DeepLearning.AI · on Coursera

Machine Learning Specialization

4.9(39,000) on Coursera·820K enrolled
Intermediate 86 hours English SpecializationFREE
SkillsMachine learningSupervised learningUnsupervised learningRegressionClassificationPythonscikit-learn

Is this course right for you?

Our take
A widely recommended starting point for machine learning, and it is free to audit. Andrew Ng is an exceptional teacher, and if you are new to ML this is the place to begin.

Good for: beginners to machine learning who want a well-regarded starting point.

Skip if: you already know the ML basics, or you want deep mathematical theory.

The updated three-course version is rebuilt in Python and covers the core ground — supervised learning, neural networks, then unsupervised learning, recommenders and a taste of reinforcement learning — with the maths kept approachable. You need comfortable high-school algebra, not a degree.

It is foundations, not job-ready engineering: it will not teach you to build production systems, and it stops short of deep-learning depth. Follow it with hands-on projects or the Deep Learning Specialization. The certificate needs a paid subscription; the free audit covers the learning (as of 2026).

Comparison · LBS

Compare alternatives for Machine Learning Specialization

Same topic, different options. We surface the trade-offs others hide so you can pick the course that actually fits your time, budget, and goals.
Coursera4.9(39,000)
Machine Learning Specialization
Price
Free
Audit free · Certificate on subscription
Duration
86 hrs
Level
Intermediate
Certificate
Specialization
Coursera4.8(14,000)
Google Advanced Data Analytics Professional Certificate
Price
Free
Audit free · Certificate on subscription
Duration
197 hrs
Level
Intermediate
Certificate
Professional Certificate
Google4.7(8,500)
Machine Learning Crash Course
Price
Free
Completely free, self-paced
Duration
15 hrs
Level
Beginner
Certificate
DataCamp4.7(14,800)
Data Scientist with Python Career Track
Price
Paid
Subscription, billed monthly
Duration
88 hrs
Level
Beginner
Certificate
Specialization
Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.

About this course

The Machine Learning Specialization is Andrew Ng's updated three-course introduction to machine learning, rebuilt in Python. Most learners finish in about two to three months at a few hours a week. You can audit it free; the certificate runs on Coursera's monthly subscription.

Instructor

AN
Andrew Ng
Coursera instructor

Andrew Ng co-founded Coursera and Google Brain and is one of the most recognised teachers in machine learning. This specialization is the modern rebuild of the original course that introduced millions of people to the field.

Frequently asked questions

No — it's the 2022 rebuild, and the difference matters. The famous old course used Octave/MATLAB in one long course; this uses Python (NumPy, scikit-learn, TensorFlow), is split into three courses, and adds modern topics like neural networks and decision trees. If someone links you the old Octave course, take this instead — what you learn here transfers straight to real work.

Less than you'd fear. It's designed for high-school-level algebra; anything heavier is explained as it comes up, with the deeper maths tucked into optional videos. It leads with intuition and code, then the maths — so it's approachable even if you're not confident with numbers.

This one first. It's the beginner-friendly foundation covering the breadth of machine learning; the Deep Learning Specialization is the more advanced follow-on that goes deep on neural networks. Start here, and move to that when you want the depth.

Three courses: supervised learning (regression and classification), advanced algorithms (neural networks and decision trees), and unsupervised learning with recommender systems and a taste of reinforcement learning. It's a broad, modern grounding rather than a deep dive into any one area.

On its own, no — but it's an excellent, respected foundation. Employers want to see two or three real projects you've built, plus a solid resume and interview practice. Use the specialization to learn the fundamentals properly, then build a portfolio on top; together they're a strong application.
Free
to audit
Enroll now