Diploma In AI & Generative AI Models

An AI (Artificial Intelligence) course typically covers a broad range of topics, depending on the level of the course (beginner, intermediate, or advanced). Here's a general overview of what an AI course might include: 1. Introduction to AI History and evolution of AI. Applications of AI in real-world scenarios (e.g., healthcare, finance, robotics, etc.). Ethics and societal implications of AI (e.g., privacy, bias, AI in decision-making). 2. Mathematics for AI Linear Algebra: Vectors, matrices, and their applications in machine learning. Calculus: Differentiation, optimization, and gradient descent. Probability and Statistics: Basic concepts for understanding data distributions, Bayesian probability, and statistical inference. Optimization: Techniques like gradient descent and optimization methods used in machine learning algorithms. 3. Machine Learning Basics Supervised Learning: Understanding labeled data and models such as Linear Regression, Decision Trees, and Neural Networks. Unsupervised Learning: Techniques like Clustering (K-means, Hierarchical Clustering) and Dimensionality Reduction (PCA). Reinforcement Learning: Basic concepts of agents, environments, rewards, and policy optimization.