The field of data science is one of the most influential disciplines in today's technology-driven world. It encompasses various techniques, methodologies, and applications aimed at deriving insights from data to drive decision-making. Among the most powerful methodologies in data science is machine learning, which enables computers to learn from data without explicit programming. One of the most well-known applications of machine learning is in movie recommendation systems, which personalize content for users based on past preferences.

Harvard University, through edX, offers an 8-week online course titled “Data Science: Machine Learning”, which is part of the Professional Certificate in Data Science program. This course provides an introductory yet rigorous introduction to the foundations of machine learning. Participants will gain hands-on experience building a movie recommendation system while learning fundamental concepts such as cross-validation, regularization, and prediction algorithms. The course is self-paced, allowing learners to engage with the material according to their schedules.

Course Overview

Key Details

• Institution: Harvard T.H. Chan School of Public Health
• Platform: edX
• Duration: October 16, 2024 – June 18, 2025
• Length: 8 weeks
• Time Commitment: 2 – 4 hours per week
• Modality: Online, Self-paced
• Difficulty Level: Introductory
• Credit: Free Audit; Verified Certificate available for $149

What You Will Learn

This course provides a solid foundation in machine learning, equipping students with the knowledge and tools to apply machine learning techniques in real-world scenarios. Participants will explore:

• The Basics of Machine Learning – Understanding the core principles of how machines learn from data.
• Building Prediction Algorithms – Learning how algorithms use data to make predictions.
• Cross-Validation Techniques – Preventing overtraining by evaluating models effectively.
• Popular Machine Learning Algorithms – Exploring decision trees, random forests, support vector machines, and more.
• Developing a Movie Recommendation System – Applying learned concepts to create a practical application.
• Regularization and Its Importance – Understanding how regularization prevents overfitting and enhances model performance.

Course Structure and Curriculum

Week 1: Introduction to Machine Learning

• Overview of data science and its significance
• Introduction to supervised and unsupervised learning
• Applications of machine learning in real-world industries

Week 2: Data Processing and Training Data

• Understanding training datasets and their role in machine learning
• Preprocessing techniques for clean and usable data
• Feature selection and engineering

Week 3: Machine Learning Algorithms

• Overview of decision trees, random forests, and neural networks
• Supervised vs. unsupervised learning models
• Hands-on coding exercises

Week 4: Cross-Validation and Model Performance

• Concept of overfitting and underfitting
• Cross-validation techniques to optimize model accuracy
• Bias-variance tradeoff and its implications

Week 5: Recommendation Systems and Predictive Analytics

• Understanding collaborative filtering and content-based filtering
• Implementing recommendation engines using Python
• Case studies on real-world recommendation systems

Week 6: Regularization Techniques

• L1 (Lasso) and L2 (Ridge) regularization
• Preventing model overfitting through regularization
• Hands-on exercises in applying regularization techniques

Week 7: Advanced Topics in Machine Learning

• Principal component analysis (PCA) and dimensionality reduction
• Hyperparameter tuning for optimized performance
• Exploring deep learning concepts

Week 8: Final Project – Movie Recommendation System

• Applying knowledge gained to build a functioning recommendation system
• Showcasing project outcomes and refining models
• Preparing for future studies and career opportunities in data science

Hands-On Learning Experience

This course provides a dynamic and engaging learning experience through:

• Video Lectures – Delivered by leading Harvard instructors with practical demonstrations.
• Interactive Coding Exercises – Designed to reinforce theoretical concepts with real-world applications.
• Practical Assignments – Weekly problem sets ensuring comprehension and mastery.
• Final Capstone Project – A movie recommendation system to showcase learned skills.

Why Take This Course?

1. Learn from Harvard Experts

Harvard University’s faculty brings world-class expertise in data science and machine learning. This course provides an academically rigorous curriculum taught by experienced professionals in the field.

2. Gain Hands-On Practical Experience

This course is not just about theory—it emphasizes practical applications of machine learning techniques, allowing students to build a tangible movie recommendation system.

3. Flexible and Self-Paced Learning

With a self-paced structure, learners can complete the course at their own convenience, making it ideal for professionals, students, and lifelong learners.

Official Registration Link