Introduction

This course provides a comprehensive foundation in supervised and unsupervised learning models, focusing on practical implementation and conceptual understanding for real-world applications.

Objectives

• Understand the key concepts of supervised and unsupervised learning.
• Distinguish between various algorithms and their use cases.
• Apply machine learning models to real datasets.
• Evaluate and interpret model performance.
• Gain hands-on experience using Python and popular ML libraries.

Course Outlines

Day 1: Introduction to Machine Learning and Data Preparation

• Overview of machine learning paradigms
• Difference between supervised and unsupervised learning
• Types of data and feature selection
• Data preprocessing techniques
• Introduction to Python ML libraries

Day 2: Fundamentals of Supervised Learning

• Regression and classification explained
• Linear regression and logistic regression
• Decision trees and random forests
• Model evaluation metrics (accuracy, precision, recall)
• Hands-on: Building and evaluating supervised models

Day 3: Advanced Supervised Learning Techniques

• Support Vector Machines (SVM)
• Ensemble methods (Bagging, Boosting)
• Hyperparameter tuning and model optimization
• Cross-validation strategies
• Case study: Solving a classification problem

Day 4: Unsupervised Learning Models

• Clustering techniques: K-means, Hierarchical clustering
• Dimensionality reduction: PCA, t-SNE
• Anomaly detection methods
• Evaluation of unsupervised models
• Hands-on: Applying clustering and dimensionality reduction

Day 5: Integrating and Deploying Learning Models

• Comparing supervised vs. unsupervised approaches
• Model selection and validation in practice
• Deploying ML models in production
• Ethics and best practices in machine learning
• Capstone project: End-to-end ML workflow