Machine learning books are a great resource to pump up your knowledge, and in our experience usually explain things better and deeper than online courses or MOOCs. Once you are comfortable with Python and with Data Analysis using its main libraries, it is time to enter the fantastic world of Machine Learning: Predictive models, applications, algorithms, and much more. There are a lot of books out there that try to teach you Machine Learning; here we have only listed some of the best ones. Before getting into more extensive coding ML books, we wanted to offer a book that is more related towards giving the readers an understanding of the main topics of Machine Learning and artificial intelligence in an elegant, clear, and concise manner. Although there is code and maths in the book, the goal of the 100 Page Machine Learning book by Andriy Burkov is to provide a common ground for any kind of person with an STEM background to meet the wonderful world of Data Science. It covers an amazing variety of topics but not in the depth that might be offered by other books (take into account it is only a little more than 100 pages), but it does so in a simple and clear manner, and it is useful for Machine Learning practitioners as well as for newcomers to the field.

Over the past decade, Machine Learning (ML) has increasingly been used to power a variety of products such as automated support systems, translation services, recommendation engines, fraud detection models and many, many more. Surprisingly, there aren't many resources available to teach engineers and scientists how to build such products. Many books and classes will teach how to train ML models, or how to build software projects, but very few blend both worlds to teach how to build practical applications that are powered by ML. This book goes through every step of this process, and aims to help you accomplish each of them by sharing a mix of methods, code examples, and advice from me and other experienced practitioners. We'll cover the practical skills required to design, build, and deploy ML powered applications.

All Indian Reprints of O'Reilly are printed in Grayscale Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-including experienced practitioners and novices alike will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success.