The Machine Learning Workshop: Get ready to develop your own high-performance machine learning algorithms with scikit-learn, 2nd Edition - Softcover

Hyatt Saleh

Softcover

ISBN 10: 1839219068 ISBN 13: 9781839219061

Verlag: Packt Publishing, 2020

Inhaltsangabe

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

Book Description

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, you'll 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 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, 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.

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 model's performance

Who this book is for

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

1. Introduction to Scikit-Learn
2. Unsupervised Learning - Real-Life Applications
3. Supervised Learning - Key Steps
4. Supervised Learning Algorithms: Predicting Annual Income
5. Artificial Neural Networks: Predicting Annual Income
6. Building Your Own Program

Über diesen Titel

Über die Autorin bzw. den Autor

Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. She has also authored the book Machine Learning Fundamentals, by Packt Publishing.

Von der hinteren Coverseite

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 Book Description 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, you'll 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 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, 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. 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 model's performance Who this book is for 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.

Details zur Ausgabe

• Verlag: Packt Publishing
• Erscheinungsdatum: 2020
• Sprache: Englisch
• ISBN 10: 1839219068
• ISBN 13: 9781839219061
• Einband: Taschenbuch
• Auflage: 2
• Anzahl der Seiten: 286
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: gpsr@libri.de; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Deutschland