April 2018
Intermediate to advanced
456 pages
11h 47m
English
The principles of machine learning are largely dependent on effectively handling the uncertainty in data and predicting the outcome based on data in hand. All the learning processes we will be discussing in later chapters of this book expects the readers to understand the foundational rules of probability, the concept of random and continuous variables, distribution, and sampling principles and few basic principles such as central limit theorem, hypothesis testing, and Monte Carlo approximations. As these rules, theorems, and principles form the basis of learning principles, we will be discussing those in this chapter with examples and illustrations.
As we ...