Machine Learning (ML) enables systems to identify patterns and make predictions without explicit programming. This course introduces the principles of ML, focusing on supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning. Through practical examples, you’ll explore how algorithms like decision trees, k-means clustering, and neural networks operate. The course also emphasizes model evaluation and improvement, teaching techniques to measure accuracy and mitigate overfitting. By combining theoretical knowledge with practical exercises, you’ll develop the skills to create adaptable ML systems capable of solving diverse problems in domains like healthcare, finance, and automation.

Mathematics forms the foundation of AI and ML algorithms. This course demystifies key mathematical concepts, making it easier to build robust AI models.

Course Breakdown:

• Linear Algebra: Vectors, matrices, and transformations.
• Probability and Statistics: Bayes’ theorem, distributions, and hypothesis testing.
• Calculus for Optimization: Gradient descent and backpropagation.