An Ultimate List of 20+ Free Online Courses on Machine Learning

Introduction

When you’re choosing how to learn ML, one straightforward path is online courses. In this list, we’ve collected a curated set of free (or free-to-audit) ML courses you can choose from. These range from university lectures to structured programs offered by online platforms, giving you a variety of entry points depending on your background. Certificates are often optional and paid, but the learning materials themselves are accessible to anyone.

Our goal here is to save you time: instead of trial and error across countless options, you’ll find in one place the most solid foundations to start building your ML skills.

Note about Free Courses

Some options are fully free and even provide certificates, but those are rare.

Most major platforms, such as Coursera and edX, allow you to audit course materials (videos, readings, lecture notes) at no cost. However, if you want access to graded assignments or a certificate, you’ll need to pay. On Coursera, keep in mind that the Audit option is only visible on individual course pages—not on Specialization pages.

University open-courseware is another category: these are completely free and often come with full access to lectures, notes, and problem sets. The trade-off is that they usually don’t offer certificates of completion.

1. ML Zoomcamp by DataTalksClub

1. Platform: DataTalks.Club (ML Zoomcamp)
2. Provider: DataTalks.Club (instructor: Alexey Grigorev)
3. Difficulty Level: Beginner-Intermediate (coding experience required; no prior ML needed)
4. Format: Free course offered in two modes—self-paced (full materials) or a 4-month live cohort with weekly assignments, project reviews, and Slack community
5. Duration: ~4 months (live cohort starting September 2025) or self-paced
6. Certificate: Available for free upon completion for the live cohort; not issued for the self-paced track

Machine Learning Zoomcamp is a practical, end-to-end ML engineering course that takes learners from core foundations to production deployment. You’ll cover regression and classification, evaluation and cross-validation, trees and gradient boosting, and deep learning (CNNs, transfer learning). A deployment track focuses on packaging and serving models (Flask APIs, Docker, cloud, serverless, TensorFlow Serving, Kubernetes, optional KServe) plus monitoring and CI/CD. The program centers on building: a midterm end-to-end project and a capstone production system, emphasizing reproducible code, system design, and documentation, supported by a structured homework cadence and an active community.

Examples of the final projects:

• Blood Cell Classifier for Cancer Prediction (Computer Vision)

• Waste Classifier (Computer Vision)

2. Google Machine Learning Crash Course (MLCC)

1. Platform: Google Developers
2. Provider: Google
3. Difficulty Level: Beginner-Intermediate
4. Format: Free, self-paced course
5. Duration: Self-paced (module-based; time commitment varies)
6. Certificate: Not available

“Machine Learning Crash Course” by Google is a practical introduction to ML that combines short videos, interactive demos, and coding exercises. Core modules cover linear and logistic regression, binary classification metrics, and data preparation for numerical and categorical features (including one-hot, feature hashing, mean encoding, and feature crosses), plus generalization and overfitting. Advanced modules introduce neural networks, embeddings, and a primer on large language models (tokens, Transformers, training basics). Real-world tracks address production ML systems, AutoML, and ML fairness, emphasizing deployment considerations and responsible practice.

3. Microsoft “ML for Beginners”

1. Platform: GitHub (open-source curriculum; supplemental modules on Microsoft Learn)
2. Provider: Microsoft (Cloud Advocates)
3. Difficulty Level: Beginner
4. Format: Free, self-paced curriculum
5. Duration: 12 weeks (self-paced; time per week varies)
6. Certificate: Not available

“Machine Learning for Beginners” by Microsoft is a project-based, introductory curriculum that teaches classical ML with Python and scikit-learn—deliberately avoiding deep learning. Organized as 26 lessons over 12 weeks, it blends short readings, knowledge checks, and coding assignments with end-to-end mini-projects (including a simple web app) to reinforce practice. Datasets and examples span global contexts, and many lessons have R equivalents. You’ll progress from fundamentals and regression to classification, clustering, NLP, time series, and reinforcement learning, with frequent quizzes and structured challenges to solidify concepts. The repository includes solutions, a quiz app, translation support, and guidance for both self-learners and instructors.

