In almost any Python program code in Machine Learning, you see the "Numpy" library being used! Why? Doing Machine Learning is impossible without Linear Algebra and Linear Algebra is formed by vectors, matrices, etc. Well, Numpy is one of the best scientific computing packages for Linear Algebra! So that was the short answer to the "why" question earlier!
This course is dedicated to Numpy, one of the most important scientific computing libraries for Linear Algebra and henceforth, Machine Learning!
This is a short video course designed for beginners. It assumes the practitioner does not have any prior knowledge about Python programming. The goal is to provide short, concise tutorials dedicated to describe the Python syntax with examples.
In this course, the probability theory is described.
The probability theory is of great importance in many different branches of science. Let's focus on Artificial Intelligence empowered by Machine Learning. The question is, "how knowing probability is going to help us in Artificial Intelligence?" In AI applications, we aim to design an intelligent machine to do the task.
First, the model should get a sense of the environment via modeling. As there is ambiguity regarding the possible outcomes, the model works based on estimation and approximation, which are done via probability.
Second, as the machine tries to learn from the data (environment), it must reason about the process of learning and decision making. Such reasoning is not possible without considering all possible states, scenarios, and their likelihood.
Third, to measure and assess the machine capabilities, we must utilize probability theory as well.
Here, you will learn what is necessary for Machine Learning from probability theory.
- Learn the core concepts of probability theory.
- Becoming familiar with mostly used probability concepts and distributions in Machine Learning
- Understand the mathematical foundation of probability theory and their applications