Build machine learning models with a clear statistical understanding Complex statistics in machine learning worry a lot of developers. Developing an accurate understanding of statistics will help you build robust machine learning models that are optimized for a given problem statement. This book will teach you everything you need to perform the complex statistical computations required for machine learning. You will learn about the statistics behind supervised learning, unsupervised learning, and reinforcement learning. The book will then take you through real-world examples that discuss the statistical side of machine learning to familiarize you with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more. By the end of this machine learning book, you’ll be well-versed with the statistics required for machine learning and will be able to apply your new skills to tackle problems related to this technology. This book is for developers with little to no background in statistics who want to implement machine learning in their systems. Some knowledge of R programming or Python programming will be useful.
Maybe it's just my reading style, but I felt I only learned the overview of the topic rather than feeling like I've obtained some deeper understanding. It's still perfectly fine for getting and overview, and again this is perhaps my own deficient learning style, but I would have preferred if it studied a few techniques in much more depth, especially at the algorithmic level.
It started good. But gradually looses the momentum. Felt like you already need to know many things prior reading it. Less example and description is making it hard to understand.