Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
by: Tarek Amr
Print Length 页数: 384 pages
Publisher finelybook 出版社: Packt Publishing (24 July 2020)
Language 语言: English
ISBN-10: 1838826041
ISBN-13: 9781838826048
Book Description
By finelybook
Integrate scikit-learn with various tools such as NumPy,pandas,imbalanced-learn,and scikit-surprise and use it to solve real-world machine learning problems
Machine learning is applied everywhere,from business to research and academia,while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.
The book begins with an explanation of machine learning concepts and fundamentals,and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms,and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm,Bayesian estimation,a deep neural network,a tree-based ensemble,or a recommendation system,you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance,you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.
By the end of this machine learning book,you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand,prepare required data,and evaluate and deploy models in production.
What you will learn
Understand when to use supervised,unsupervised,or reinforcement learning algorithms
Find out how to collect and prepare your data for machine learning tasks
Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
Apply supervised and unsupervised algorithms to overcome various machine learning challenges
Employ best practices for tuning your algorithm’s hyper parameters
Discover how to use neural networks for classification and regression
Build,evaluate,and deploy your machine learning solutions to production
