Fundamentals of Programming and Machine Learning
Build a strong foundation in Python, data analytics, and machine learning by learning how code, data, and machine learning models actually work.
Course Information
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About this Course
Build a strong foundation in programming and data science so you can confidently engage with today’s AI-driven world. In this course, you’ll develop a clear understanding of how code works, learn how data is collected and prepared for analysis, and build intuition for how machine learning models find patterns and make predictions. You’ll explore both supervised and unsupervised learning, gaining the skills to interpret results, evaluate performance, and understand how models are used in real-world contexts.
Fundamentals of programming and machine learning is one of the five courses in Universal AI, a self-paced program designed to help you go from beginner to AI authority—no coding or technical skills required.
What you'll learn
- Read, write, and fix Python code while building a clear understanding of how programs work
- Work with real data by opening CSV files, organizing information with tools like dictionaries, NumPy, and Pandas, and creating simple charts
- Build clean, reusable code using basic concepts like functions, classes, and object-oriented programming
- Learn the full process of data analysis, from cleaning data to making charts and explaining what the results mean
- Use machine learning methods like predicting numbers (regression), sorting into groups (classification), and decision trees, and check how well they work
- Explore clustering to find patterns in data and learn how to explain those patterns in a clear way
Modules
This course has 5 modules
Meet your instructors
Dimitris Bertsimas
Vice Provost for Open Learning, MIT Open Learning
Dimitris Bertsimas is the Vice Provost for Open Learning at MIT, the Associate Dean of Business Analytics, the Boeing Leaders for Global Operations Professor of Management, and a Professor of Operations Research at MIT Sloan School of Management. At MIT Open Learning, he oversees Open Learning’s product offerings, new initiatives, infrastructure, finances, and operations.
