poojary, Abhishek Rane, Brendan Rodrigues, Erol Staveley, Ankita Thakur, Nitesh Thakur, and Jonathan Wray
First published: November 2018
Second edition: July 2020
Production reference: 1210720
ISBN: 978-1-83921-906-1
Published by Packt Publishing Ltd.
Livery Place, 35 Livery Street
Birmingham B3 2PB, UK
Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, Second Edition, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms.
The Machine Learning Workshop, Second Edition, begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one.
By the end of The Machine Learning Workshop, Second Edition, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.
The Machine Learning Workshop, Second Edition, is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.
Chapter 1, Introduction to Scikit-Learn, introduces the two main topics of the book: machine learning and scikit-learn. It explains the key steps of preprocessing your input data, separating the features from the target, dealing with messy data, and rescaling the values of data.
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