The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published today and will be soon available through all channels.
From the back cover:
Machine learning has gained tremendous popularity for its powerful and fast predictions through large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate sufficient insight.
This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across supervised, semi-supervised, and reinforcement learning areas. Once the core concepts of an algorithm have been exposed, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and Gaussian mixture.
By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Link to the publisher page: https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-algorithms-second-edition
Source code repository: https://github.com/PacktPublishing/Machine-Learning-Algorithms-Second-Edition
My latest machine learning book has been published and will be available during the last week of July. From the back cover: In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science.