The “Foundational Components of AI” course delves into the core elements of artificial intelligence, covering input sources like data, models, and sensor data; processing techniques involving sophisticated algorithms; and output generation, including predictions, pattern recognition, reinforcement learning, and the creation of new content.

This “Intro to AI” course explores the definition, history, and evolution of artificial intelligence, its importance in modern society, key branches like machine learning and robotics, and addresses potential threats and risks, fostering a comprehensive understanding of AI’s impact and future.

The “Intro to Machine Learning” course covers how machines learn, focusing on the model brain, the test and train split methodology, and essential techniques for validation and model evaluation, providing a comprehensive understanding of the machine learning process and its practical applications.

“Machine Learning Algorithms” explores supervised, unsupervised, and reinforcement learning algorithms, detailing their purposes and optimal use cases. This course provides a clear understanding of the business applications of these algorithms, when and how to apply each type of algorithm to solve various real-world problems.