4. Machine Learning with Python by IBM

1. Platform: Coursera
2. Provider: IBM
3. Difficulty Level: Intermediate (recommended experience)
4. Format: Self-paced online course
5. Duration: ~2 weeks at ~10 hours/week
6. Certificate: Audit free; optional certificate available (paid)

“Machine Learning with Python” by IBM gives a practical introduction to machine learning with Python and scikit-learn. You’ll cover core concepts and roles in ML; implement regression (linear, multiple linear, polynomial, logistic), supervised methods (decision trees, k-nearest neighbors, SVM), and unsupervised techniques (clustering; dimensionality reduction with PCA, t-SNE, UMAP). Labs emphasize model evaluation (metrics, cross-validation, regularization) and pipeline optimization. A rainfall-prediction project and a course-wide exam consolidate skills. Instructors: Joseph Santarcangelo and Jeff Grossman (IBM).

5. Machine Learning Specialization by DeepLearning.AI

1. Platform: Coursera
2. Provider: DeepLearning.AI & Stanford Online (offered by Stanford University and DeepLearning.AI)
3. Difficulty Level: Beginner (recommended experience)
4. Format: Self-paced Specialization; 3 courses
5. Duration: ~2 months at ~10 hours/week (≈94 hours total)
6. Certificate: Audit free; optional certificate available (paid)

“Machine Learning Specialization” by DeepLearning.AI is a beginner-friendly, three-course program (taught by Andrew Ng) covers the fundamentals of modern machine learning and how to apply them in practice. You’ll build models in Python with NumPy and scikit-learn; implement supervised learning for regression and classification (linear/logistic regression, neural networks with TensorFlow, decision trees and tree ensembles); apply best practices for evaluation and data-centric improvement; and use unsupervised methods such as clustering and anomaly detection. The final course adds recommender systems (collaborative filtering, content-based deep learning) and an introduction to deep reinforcement learning.

6. StanfordOnline: Statistical Learning with Python

1. Platform: edX
2. Provider: StanfordOnline
3. Difficulty Level: Introductory
4. Format: Self-paced online course
5. Duration: ~11 weeks at 3-5 hours/week
6. Certificate: Audit free; optional certificate available (paid)

“Statistical Learning with Python” by StanfordOnline led by Trevor Hastie, Robert Tibshirani, and Jonathan Taylor centers on regression and classification and builds practical intuition for resampling (cross-validation, bootstrap), model selection and regularization (ridge, lasso), nonlinear modeling (splines, GAMs), tree-based methods (trees, random forests, boosting), support-vector machines, and a concise treatment of neural networks/deep learning. It also surveys unsupervised learning (PCA, k-means, hierarchical clustering), survival analysis, and multiple testing. Computing is done in Python with step-by-step labs that implement the methods covered in the lectures, closely aligned with the textbook An Introduction to Statistical Learning with Applications in Python.

7. MITx: Machine Learning with Python: from Linear Models to Deep Learning

1. Platform: edX
2. Provider: MITx (Massachusetts Institute of Technology)
3. Difficulty Level: Intermediate-Advanced (requires Python, probability, calculus, and linear algebra)
4. Format: Instructor-paced online course
5. Duration: ~15 weeks at 10-14 hours/week
6. Certificate: Audit free; optional certificate available (paid)

“Machine Learning with Python: from Linear Models to Deep Learning” by MIT is an in-depth, instructor-led course that teaches how to turn training data into effective predictive systems. It develops the theory of representation, overfitting, regularization, and generalization (including VC dimension), and applies it to classification, regression, clustering, recommender problems, probabilistic modeling, and reinforcement learning. You’ll implement linear models, kernel methods and SVMs, neural networks (including deep and recurrent nets), and EM-based generative models, while practicing end-to-end project organization from training/validation and hyperparameter tuning to feature engineering. Projects include an automatic review analyzer, digit recognition with neural networks, and a reinforcement-learning task. Instructors include Regina Barzilay, Tommi Jaakkola, and Karene Chu.

