11. Introduction to Machine Learning
by MIT OpenCourseWare · 1 chapter · 52 min
- machine learning introduction
- MIT 6.0002 machine learning
- supervised vs unsupervised learning
- k nearest neighbors explained
- clustering algorithm tutorial
- feature engineering basics
- Minkowski distance Manhattan Euclidean
- confusion matrix accuracy sensitivity specificity
Free with Google or your email. The videos play from YouTube, credited to their creator.
Chapters
- 0111. Introduction to Machine Learning51:31
About this course
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
More courses like this
Computer Vision Bootcamp | Full 12 hour series | OpenCV, RCNN, UNet, YOLO and Roboflow
SQL Tutorial for Beginners (Complete Course using MySQL)
2026 FREE Data Analyst Bootcamp [24 Hours+] for FREE | SQL, Excel, Python, Power BI, GitHub, AWS
Barbara O'Neill Part 1. DNA and The True Cause of Disease
Claude – Full Course 2026 (How to use Claude AI for beginners)
