Machine Learning Specialization
Andrew Ng’s classic machine learning course, rebuilt in Python with NumPy, scikit-learn, and TensorFlow. It goes from linear regression to neural networks, recommenders, and reinforcement learning, and expects basic coding and high-school math.
Go to the course on DeepLearning.AI- Provider
- DeepLearning.AI and Stanford Online (also on Coursera)
- Price
- Free to start, paid extras
- Certificate
- Paid certificateNeeds DeepLearning.AI Pro or a paid Coursera enrollment, plus the required assessments.
- Level
- Beginner
- Time
- About 95 hours of material (DeepLearning.AI); Coursera suggests 2 months at 10 hours a week
- Format
- 3-course specialization: video lessons, ungraded code notebooks, and graded Python programming assignments
- Checked
- 2026-11-05
DeepLearning.AI says you can audit for free without a certificate; the certificate needs Pro ($25/mo billed annually or $30/mo monthly when checked). On Coursera, the FAQ says it cannot be taken for free ($49/month subscription); financial aid available there.
01
What you will learn.
- 01Supervised learning
- 02Neural networks
- 03Decision trees
- 04Unsupervised learning
- 05Recommender systems
- 06Reinforcement learning
- 07Python
More inMachine learning courses
02
Before you enroll.
From the course page, checked 2026-11-05.
- Who it is for
- Beginners breaking into AI, and early-career software engineers or data analysts moving into ML.
- You need
- Basic coding: for loops, functions, if/else statements
- High-school math: arithmetic and algebra
- What you build
- A TensorFlow neural network for multi-class classification
- Recommender systems with collaborative filtering and content-based methods
- A deep reinforcement learning model
- Good to know
- All 42 graded assignments are marked Pro on DeepLearning.AI
- Coursera certificate eligibility lasts 180 days, then you must buy again
03
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