Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
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 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 learnt 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.
This book is easy to read and understand. For someone like me, who is not an expert in Python or Machine Learning, I was still able to follow the concepts easily and understand the examples. I was able to learn quite a few things from this book. I really recommend this book to people who are interested and keen to learn more about Machine Learning algorithms.
For a machine learning noob like me, it was pleasing to see that the book did not dive straight into the nitty-gritty of machine learning algorithms: it first established the raison d’être for machine learning and cohesively captured the whole gamut of developing a machine learning model. This helped me quite a bit to understand the bigger picture later on in the book where it demonstrated the practical use of various machine learning algorithms. I'll happily recommend this book to anyone interested in scikit-learn, and machine learning in general too.
This book is information rich with practical examples. I whom never read or touched this area was suprised to learn the weight that data analysis had on machine learning. Yes, this book also teaches you about data analysis. Throughout the chapters you learn what not to do when building machine learning and deep learning models. The author teaches you what not to do by analysing the data at hand and improving the models upon that knowledge. The book is very information rich and can easily be reread from chapter to chapter. There are some things to keep in mind, this book is not for python beginners and i urge you to know some of the basics from the pandas and matplotlib modules. In other words this book is strongly recommended.