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380 Best + Free Machine Learning Courses & Certificates [2026]
Ranked by Michael Kuhlman, CourseDuck's founder, from student reviews across every provider. How we rank · Embed this list
Our top picks
- Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R [Udemy] - Best Paid Course
- Python for Data Science and Machine Learning Bootcamp [Udemy] - Editor's Choice
- Complete A.I. & Machine Learning, Data Science Bootcamp [Udemy]
- The Top 5 Machine Learning Libraries in Python [Udemy]
- The Complete Python Developer [Udemy]
- Machine Learning, Data Science & AI Engineering with Python [Udemy]
- Machine Learning for Absolute Beginners - Level 1 [Udemy]
- Complete Data Science,Machine Learning,DL,NLP Bootcamp [Udemy]
- Artificial Intelligence & Machine Learning for Business [Udemy]
- Machine Learning by Stanford [Coursera] - Best Course Overall
About the best Machine Learning courses
CourseDuck compares the 380 Machine Learning courses listed here from 26 providers, 171 of them free, and ranks them from the 1.2 million ratings and reviews their students have left, with the price, length and level of each one side by side. No course pays to be listed or ranked. As featured on Harvard EDU, Stackify and Inc. How we rank
Frequently asked questions
- Udemy and Eduonix are best for practical, low cost and high quality Machine Learning courses.
- Coursera, Udacity and EdX are the best providers for a Machine Learning certificate, as many come from top Ivy League Universities.
- YouTube is best for free Machine Learning crash courses.
- PluralSight, SkillShare and LinkedIn are the best monthly subscription platforms if you want to take multiple Machine Learning courses.
- Independent Providers for Machine Learning courses & certificates are generally hit or miss.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
- Average
- $157,676 a year
- Middle half earn
- $129,000 to $187,500 25th to 75th percentile
- Full range
- $108,000 to $196,000
Share of US job postings in each pay band; the dark bar holds the average, the striped bars the 25th and 75th percentiles. Source: ZipRecruiter salary data, gathered when this guide was first published in February 2021.
Yes and No. Certified Machine Learning developers on average make more money. Having a Machine Learning certificate greatly increases the chance of landing an interview and can open otherwise closed doors. Coursera, Udacity and EdX offer excellent certificate options for impressing your future employers. Eduonix, Udemy and several other providers offer certificates, but they aren't as reputable. If you have a Computer Science Degree, certificates are not as important. Still, many employers won't care about certificates, but rather your interview skills, experience and/or skills assessment.
Provider
University
Tags
Rating
Duration
Difficulty
Publication Year
Language
380 Filtered Courses
![Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R](../../programming/machine-learning/images/950390_270f_7.jpg?v=1789255153)
Best Paid Course
1
Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R
4.5
A 49-hour survey of nearly every classic machine learning algorithm in Python and R, plus an AWS deployment track, taught intuition-first with diagrams rather than derivations.
Best for: Beginners with high school math who want a map of the whole ML field, with code templates in Python and R, before specialising.
Pros
Cons
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- Theory lectures explain each algorithm with diagrams and intuition; reviewers say the concepts stick.
- Covers regression through neural networks, plus an AWS track on SageMaker, deployment and CI/CD.
- Python and R tracks can be taken separately, with code templates included for the models.
- Refreshed in June 2026 and backed by 1.2 million students and a 4.5 rating from 206,000 reviews.
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- Coding videos are padded with long intros; one reviewer estimates a quarter of the runtime could be cut.
- Some Python snippets are deprecated and the graded labs fail on variable names, per recent reviews.
- Shallow on statistics; model assumptions are rarely checked, so it can feel like a coding tutorial.
Sponsored
2
The Data Science Course: Complete Data Science Bootcamp 2026
4.5
A 32-hour, theory-first path from probability and statistics through Python to regression, clustering and TensorFlow, aimed squarely at people starting from zero.
Best for: Beginners who want the math and statistics explained before touching a machine learning library.
