Machine Learning Specialization Course Syllabus

This specialization offers a practical, case-driven approach to mastering machine learning with Python, structured across five core modules and a final project. Through real-world applications like housing price prediction, sentiment analysis, and recommendation systems, learners gain hands-on experience building, evaluating, and deploying models. The course spans approximately 28–46 weeks of part-time study, with each module combining theory, coding exercises, and project work to build a professional portfolio. Estimated total time commitment: 120–160 hours.

Module 1: Machine Learning Foundations: A Case Study Approach

Estimated time: 20 hours

• Introduction to machine learning through real business problems
• Matching use cases to supervised and unsupervised learning
• Applying black-box ML models in practice
• Evaluating models using error metrics and performance trade-offs

Module 2: Machine Learning: Regression

Estimated time: 30 hours

• Building linear regression models for continuous prediction
• Handling large feature sets and model complexity
• Implementing regularization techniques (Ridge, LASSO)
• Optimizing regression models using Python

Module 3: Machine Learning: Classification

Estimated time: 40 hours

• Applying logistic regression for binary classification
• Building and tuning decision trees
• Using boosting methods to improve accuracy
• Addressing class imbalance and overfitting in models

Module 4: Machine Learning: Clustering & Retrieval

Estimated time: 50 hours

• Implementing k-means and hierarchical clustering algorithms
• Building document and image retrieval systems
• Evaluating clustering results with appropriate metrics
• Developing content-based recommendation systems

Module 5: Model Evaluation and Deployment

Estimated time: 20 hours

• Assessing model performance using cross-validation
• Tuning hyperparameters for optimal results
• Deploying models in real-world scenarios
• Understanding trade-offs between accuracy, speed, and scalability

Module 6: Final Project

Estimated time: 40 hours

• Build a complete ML pipeline for a real-world problem
• Apply regression, classification, or retrieval techniques
• Deliver a portfolio-ready project with documentation and evaluation

Prerequisites

• Basic understanding of Python programming
• Familiarity with fundamental math concepts (algebra, statistics)
• Some prior coding experience recommended

What You'll Be Able to Do After

• Predict continuous outcomes using regression models
• Classify data using logistic regression and tree-based methods
• Group data using clustering and build retrieval systems
• Evaluate and tune ML models effectively
• Apply ML to real business problems and build a professional portfolio