intermediateFree
Introduction to Machine Learning
by Leslie Kaelbling & Tomás Lozano-Pérez · MIT OpenCourseWare
4.6(1,200 reviews)
80K+ enrolled14 weeksUpdated 2024-01
What You'll Learn
Understand the fundamentals and key concepts of machine learning
Apply machine learning techniques to solve real-world problems
Understand the fundamentals and key concepts of neural networks
Apply neural networks techniques to solve real-world problems
Understand the fundamentals and key concepts of classification
Apply classification techniques to solve real-world problems
About This Course
MIT course covering perceptrons, feature representation, margin maximization, regression, and neural networks foundations.
Curriculum
Module 1: Machine learning & Neural networks6 lessons
- Introduction to machine learning
- Machine learning in Practice
- Hands-on Exercise: Machine learning
- Introduction to neural networks
- Neural networks in Practice
- Hands-on Exercise: Neural networks
Module 2: Classification & Regression6 lessons
- Introduction to classification
- Classification in Practice
- Hands-on Exercise: Classification
- Introduction to regression
- Regression in Practice
- Hands-on Exercise: Regression
Module 3: Feature engineering3 lessons
- Introduction to feature engineering
- Feature engineering in Practice
- Hands-on Exercise: Feature engineering
Instructor
Leslie Kaelbling & Tomás Lozano-Pérez
Instructor at MIT OpenCourseWare
Pros & Cons
Pros
- Completely free to access
- Taught by Leslie Kaelbling & Tomás Lozano-Pérez
- Well-structured curriculum
Cons
- No certificate provided
- Self-paced requires discipline
Free
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