Pros
Cons
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- Probability and statistics get about seven hours of real teaching before any Python appears
- 274 quizzes and 130 coding exercises; reviewers say the quizzes make the material stick
- Pace is praised as neither too fast nor too slow for absolute beginners
- Updated September 2026; an earlier CourseDuck review found Q&A responses fast and reliable
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- Long and theory-heavy up front; several low-star reviews lost interest before the practical parts
- One reviewer says the coding exercises are strict about exact answers and don't match the video examples
- Machine learning sections lean on running code without the math depth the statistics sections had
Editor's Choice
3
Python for Data Science and Machine Learning Bootcamp (2020)
4.5
Udemy's Bestseller-badged Python data science bootcamp, taking you from pandas through scikit-learn to a first neural net in about 25 hours, if you can live with 2020-era tooling.
Best for: People who already code a little and want one course covering pandas, plotting and every classic ML algorithm.
Pros
Cons
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- A code notebook for every lecture, so students run the code rather than just watch it
- Pandas section and the visualization libraries are singled out by reviewers as highlights
- Covers regression, trees, SVMs, k-means, NLP, neural nets and Spark in one place
- Q&A section that reviewers call very useful when they get stuck
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- Last updated May 2020; Plotly/Cufflinks, PySpark and some Seaborn calls no longer work as shown
- Long on videos, short on practice: one quiz and no coding exercises across 165 lectures
- Decision trees and the deep learning section feel rushed to several reviewers
4
Complete A.I. & Machine Learning, Data Science Bootcamp (2026)
4.7
Andrei Neagoie and Daniel Bourke's 44 hour ramp from Python basics to TensorFlow 2, built around two milestone projects and holding a 4.7 rating from about 31,000 students.
Best for: Complete beginners, no math or coding required, who want one long guided path to a first deep learning project.
Pros
Cons
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- Two milestone projects, a classification task and a time series model, with all notebooks provided on GitHub
- Two separate Python sections so non-programmers can start from zero and coders can skip straight to pandas
- Refreshed in February 2026, with 384 lectures and 61 articles spread across 20 sections
- Reviewers call the teaching engaging and fun to watch, with a new-hire framing that keeps lessons focused
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- Unsupervised learning is in the blurb, but one low-star reviewer says it is missing and the course feels incomplete
- Only two quizzes and one coding exercise across 44 hours, so practice is mostly watch and retype
- Informal chatter and lightly explained concepts frustrate reviewers who wanted more depth
5
The Top 5 Machine Learning Libraries in Python (2025)
4.6
A free, 100-minute tour of pandas, NumPy, scikit-learn, matplotlib and NLTK that shows what each library is for before anyone commits to a long machine learning bootcamp.
Best for: Total beginners who want to know what each Python ML library does before paying for a full bootcamp.
Pros
Cons
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- Free and only 100 minutes long, so it fits in a single evening
- Opening terminology section that reviewers say clears up most of the early confusion
- Annotated Jupyter notebook included so students can run the code while watching
- Refreshed in April 2025, unusual for a free course first published in 2017
-
- Surface level only; three-star reviewers say nobody could build a model from this alone
- Some reviewers say it jumps between topics rather than teaching one library properly
- No coding exercises, and the final bonus lecture is a pitch for the paid course
![Deep Learning A-Z [2026]: DL, AI in Python & AWS + LLM Prize](../../programming/data-science/images/1151632_de9b_3.jpg?v=1789256617)
Sponsored
6
Deep Learning A-Z [2026]: DL, AI in Python & AWS + LLM Prize
4.6
Six-part tour of neural networks, CNNs, RNNs, self organizing maps, Boltzmann machines and autoencoders that leads with intuition and has drawn over 400,000 students.
Best for: Beginners who want to understand why each network works before writing code, across six different architectures.
