Machine Learning Specialization
by DeepLearning.AI & Stanford (Andrew Ng)
The definitive beginner on-ramp to machine learning, taught by Andrew Ng.
Overview
This is the modern remake of the course that introduced millions to machine learning. Across three courses you cover linear and logistic regression, neural networks, decision trees, clustering, anomaly detection, recommender systems, and an intro to reinforcement learning — all with Python and scikit-learn/TensorFlow labs. Andrew Ng's teaching is famously clear, making this the default first ML course to recommend. Video content is free to audit; the certificate requires a subscription.
At a Glance
- Topic
- ML
- Level
- Beginner
- Format
- Course
- Cost
- Freemium
- Duration
- ~2 months, ~9 hrs/week
- Provider
- DeepLearning.AI & Stanford (Andrew Ng)
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Supervised learning: regression and classification
- ✓Neural networks and decision trees
- ✓Unsupervised learning, recommenders, and RL basics
- ✓Practical ML: bias/variance, evaluation, and tuning
Highlights
- •Taught by Andrew Ng
- •Refreshed, Python-based curriculum
- •Audit for free; optional paid certificate
Who It’s For
Best For
- ✓Anyone starting machine learning from scratch
Prerequisites
- •High-school math
- •Basic Python helpful
FAQ
What is Machine Learning Specialization?
Andrew Ng's updated three-course specialization covering supervised learning, advanced algorithms, and unsupervised/recommender/reinforcement learning in Python.
Is Machine Learning Specialization free?
Machine Learning Specialization offers free content, with paid options for certificates or premium features.
What level is Machine Learning Specialization for?
Machine Learning Specialization is aimed at a beginner audience. Recommended background: High-school math, Basic Python helpful.
How long does Machine Learning Specialization take?
Expect roughly ~2 months, ~9 hrs/week. Most learners work through it at their own pace.
What will I learn from Machine Learning Specialization?
You'll learn: Supervised learning: regression and classification; Neural networks and decision trees; Unsupervised learning, recommenders, and RL basics; Practical ML: bias/variance, evaluation, and tuning.