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A new second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems, updated to include Python 3.8 and TensorFlow 2.x as well as the latest in new algorithms and techniques.
Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges.
This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to ...
This book is your guide to quickly get to grips with the most widely used machine learning algorithms. As a data science professional, this book will help you design and train better machine learning models to solve a variety of complex problems, and make the machine learn your requirements.
Helps you build a strong foundation for entering the world of machine learning and data science. This book shows you how to acquaint yourself with important elements of Machine Learning; understand the feature selection and feature engineering process; and assess performance and error trade-offs of Linear Regression, among others.
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