Machine Learning Specialization review
DeepLearning.AI and Stanford · deeplearning-ai
Our Andrew Ng Machine Learning Specialization review: it is still the clearest foundations course for classic machine learning, taught by Andrew Ng and 3 other instructors across 3 courses and 95 hours. Coursera's FAQ says you cannot take it for free. You pay with a subscription or financial aid, unless a no-certificate option appears when you enrol.
What does it take?
| Difficulty | 3 out of 5 |
|---|---|
| Hours | About 95 hours |
| Format | Video, Code notebooks |
| Maths needed | School-level maths |
| Coding needed | Yes, you will write code |
| Hardware needed | A browser is enough |
| Certificate | Paid certificate: Included in a Coursera subscription; shown when you enrol |
| Free to audit | No |
| Pace | Self-paced |
| Last updated by the provider | Not checked yet |
| Teachers | Andrew Ng and 3 other instructors (DeepLearning.AI and Stanford) |
| Length | 3 courses, 95 hours (33, 34 and 28 hours) |
| Level | Beginner, per Coursera |
| Prerequisite | Basic coding and high school maths |
| Cost | Coursera says you cannot take it for free; subscription 49 US dollars a month, or financial aid |
| No-certificate option | Some courses offer it; look for it when you enrol |
| Certificate | Per course if you pay and finish the programming assignments |
| Certificate eligibility | 180 days, then you must buy again |
Who should take it, and who should skip it?
Take it if you are
- Learners with basic coding and school maths who want the classic foundations of machine learning
- Anyone who wants Andrew Ng's updated version of the course taken by over 4.8 million learners
- People who want unsupervised learning, recommender systems and tree-based models, not only neural networks
Skip it if you are
- Anyone who needs a course that costs nothing: take Kaggle Intro to Machine Learning or Google's Machine Learning Crash Course
- Learners who cannot commit to 95 hours across 3 courses
- Anyone who has not written for loops, functions and if/else statements yet
What will you be able to do after it?
- Build machine learning models in Python with NumPy and scikit-learn
- Train linear and logistic regression models
- Build a neural network with TensorFlow for multi-class classification
- Use decision trees, random forests and boosted trees, plus clustering and anomaly detection
- Build recommender systems and a deep reinforcement learning model
What's in each module?
| Module | What it covers |
|---|---|
| Course 1, week 1: Introduction to machine learning | About 7 hours. Part of Supervised Machine Learning: Regression and Classification, which takes 33 hours. |
| Course 1, week 2: Regression with multiple input variables | About 10 hours of regression with more than 1 input variable. |
| Course 1, week 3: Classification | About 16 hours of classification, the longest module in the specialization. |
| Course 2, week 1: Neural networks | About 7 hours. Part of Advanced Learning Algorithms, which takes 34 hours. |
| Course 2, week 2: Neural network training | About 10.5 hours on training neural networks. |
| Course 2, week 3: Advice for applying machine learning | About 8.5 hours of practical advice, updated with best practice from the last decade. |
| Course 2, week 4: Decision trees | About 8 hours on decision trees, random forests and boosted trees. |
| Course 3, week 1: Unsupervised learning | About 9 hours. Part of Unsupervised Learning, Recommenders, Reinforcement Learning, which takes 28 hours. |
| Course 3, week 2: Recommender systems | About 10.5 hours, including collaborative filtering and a content-based deep learning method. |
| Course 3, week 3: Reinforcement learning | About 8.5 hours, ending in a deep reinforcement learning model. |
What we liked, and what we didn't
What we liked
- Andrew Ng teaches it, and Coursera describes it as an updated version of his original course from 2012
- It covers regression, neural networks, decision trees, clustering, recommenders and reinforcement learning in 3 courses
- Ungraded code notebooks with sample code and interactive graphs come before each graded programming assignment
- The section on applying machine learning was updated with best practice from the last decade
- Coursera says it needs only basic coding and high school maths, and explains other maths in the course
What we didn't
- Coursera's FAQ says you cannot take it for free
- A no-certificate option may or may not appear when you enrol, so you cannot plan around it
- It is a big commitment: 33, 34 and 28 hours for the 3 courses, 95 hours in all
- The certificate needs a paid subscription and finished programming assignments, and eligibility lasts 180 days
- It carries no university credit
What's the catch?
