Lec 01 Overview of Machine Learning and Deep Learning

A chronological review of AI development, starting from the first artificial neuron in 1943, through the Turing Test, the golden ages of AI, the impact of hardware advancements like GPUs, and the rise of modern transformer models.

Distinguishes between traditional rule-based programming and machine learning, which allows computers to learn patterns from data, using medical diagnosis as a case study.

Explains the three main categories of machine learning: supervised learning (classification and regression using labeled data), unsupervised learning (clustering), and reinforcement learning (learning from environmental feedback).

Discusses real-world applications of machine learning, including weather forecasting, medical diagnosis, customer recommendation systems, and robotics.

Defines deep learning as a subset of machine learning based on artificial neural networks and explains how it differs from traditional machine learning, specifically regarding manual vs. automatic feature extraction.