Develop a mathematical intuition for how machine learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems. - Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. For each category of algorithm, you’ll go from math-first principles to a hands-on implementation in Python, exploring dozens of examples from across all the fields of machine learning. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics. Purchase of the print book includes a free e - Book in PDF, Kindle, and e - Pub formats from Manning Publications. - Biographical Note - Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. He is a former Ph - D student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.