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


The Machine Learning Workshop: Get ready to develop your own high-performance machine learning algorithms with scikit-learn, 2nd Edition
By 作者:Hyatt Saleh
pages 页数: 286 pages
Publisher Finelybook 出版社: Packt Publishing; 2nd edition edition (22 July 2020)
Language 语言: English
ISBN-10 书号:1839219068
ISBN-13 书号:9781839219061
The Book Description robot was collected from Amazon and arranged by Finelybook
Take a comprehensive and step-By 作者:-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 begins By 作者:demonstrating how unsupervised and supervised learning algorithms work By 作者: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 作者: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

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