Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.
Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code.
We are trying to implement a data lake on our network. During this work to launch Spark as a processing layer the book gave me a lot of ideas to do. I changed the examples to adapt to execute on cluster then I submitted on our cluster.
The book is ok to get some level of overview of some machine learning practices. It doesn't really explain much (mathematical) background of what's going on, so if you don't have that background already you will have a harder time in places. The examples feel a little random, but on the other hand they cover a fairly broad spectrum of application areas which is good. Probably the most important issue with the book nowadays is that it was written in 2017, and in the fast evolving landscape of ML, especially deep learning model architecture, it often feels woefully out of touch with topics of interest today. Also, a lot of the Jupyter notebooks don't run anymore since the software APIs of the current versions of tools used in them have made changes that are not backed compatible. It would have helped if the authors had provided a docker image (or similar) with frozen versions of all the Python libraries that the Jupyter notebooks depend on.
This book presents random examples of machine learning exercises. Unfortunately this is no introduction to machine learning, no guide on ML ecosystem in python