Introduction to Machine Learning

Week 1: Basics of Machine Learning

 • Module 1: Introduction to Machine Learning

 ◦ What is Machine Learning?

 ◦ History and Evolution

 ◦ Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)

 • Module 2: Data Preprocessing

 ◦ Data Cleaning and Preparation

 ◦ Feature Engineering

 ◦ Data Splitting (Training, Validation, Test Sets)

Week 2: Supervised Learning

 • Module 3: Regression

 ◦ Linear Regression

 ◦ Polynomial Regression

 ◦ Evaluation Metrics (MSE, RMSE, R²)

 • Module 4: Classification

 ◦ Logistic Regression

 ◦ k-Nearest Neighbors (k-NN)

 ◦ Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)

Week 3: Unsupervised Learning and Model Evaluation

 • Module 5: Clustering

 ◦ k-Means Clustering

 ◦ Hierarchical Clustering

 ◦ Evaluation Metrics (Silhouette Score, Elbow Method)

 • Module 6: Dimensionality Reduction

 ◦ Principal Component Analysis (PCA)

 ◦ t-Distributed Stochastic Neighbor Embedding (t-SNE)

 ◦ Applications and Use Cases

Week 4: Advanced Topics and Applications

 • Module 7: Neural Networks and Deep Learning

 ◦ Introduction to Neural Networks

 ◦ Basics of Deep Learning

 ◦ Applications in Image and Text Processing

 • Module 8: Practical Applications and Case Studies

 ◦ Building and Evaluating Models

 ◦ Real-world Case Studies