Pros
Cons
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- Intuition lectures before every coding section, which reviewers call clear and lucid
- Covers six architectures including SOMs, Boltzmann machines and autoencoders
- Updated June 2026, with AWS sections and an annex on regression and preprocessing
- Links to further reading and code templates included
-
- Coding sections reportedly lean on copy and paste, with some keywords left unexplained
- Datasets must be requested from an outside site, which one reviewer found slow
- No transformers in the syllabus, and reviewers call parts outdated or too basic
7
The Complete Python Developer (2026)
4.6
How to become a Python 3 Developer and get hired! Build 12+ projects, learn Web Development, Machine Learning + more!
What You'll Learn
- Become a professional Python Developer and get hired
- Master modern Python 3.13(latest) fundamentals as well as advanced topics
- Learn Object Oriented Programming
- Learn Function Programming
- Build 12+ real world Python projects you can show off
- Learn how to use Python in Web Development
- Learn Machine Learning with Python
- Build a Machine Learning Model
- Learn Data Science - Analyze and Visualize Data
- Build a professional Portfolio Website
- Use Python to process: Images, CSVs, PDFs, and other Files
- Build a Web Scraper with Python and BeautifulSoup
- Use Python to send Emails and SMS
- Use Python to build a Twitter bot
- Learn to Test, Debug and Handle Errors in your Python programs
- Learn best practices to write clean, performant, and bug free code
- Learn to use Selenium and Python in Automation
- Set up a professional workspace with Jupyter Notebooks, PyCharm
8
Machine Learning, Data Science & AI Engineering with Python (2026)
4.6
Frank Kane's 20-hour survey runs from a Python and stats refresher through classical ML to GPT, RAG and LLM agents, refreshed August 2026 and rated 4.6 by nearly 37,000 students.
Best for: Working programmers who want one broad, current tour of machine learning plus generative AI before picking a specialty.
Pros
Cons
-
- Covers GPT, the OpenAI API, RAG and LLM agents, with the content refreshed in August 2026
- Reviewers say the instructor speaks slowly and clearly and the setup steps and notebooks just work
- About 20 hours across 148 lectures, from a stats refresher to Spark MLlib and deep learning
- 4.6 stars from nearly 37,000 reviews, roughly 90 percent of them 4 or 5 stars
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- Several reviewers say the instructor reads from notes rather than building intuition
- A long statistics and probability stretch comes before the real projects begin
- Some download links have gone stale, and there is no full LLM or agents project
9
Machine Learning for Absolute Beginners - Level 1 (2026)
4.5
Learn the Fundamental Concepts of Artificial Intelligence and Machine Learning as the Next Game-Changing Technology
What You'll Learn
- Artificial Intelligence, Machine Learning and Deep Learning
- Applied vs. Generalized AI
- Features, Labels, Examples
- The Process of Training a Model
- Under-fitting and Over-fitting
- Supervised/Unsupervised Learning
- Classification and Regression
- Clustering and Dimension Reduction
- Reinforcement Learning
- Generative AI
10
Complete Data Science,Machine Learning,DL,NLP Bootcamp (2026)
4.5
Master the theory, practice,and math behind Data Science,Machine Learning,Deep Learning,NLP with end to end projects
What You'll Learn
- Master foundational and advanced Machine Learning and NLP concepts.
- Apply theoretical and practical knowledge to real-world projects using Machine learning,NLP And MLOPS
- Understand and implement mathematical principles behind ML algorithms.
- Develop and optimize ML models using industry-standard tools and techniques.
- Understand The Core intuition of Deep Learning such as optimizers,loss functions,neural networks and cnn
11
Artificial Intelligence & Machine Learning for Business (2025)
4.5
The Ultimate Artificial Intelligence & Machine Learning course for CxOs, Managers, Team Leaders and Entrepreneurs
What You'll Learn
- Applications of Artificial Intelligence and Machine Learning for Business Leaders (CxOs, Managers, Team Leaders, MBA Students, Entrepreneurs)
- How can you apply Artificial Intelligence and Machine Learning in your areas of functions?
- Disruption happening in several domains and industries because of Artificial Intelligence and Machine learning