The Machine Learning Workshop – Second Edition

eBook Details:

• Paperback: 286 pages
• Publisher: WOW! eBook (July 22, 2020)
• Language: English
• ISBN-10: 1839219068
• ISBN-13: 978-1839219061

eBook Description:

The Machine Learning Workshop, 2nd Edition: Take a comprehensive and step-by-step approach to understanding machine learning

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, 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.

• 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

By the end of The Machine Learning Workshop, 2nd 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.

[ Read also: The Rise of Machine Learning on The Edge: AI Everywhere article on The Cloud Navigator ]