It is not reliably free. Coursera's FAQ says you cannot take it for free, and some courses offer a Full Course, No Certificate option that gives materials and grades but no certificate, so look for it when you enrol. Otherwise you need a subscription (49 US dollars a month in Coursera's FAQ, and the price you see may differ) or financial aid. At 95 hours, it is also a big commitment.
Is it still current?
Coursera's specialization page and FAQ were checked on 5 October 2026. The course describes itself as an updated version of Andrew Ng's original course, and its section on applying machine learning was refreshed with best practice from the last decade.
What should you take before it?
- Kaggle Learn: Python
Kaggle's 7-lesson Python course takes about 5 hours, costs nothing and pairs every lesson with an exercise notebook. Kaggle gives a free completion certificate.
What should you take after it?
- Practical Deep Learning for Coders
Practical Deep Learning for Coders has 9 lessons of about 90 minutes, costs nothing and has no certificate. You deploy your own model by the end of lesson 2.
- Hugging Face LLM Course
Hugging Face LLM Course has 12 chapters of 6 to 8 hours each, costs nothing and has no certificate. You learn LLMs with the Transformers library.
How we rated it: 8.4 out of 10
| Criterion | Score out of 10 |
|---|---|
| Clarity | 9 |
| Hands-on practice | 8 |
| Up to date | 8 |
| Depth | 8 |
| Truly free | 4 |
Questions people ask
Is the Machine Learning Specialization free?
No. Coursera's FAQ says you cannot take it for free. Some courses offer a Full Course, No Certificate option with materials and grades, so look for it when you enrol. Otherwise you need a subscription or financial aid.
How much does the Machine Learning Specialization cost?
Coursera's FAQ says a subscription costs 49 US dollars a month, and the price you see may differ by country. Financial aid is available, and eligible learners may get a free trial.
How long does the Machine Learning Specialization take?
It takes 95 hours, which is 33 plus 34 plus 28 hours across the 3 courses. Coursera also says 2 months at 10 hours a week.
What do you need before the Machine Learning Specialization?
Coursera lists basic coding (for loops, functions and if/else statements) and high school maths (arithmetic and algebra). Other maths concepts are explained in the course.
Do you get a certificate for the Machine Learning Specialization?
Yes, if you pay and complete the programming assignments. You get a certificate for each course, and eligibility lasts 180 days. Finishing all 3 courses while subscribed adds a specialization certificate, and there is no university credit.
How is it different from the Deep Learning Specialization?
Coursera says it covers unsupervised learning, recommender systems, tree-based models and other classic algorithms that do not rely on neural networks.
So should you take it?
Take the Machine Learning Specialization if you can code a little and want the clearest foundations course for classic machine learning. It scores 8.4: clarity is 9, hands-on, up to date and depth are 8, and truly free is 4, because Coursera's FAQ says you cannot take it for free and a no-certificate option may or may not show when you enrol. If cost matters, Kaggle Intro to Machine Learning and Google's Machine Learning Crash Course are the truly free alternatives.
Where to go next
- Become an ML engineer
A learning path that includes Machine Learning Specialization.
- DeepLearning.AI: our platform review
What else it offers, and what costs money.
- Kaggle Intro to Machine Learning
Kaggle's 7-lesson first machine learning course takes about 3 hours, costs nothing and ends with a competition. Kaggle gives a free completion certificate.
- Machine Learning Crash Course
Google's Machine Learning Crash Course has 12 modules and takes about 13.5 hours by Google's own timings. It is free, with quiz badges and no certificate.
- AI for Beginners
A 24-lesson open curriculum on GitHub covering classic AI, ML and a little deep learning.
Last checked : teachers, level, prerequisite, course hours, modules, cost, no-certificate option, certificate and what you build.
Not sure this is the one?
Answer two questions and we'll point you to the course that fits where you are today.