8. Data Science: Machine Learning by Harvard

1. Platform: edX
2. Provider: HarvardX
3. Difficulty Level: Introductory
4. Format: Self-paced online course
5. Duration: ~8 weeks at 2-4 hours/week
6. Certificate: Audit free; optional certificate available (paid)

“Data Science: Machine Learning” by Harvard teaches core machine learning skills by guiding you through building a movie recommendation system end to end. You’ll work with training data to uncover predictive relationships, practice cross-validation to avoid overfitting, and implement several common algorithms with an emphasis on when and why they work. Along the way, you’ll learn about regularization and principal component analysis and apply them in a practical pipeline. Designed for learners in the HarvardX Professional Certificate in Data Science, it balances fundamentals with hands-on implementation to develop sound intuition for ML model development and evaluation.

9. Stanford CS229

1. Platform: Stanford University course website
2. Provider: Stanford University (CS)
3. Difficulty Level: Intermediate-Advanced (requires Python/NumPy, probability, multivariable calculus, and linear algebra)
4. Format: Instructor-led university course
5. Duration: Summer 2025 (June 24-August 16, 2025)
6. Certificate: Not available

CS229 “Machine Learning” is a broad, rigorous introduction to machine learning and statistical pattern recognition. The course covers:

• Supervised learning (generative vs. discriminative models; parametric and non-parametric methods; logistic/linear regression; neural networks; support vector machines)
• Unsupervised learning (clustering, dimensionality reduction, kernel methods)
• Elements of learning theory (bias-variance, generalization)
• Reinforcement learning/adaptive control.

Students practice through problem sets and companion lectures, with recent offerings adding topical guest sessions (e.g., agentic systems, sequence models, time-series forecasting) to connect core theory with modern applications in areas like robotics, data mining, bioinformatics, speech, and text/web processing.

10. UC Berkeley CS189/289A (upper-undergrad/grad)

1. Platform: University course website (UC Berkeley CS 189/289A)
2. Provider: University of California, Berkeley (EECS)
3. Difficulty Level: Intermediate-Advanced
4. Format: Instructor-led university course
5. Duration: ~16 weeks
6. Certificate: Not available

CS 189/289A “Introduction to Machine Learning” is Berkeley’s rigorous survey of modern ML, balancing theory and implementation. The course builds from probabilistic modeling and linear/logistic regression into neural networks (backpropagation, CNNs, attention/Transformers) and model evaluation, then broadens to dimensionality reduction (PCA, t-SNE), clustering, nearest neighbors, and tree/ensemble methods. Later units cover graphical models and HMMs, Markov decision processes and reinforcement learning, plus contemporary topics such as graph neural networks, language/vision applications, and causality. Students apply concepts through weekly discussions and programming assignments, with a midterm and final consolidating both mathematical grounding and practical skills.

11. Introduction to Machine Learning (MIT 6.036)

1. Platform: MIT Open Learning Library
2. Provider: Massachusetts Institute of Technology (MIT)
3. Difficulty Level: Intermediate (Python programming, calculus, and linear algebra recommended)
4. Format: Free, self-paced online course
5. Duration: ~13 weeks
6. Certificate: Not available

“Introduction to Machine Learning” (MIT 6.036) formulates well-specified learning problems and develops core ideas of representation, overfitting, and generalization. These principles are applied in supervised learning and reinforcement learning, with example applications to images and temporal sequences. Materials include lectures, notes, labs, and graded-style exercises to build both conceptual understanding and practical skills.

12. Caltech CS156 “Learning from Data”

1. Platform: Caltech (course website / YouTube)
2. Provider: California Institute of Technology (Caltech)
3. Difficulty Level: Introductory-Intermediate (requires basic probability, linear algebra with matrices, and calculus)
4. Format: Free, self-paced MOOC
5. Duration: 18 lectures (~60 minutes each)
6. Certificate: Not available

Recorded from a live Caltech broadcast, Yaser S. Abu-Mostafa’s Machine Learning course blends rigorous theory with practical intuition. It develops generalization foundations (training vs. testing, VC dimension, bias-variance), then works through linear and logistic models, neural networks and backpropagation, overfitting, regularization, and validation. The course also covers SVMs, kernel methods, radial basis functions, and practitioner principles (Occam’s razor, sampling bias, data snooping), with a concluding synthesis that situates Bayesian and ensemble ideas. Learners practice via eight homework sets and a final exam, guided by clear mathematical framing and implementation-focused discussion.

