Course description

The Art of Probabilities in Machine Learning

Join us for an insightful session on "The Art of Probabilities in Machine Learning,"  where we will explore the essential role of probability theory in enhancing machine learning models. This session is structured into three comprehensive parts to provide a deep understanding of probability concepts and their practical applications.

Part 1: Introduction to Probability Concepts

We begin with the fundamentals of probability theory, covering basic principles and essential definitions. Dive into conditional probability and Bayes' Theorem, learning how to apply these concepts to assess and manage uncertainty in machine learning models.

Part 2: Probability Distributions and Their Applications

Discover the difference between discrete and continuous probability distributions, and understand their significance in data analysis. We will explore various probability distributions, such as the Normal distribution, Binomial distribution, and others, discussing their applications in different machine learning contexts.

What will i learn?

• Basic understanding of Probabilities in ML.