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Machine Learning with Python Certification

4.7(5,800) on freeCodeCamp·890K enrolled
Intermediate 300 hours English Course CertificateFREE
SkillsMachine learningTensorFlowNeural networksPythonModel building

Is this course right for you?

Our take
Yes, if you already know Python and want a free, hands-on introduction to machine learning. You learn by building projects, and the certificate is free.

Good for: python programmers who want a free, hands-on introduction to machine learning.

Skip if: you are new to Python, or you want deep mathematical theory.

Built with TensorFlow, it moves through neural networks, NLP and reinforcement-learning basics, and the five projects — a book recommender, an SMS spam classifier, a rock-paper-scissors bot — make you apply each idea. It sits between the conceptual (Ng's ML Specialization) and the advanced (Stanford's CS231n and CS224n).

It moves fast and is lighter on theory than a university course, so it does not give you the mathematical depth, and it assumes you can already program in Python. If you are new to Python or want the theory first, start elsewhere. The curriculum is free and kept current (as of 2026).

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About this course

freeCodeCamp's Machine Learning certification is built in partnership with TensorFlow and uses TensorFlow 2.x as the primary framework. It covers core neural network concepts (dense layers, convolutional networks, recurrent networks), natural language processing, reinforcement learning basics, and model evaluation — structured around video content developed with input from MIT.

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Instructor

FT
freeCodeCamp Team
freeCodeCamp instructor
890K+ learners12 courses4.7 instructor rating

Produced by freeCodeCamp in partnership with TensorFlow, with curriculum input from MIT, covering applied machine learning through practical TensorFlow projects.

Frequently asked questions

Yes — this is machine learning with Python, not a course to learn Python. It assumes you can already write basic Python. If you can't yet, do freeCodeCamp's Scientific Computing with Python certification first, then come back for the machine learning.

A broad, hands-on sweep: TensorFlow, neural networks, natural language processing and reinforcement learning, plus core supervised and unsupervised methods. It's more about seeing the range of what machine learning can do than going deep on any single technique.

Five, which is how the certificate is earned — including a rock-paper-scissors bot, an image classifier, a book-recommendation engine, a health-cost prediction model and an SMS spam classifier. They cover different ML techniques, so you finish with varied, working examples.

Heavily practice. It teaches you to build and run models in TensorFlow, but it's light on the maths and theory behind them. You'll come away knowing how to use machine learning more than why the algorithms work, which is fine as a start but worth knowing going in.

Different emphases. This is free, code-first and built on TensorFlow — you build things quickly. Ng's teaches the algorithms from the ground up with more maths and intuition. Do this if you want practical, free, hands-on ML; do Ng's if you want to understand the fundamentals properly. Many people do both.

It's a genuine hands-on introduction and a good free way to see if ML is for you, but it isn't a full path to a job. Machine-learning roles want stronger maths, deeper understanding, and real projects of your own. Treat it as a strong first step, not the finish line.
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