13. Machine Learning by Carnegie Mellon University

1. Platform: Carnegie Mellon University (course website; open materials)
2. Provider: Carnegie Mellon University (CMU)
3. Difficulty Level: Intermediate-Advanced (upper-level undergraduate / intro graduate)
4. Format: University lecture course; recorded lectures + slides; recitations; homeworks; programming project; two exams
5. Duration: Spring 2015 semester (~16 weeks; Jan 12-Apr 29, 2015)
6. Certificate: Not available

CMU’s 10-601 “Machine Learning”, taught by Tom Mitchell and Maria-Florina (Nina) Balcan, is a rigorous, semester-long introduction that blends theory with practice. The course builds from core supervised learning and probabilistic modeling into the mathematics of generalization and regularization, then moves through probabilistic graphical models and inference before tackling ensemble methods, kernel methods, and support vector machines. Mid-semester it shifts to semi-supervised and active learning and, later, to representation learning and dimensionality reduction, culminating with modern neural networks, deep learning, and a primer on reinforcement learning and privacy. Lectures, recitations, homeworks, a programming project, and two exams are designed to connect formal guarantees with hands-on implementation, mirroring the perspective of Mitchell’s classic textbook while updating it with contemporary practice.

14. Mathematics for Machine Learning Specialization by Imperial College London

1. Platform: Coursera
2. Provider: Imperial College London
3. Difficulty Level: Foundational to Intermediate (math-focused)
4. Format: Self-paced Specialization; 3 courses (Linear Algebra, Multivariate Calculus, Principal Component Analysis); video lectures, quizzes, and exercises
5. Duration: ~4-6 weeks per course (≈12-18 weeks total), learn at your own pace
6. Certificate: Audit free; optional certificate available (paid)

“Mathematics for Machine Learning” by Imperial College London is a three-course sequence that builds the core math needed to understand and implement ML algorithms. The Linear Algebra course covers vectors, matrices, dot products, basis changes, and eigenvalues/eigenvectors. Multivariate Calculus covers single- and multivariable differentiation, the chain rule, Jacobians, Hessians, Lagrange multipliers, and Taylor series. The PCA course connects linear algebra and calculus to statistics and geometry—orthogonality, projections, and dimensionality reduction—culminating in practical PCA applications. Courses are taught by Imperial College London faculty.

15. Machine Learning with Python by FreeCodeCamp

1. Platform: freeCodeCamp
2. Provider: freeCodeCamp
3. Difficulty Level: Intermediate (Python experience recommended)
4. Format: Self-paced online certification
5. Duration: Self-paced (project-based; varies by learner)
6. Certificate: freeCodeCamp “Machine Learning with Python” Certification (upon completing required projects)

“Machine Learning with Python” by FreeCodeCamp teaches practical machine learning with Python and TensorFlow. You’ll build several neural networks and explore core and advanced topics including how neural networks work, CNNs, RNNs/LSTMs, natural language processing, and an introduction to reinforcement learning. Instruction combines a TensorFlow course by Tim Ruscica (“Tech With Tim”) with conceptual videos by Brandon Rohrer. To earn the certificate, you complete hands-on projects such as Rock-Paper-Scissors, a Cat/Dog image classifier, a KNN book recommender, a linear-regression health-costs calculator, and an SMS text classifier, demonstrating applied skills across computer vision, NLP, recommendation, and regression.

16. Practical Deep Learning for Coders by Fast.ai

1. Platform: fast.ai
2. Provider: fast.ai (Jeremy Howard; co-created with Rachel Thomas)
3. Difficulty Level: Beginner-friendly (requires basic coding; minimal math)
4. Format: Free, self-paced video course
5. Duration: ≈13.5 hours of core video
6. Certificate: No formal certificate

“Practical Deep Learning for Coders” by Fast.ai teaches you to apply deep learning and machine learning to real problems without heavy prerequisites. Starting from working, state-of-the-art models, you’ll build and train systems for computer vision, NLP, tabular data, and collaborative filtering; create random-forest and regression baselines; and deploy models as simple web apps. Along the way you’ll learn transfer learning, data augmentation, weight decay, embeddings, and the training loop (including SGD) to understand why models work and how to improve them. The course emphasizes coding first, provides free cloud options for training, and is taught by Jeremy Howard.

