Hands-on Machine Learning

Objective

This course aims to teach students the fundamentals of Machine Learning using Python programming. Participants will gain proficiency by implementing and understanding various ML algorithms through hands-on coding exercises and projects with real-world datasets.

Syllabus

Introduction to Machine Learning, Types of learning algorithms, ML workflow, data manipulation with Pandas, exploratory data analysis, feature engineering, train/validation/test datasets, overfitting, underfitting, bias, variance, evaluation metrics. Supervised Learning: Linear regression, logistic regression, Naïve Bayes, K-Nearest Neighbors, Decision Trees, ensemble learning. Unsupervised Learning: Clustering, K-Means, dimensionality reduction. Case studies and end-to-end ML applications.