Course Description

"Machine Learning with Python: From Linear Models to Deep Learning" is an advanced-level computer science course offered by MITx as part of their MicroMasters Program in Statistics and Data Science. This comprehensive course delves into the fascinating world of machine learning, exploring its principles, algorithms, and real-world applications. Students will gain hands-on experience implementing various machine learning techniques using Python, enabling them to turn training data into effective automated predictions.

What Students Will Learn

• Understand the fundamental principles behind various machine learning problems, including classification, regression, clustering, and reinforcement learning
• Implement and analyze a wide range of models, such as linear models, kernel machines, neural networks, and graphical models
• Develop the ability to choose suitable models for different applications
• Learn to organize and implement machine learning projects, covering aspects like training, validation, parameter tuning, and feature engineering

Prerequisites

• Proficiency in Python programming (equivalent to MITx's 6.00.1x course)
• Knowledge of probability theory (equivalent to MITx's 6.431x course)
• College-level single and multi-variable calculus
• Understanding of vectors and matrices

Course Coverage

• Representation, over-fitting, regularization, and generalization
• VC dimension concept
• Clustering and classification techniques
• Recommender systems
• Probabilistic modeling
• Reinforcement learning
• On-line algorithms
• Support vector machines
• Neural networks and deep learning
• Natural Language Processing applications

Who This Course Is For

• Advanced computer science students
• Data scientists and aspiring data professionals
• Engineers and scientists looking to incorporate machine learning into their work
• Professionals in fields such as search engines, recommender systems, advertising, and finance
• Anyone seeking to gain a deep understanding of machine learning principles and applications

Real-World Applications

• Developing predictive models for business decision-making
• Creating recommendation systems for e-commerce platforms
• Implementing fraud detection systems in financial institutions
• Designing autonomous systems and robotics
• Enhancing natural language processing applications
• Improving image and speech recognition technologies
• Optimizing supply chain and logistics operations
• Advancing medical diagnosis and treatment planning
• Enhancing cybersecurity through anomaly detection

Syllabus

Lectures:

1. Introduction
2. Linear classifiers, separability, perceptron algorithm
3. Maximum margin hyperplane, loss, regularization
4. Stochastic gradient descent, over-fitting, generalization
5. Linear regression
6. Recommender problems, collaborative filtering
7. Non-linear classification, kernels
8. Learning features, Neural networks
9. Deep learning, back propagation
10. Recurrent neural networks
11. Generalization, complexity, VC-dimension
12. Unsupervised learning: clustering
13. Generative models, mixtures
14. Mixtures and the EM algorithm
15. Learning to control: Reinforcement learning
16. Reinforcement learning continued
17. Applications: Natural Language Processing

Projects:

1. Automatic Review Analyzer
2. Digit Recognition with Neural Networks
3. Reinforcement Learning

This course offers a unique opportunity to gain expertise in machine learning from one of the world's leading institutions, providing students with the knowledge and skills to become effective practitioners in the rapidly growing field of data science.