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Andrew Ng’s Machine Learning course

Free

Learn supervised ML: regression and classification from Andrew Ng

FreeFree tier
Type
Open Source
Company
DeepLearning.AI
Visit Website GitHub (40 stars)

About Andrew Ng’s Machine Learning course

Andrew Ng's 'Supervised Machine Learning: Regression and Classification' is the first course of the Machine Learning Specialization, offered on Coursera in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly course teaches the fundamentals of supervised learning, including linear regression and logistic regression, using Python libraries such as NumPy and scikit-learn. Learners build and train machine learning models for prediction and binary classification tasks through hands-on projects and assignments. The course is part of a three-course specialization that covers modern machine learning techniques, including neural networks, decision trees, clustering, and best practices for AI innovation. Taught by AI visionary Andrew Ng, this updated version of his pioneering course has been taken by over 4.8 million learners and is available in multiple languages with a flexible 3-week schedule.

Key Features

Build machine learning models in Python using NumPy and scikit-learn
Build and train supervised models for prediction and binary classification
Includes linear regression and logistic regression
Part of a 3-course Machine Learning Specialization
Taught by Andrew Ng, AI visionary from Stanford and DeepLearning.AI
Hands-on projects and assignments
Shareable certificate upon completion
Available in 33 languages with French (AI Dubbing)
Flexible schedule: 3 weeks at 10 hours per week
Rated 4.9 out of 5 by over 32,000 reviewers

Pros & Cons

Pros
  • Free to enroll with high-quality content from a top instructor
  • Hands-on coding projects using real-world libraries
  • Beginner-friendly with no prior ML experience required
  • Part of a comprehensive specialization covering modern ML
  • Flexible schedule allows self-paced learning
  • Large community of over 1.2 million enrolled learners
Cons
  • Requires basic programming knowledge in Python (not taught)
  • Only covers supervised learning; other topics in later courses
  • Time commitment of 30 hours total may be heavy for some learners
  • No direct instructor interaction; discussion forums available

Best For

Building a foundation in machine learning for beginnersLearning supervised learning techniques for prediction and classificationAcquiring practical skills in Python and scikit-learn for ML projectsPreparing for advanced ML topics or a career in AIGaining a shareable credential to boost LinkedIn profile

FAQ

Is this course free?
Yes, you can enroll for free and audit the course. A certificate is available for a fee.
What will I learn in this course?
You will learn to build and train supervised machine learning models in Python using NumPy and scikit-learn, covering linear regression and logistic regression for prediction and binary classification.
What are the prerequisites?
The course is beginner-level, but basic knowledge of Python programming and high school math is recommended.
How long does the course take?
The course is designed for 3 weeks at 10 hours per week, totaling about 30 hours.
Who is the instructor?
The course is taught by Andrew Ng, an AI visionary who has led research at Stanford, Google Brain, Baidu, and Landing.AI.