Introduction to Machine Learning

Module 1

Foundations of Machine Learning

Explore the fundamental concepts of machine learning at an intermediate level, covering paradigms, evaluation metrics, and the bias-variance tradeoff.

Module 2

Linear and Logistic Regression Techniques

Dive into regularized regression models and robust classification techniques using logistic regression and maximum likelihood estimation.

Module 3

Tree-Based Models and Ensemble Methods

Master decision trees and discover how ensemble methods like Random Forests and Gradient Boosting can drastically improve predictive performance.

Module 4

Support Vector Machines and Kernel Methods

Understand the mathematics and implementation of Support Vector Machines, including the kernel trick for non-linear decision boundaries.

Module 5

Unsupervised Learning and Dimensionality Reduction

Learn how to extract patterns from unlabelled data using clustering algorithms and reduce feature space with PCA and manifold learning.

Module 6

Introduction to Neural Networks and Deep Learning

Transition into deep learning by exploring perceptrons, multi-layer architectures, backpropagation, and essential optimization techniques.

Module 7

Final Assessment

A comprehensive evaluation covering all topics taught in the Introduction to Machine Learning course to validate your intermediate-level proficiency.