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408 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 guide: The 5 Best Machine Learning Courses in 2026
Our top picks
- Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R [Udemy]Best Paid Course
- The Data Science Course: Complete Data Science Bootcamp 2026 [Udemy]
- 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]
- Deep Learning A-Z [2026]: DL, AI in Python & AWS + LLM Prize [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]
About the best Machine Learning courses
Frequently asked questions
The most-reviewed Machine Learning course in this category is Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R by Kirill Eremenko, with about 206,000 reviews at 4.5 as of October 2026.
Machine learning is a branch of artificial intelligence in which computers learn patterns from data instead of following hand-written rules. It is used for classification, prediction, recommendation and language processing. Courses here cover machine learning with Python, data science, natural language processing, MLOps and model deployment.
- 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 collected in 2020-21, when this FAQ was first written; pay has moved since, so treat it as a rough guide.
Paid Udemy video courses include a certificate of completion when you finish; free courses and practice-test-only courses do not. It shows you did the work, but it is not an accredited qualification. Coursera and edX usually charge for a certificate, issued with the university or company that made the course. LinkedIn Learning gives a certificate of completion for most of its courses as part of the subscription.
Provider
University
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![Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R](../../programming/machine-learning/images/950390_270f_7.jpg?v=1789255153)
1
Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R
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.
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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.
2
The Data Science Course: Complete Data Science Bootcamp 2026
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.
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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
3
Python for Data Science and Machine Learning Bootcamp (2020)
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.
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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)
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.
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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)
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.
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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
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- 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/deep-learning/images/1151632_de9b_3.jpg?v=1789256617)
6
Deep Learning A-Z [2026]: DL, AI in Python & AWS + LLM Prize
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.
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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
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- 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)
A 31-hour beginner Python course that goes from basics into scripting, scraping, web development and a short machine learning section, all taught by one instructor.
Best for: Complete beginners who want one course that samples scripting, scraping, web apps and a little machine learning.
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- Reviewers call the explanations clear and easy to follow for a first language
- One reviewer says it explains why Python behaves as it does, not just syntax
- After the core language, it samples scripting, scraping, web and machine learning
- Refreshed in August 2026, and the description targets Python 3.13
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- Some reviewers say tooling and practices feel dated, for example no uv
- At least one student hit errors following along on a newer Python version
- A few reviewers say it stays high level and is thin on fundamentals depth
8
Machine Learning, Data Science & AI Engineering with Python (2026)
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.
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- 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)
A short, no-code tour of machine learning and generative AI, built for people who want the vocabulary and big ideas before touching any code.
Best for: Managers, product people and curious beginners who want AI terms explained without code or math.
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- Reviewers say the explanations are easy to follow and the visuals help
- Needs no math or programming, which reviewers count as a plus
- Generative AI sections explain how models treat prompts and training data
- Short and structured: 59 lectures in eight sections with quizzes
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- All theory. No coding exercises, so nothing gets built or practiced
- Some reviewers found the examples watered down and light on how AI works inside
- A reviewer wanted a cleaner transcript, and another found it dry
10
Complete Data Science,Machine Learning,DL,NLP Bootcamp (2026)
A 100 hour path from Python basics to MLOps with Krish Naik: statistics, classic ML algorithms, two deployment projects and an NLP finish, with plenty of math.
Best for: Python-literate beginners who want one long path from statistics and ML math to Docker and cloud deployment.
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- Explains why each algorithm exists and the math behind it, reviewers say
- Two long end to end projects cover Docker, AWS, Azure, MLflow and ETL pipelines
- Updated in August 2026, with 436 lectures and 20 coding exercises
- Reviewers say lectures stay easy to follow even at 1.75x or 2x speed
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- Some topics feel rushed, and libraries get less explanation than the math
- No real deep learning section in the syllabus, and nothing practical after transformers
- True beginners report feeling lost in the statistics and hyperplane material
11
Artificial Intelligence & Machine Learning for Business (2025)
A six-hour, no-code overview of AI and machine learning for managers and executives, covering industry uses, team building and the steps of an AI project.
Best for: Managers and executives who need AI vocabulary and a project-process overview, with no coding.
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- No coding or prior AI knowledge needed, so it works for managers coming in cold
- Covers the practical side: building an AI team, setting up a practice and the steps of a project
- Well-divided sections and 13 quizzes help reinforce each topic
- Industry and function examples show where AI is used, from sales and marketing to finance and HR
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- Reviewers call it high-level and theory-heavy, with few real-world cases or frameworks
- One reviewer says many lectures are from 2020 or earlier and need updating
- Slow introductions drew complaints, and one narrator's pronunciation was hard to follow