17. Kaggle Machine Learning Courses

1. Platform: Kaggle Learn (Micro-Courses)
2. Provider: Kaggle
3. Difficulty Level: Beginner-Intermediate
4. Format: Self-paced micro-courses; short lessons with hands-on coding in Kaggle notebooks
5. Duration: A few hours per course (self-paced)
6. Certificate: Kaggle Learn certificate of completion

• Intro to Machine Learning: Covers the core supervised-learning workflow: how ML models work, basic data exploration, model validation, underfitting vs. overfitting, and training a first tree-based model (random forests) directly in Kaggle notebooks.
• Intermediate Machine Learning: Builds production-minded skills: handling missing values, working with categorical variables, building ML pipelines, applying cross-validation, training XGBoost models, and preventing data leakage.
• Feature Engineering: Focuses on improving signal: using mutual information to assess feature relevance, creating features, and applying unsupervised feature methods (k-means clustering, PCA) plus target encoding.

Recommendation: take them in the order listed to ensure prerequisites are covered.

18. Machine learning in Python with scikit-learn by Inria

1. Platform: FUN MOOC (France Université Numérique)
2. Provider: Inria (by scikit-learn core developers)
3. Difficulty Level: Intermediate (accessible with basic Python; NumPy/Pandas/Matplotlib helpful)
4. Format: Self-paced MOOC; Jupyter notebooks; quizzes + programming exercises; hands-on code labs
5. Duration: ~36 hours (self-paced)
6. Certificate: Open Badge upon ≥60% overall on quizzes and programming exercises (issued on request)

“Machine learning in Python with scikit-learn” by Inria is an in-depth, practical introduction to predictive modeling with scikit-learn, taught by members of the library’s core team. You’ll build end-to-end pipelines and develop sound intuition for model design, selection, and evaluation. The syllabus covers the predictive modeling pipeline, selecting the best model, hyperparameter tuning, linear models, decision trees, ensembles, and performance evaluation, with all materials freely available online. Instructors include Gaël Varoquaux, Olivier Grisel, Guillaume Lemaître, Loïc Estève, and colleagues from Inria.

19. Fundamentals of Machine Learning and Artificial Intelligence by AWS

1. Platform: Coursera
2. Provider: Amazon Web Services (AWS)
3. Difficulty Level: Beginner / Foundational
4. Format: Self-paced online course; 1 module; 1 assignment; taught in English; flexible schedule
5. Duration: ~1 hour
6. Certificate: Audit free; optional certificate available (paid)

“Fundamentals of Machine Learning and Artificial Intelligence” by AWS explains the foundations of AI and ML, the relationships between AI, machine learning, deep learning, and generative AI, and where these techniques are applied. You’ll learn core terminology and survey common machine learning algorithms and neural networks at a high level. The course also highlights selected AWS services that provide AI/ML capabilities and shows how they can be used to solve practical problems across industries. Skills covered include AI/ML fundamentals, generative AI, artificial neural networks, data analysis, and machine learning algorithms. Instruction is delivered by an AWS instructor.

20. Introduction to Machine Learning by Duke University

1. Platform: Coursera
2. Provider: Duke University
3. Difficulty Level: Intermediate (some related experience required)
4. Format: Self-paced online course; 6 modules; 24 assignments; hands-on practice in PyTorch; taught in English; flexible schedule
5. Duration: ~3 weeks (≈10 hours/week)
6. Certificate: Audit free; optional certificate available (paid)

“Introduction to Machine Learning” by Duke University introduces core machine learning models and where they are useful, covering logistic regression, multilayer perceptrons, convolutional neural networks, and basic natural language processing. Learners practice implementing models on real datasets with PyTorch, with applications spanning medical diagnostics, image recognition, and text prediction. Skills developed include supervised learning, neural networks, reinforcement learning fundamentals, computer vision, data validation, and applied ML. Instructors are Lawrence Carin, David Carlson, and Timothy Dunn from Duke University.

Conclusion

Learning machine learning doesn’t have to be expensive. With so many high-quality courses available for free or free-to-audit, you can build a solid foundation without spending a cent.

The key to free online learning is consistency and practice. Pick one course that matches your current level, stick with it, and apply what you learn through projects. Over time, you’ll gain the understanding of the core machine learning concepts and develop the practical skills.