Free Machine Learning Courses (and Programs) Chika O., August 16, 2025August 16, 2025 This list was curated based on the positive feedback from the free online AI courses. I hope you find this as helpful. Machine learning is one of the hottest jobs on the block, and the demand for ML skills will keep getting higher. But breaking into machine learning doesn’t have to break your bank account. Machine Learning Specializations and instructor-led bootcamps cost hundreds of dollars (sometimes thousands). In this carefully curated list, you will find world-class machine learning resources from universities, tech giants, and educational platforms, at zero cost. From Stanford’s legendary CS229 course to industry programs like Amazon’s ML Summer School, there’s a wealth of free ML resources here. This post is for anyone looking to start a career in machine learning, upgrade their ML skills, or prepare for an interview. You get to learn from MIT OpenCourseWare, Kaggle’s hands-on competitions, GitHub, YouTube, and specialized learning platforms. Although this list is by no means exhaustive, it aims to cover ML concepts (and skills required for work) for beginner and intermediate learners. Machine learning programs and internship opportunities are also covered. Toggle Free Online Machine Learning Courses Industry-Sponsored Machine Learning ProgramsFree Machine Learning Programs, Research Opportunities, and Internships for UndergraduatesTop Companies and Organisations Offering ML Internships:Free Tools and Platforms for Practising Machine LearningMaximise Your Free ML EducationCommon Pitfalls to Avoid in Your ML JourneyNext Steps After Free CoursesFinally… Free Online Machine Learning Courses (Click to download list of free ML courses PDF. If you are on mobile, please rotate your phone to view list) S/NOnline AI CourseProviderLevelPrerequisitesSpecializationType1What is Machine Learning?GoogleBeginnerNoneFoundational ML CourseFree2Machine Learning Crash CourseGoogleIntermediatePython, Basic Statistics and MathsEssential ML techniquesFree3Data Science: Machine LearningHarvard University (edX)BeginnerNoneHands-on fundamental MLAudit for free4Intro to Machine LearningUdacityIntermediateNoneData investigation for ML purposesFree5Intro to TensorFlow for Deep LearningUdacityBeginnerNoneFundamental math used in AIFree6Intro to Deep Learning with PyTorchUdacityBeginnerNoneFree7Mathematics for MLGitHubIntermediateNoneA collection of resources to learn and review mathematics for machine learning.Free8Linear AlgebraKhan AcadamyNoneFree9Stanford CS229: Machine LearningStanford UniversityNoneFree10Statistics and ProbabilityKhan AcadamyBeginnerNoneFree11ColumbiaX: Essential Math for AIAdvancedNoneComputer programming (Python); Calculus; Linear AlgebraFree to audit12Mathematics for ML (Book)Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon OngBeginner – AdvancedNoneMathematical foundations for MLFree13Mathematics for Machine Learning and Data Science SpecializationCourseraIntermediateHigh school math (functions, basic algebra) and Python programming (data structures, loops, functions, conditional statements, debugging).3-course series for fundamental ML and Data Science MathsFree14Machine Learning with PythonfreeCodeCampIntermediateKnowledge of TensorFlowML with TensorFlow, Neural networks, NLP, and reinforcement learningFree15Learning from Data (Introductory Machine Learning course)CaltechIntermediateNoneBasic ML theory, algorithms, and applicationsFree16Intro to Machine Learning in Trading with $TSLACodeSignalBeginnerNoneMachine Learning in Trading with a focus on Tesla ($TSLA) stockFree to access17Machine Learning Course CS 156CaltechIntermediateNoneBasic ML theory, algorithms and applicationsFree18Intro to Deep LearningMITIntermediateRegression, Neural Networks, and Decision TreesReinforcement learning, LLMsFree19Intro to Machine LearningMITIntermediateComputer programming (Python); Calculus; Linear AlgebraRegression, Neural Networks, and Decision treesFree20Neural NetworksGoogle Developer ProgrammeIntermediateIntro to ML, linear regression, datasetsFree21Introduction to Neural NetworksCourseraIntermediateIntro to ML, linear regression, datasetsConvolutional neural networks, Regularization in neural networks, feedforward neural networks, Learning in neural networks.Free22Scikit-Learn Tutorial: Python Machine Learning Model BuildingCodecademyIntermediateIntro to ML, Python programming, datasetsFree23Machine Learning: Introduction with RegressionCodecademyBeginnerNoneIntro to ML, Linear regression, multiple linear regressionFree24Intro to Deep Learning with TensorFlowCodecademyIntermediateNoneIntro to deep learning. and Intro to TensorFlowFree25Scikit-learn Crash Course – Machine Learning Library for PythonfreeCodeCampBeginnerIntro to ML, Python programming, datasetsData preprocessing, post-processing, and ScikitLearn IntroFree26Neural Networks3Blue1Brown (YouTube)BeginnerNoneThe basics of neural networks, and the math behind how they learnFree27Neural Networks and Deep Learning (Book)Michael NielsenBeginnerNoneNeural networks and deep learningFree28Linear Algebra (Animated Math)3Blue1Brown (YouTube)BeginnerBasic mathLearn to build, train, and optimise your own networks using TensorFlow.Free29Machine Learning: Random Forests & Decision TreesCodecademyBeginnerNoneDecision trees and random forest classifiersFree30Learn Testing for Web Development: Model TestingCodecademyIntermediateNoneHow to build the model layer of a web application using Mongoose and write tests to confirm its intended behavior.Free31Machine Learning StatisticsW3schoolsBeginnerNoneInferential Statistics, Descriptive Statistics,Free32ML MathematicsW3schoolsBeginnerNoneLinear function, linear algebra, vectors, matrices, and tensorsFree33Intro to Machine LearningKaggleBeginnerNoneBasic ML and model buildingFree34Machine Learning for BeginnersMicrosoftBeginnerNoneIntro to MLFree35ML ConceptsMicrosoftBeginnerNone–Free36Essentials of automated application deployment with GitHub Actions and GitHub PagesGitHubBeginnerNoneIntro to Git automation workflowFree37GenAI EssentialsfreeCodeCampBeginnerNoneGenerative AI development lifecycleFree38Computer VisionKaggleBeginnerNoneBuild convolutional neural networks with TensorFlow and Keras.Free39Practical Deep Learningfast.aiBeginnerPython programmingDeep learningFree Industry-Sponsored Machine Learning Programs Kindly note that applications are closed for some of these programs. You can add them to your learning resources or goals for next year. 1. Amazon Machine Learning Summer School (In-person in India) An intensive summer program where students learn ML skills directly from Amazon scientists. Eligibility: Engineering students enrolled in Bachelor’s, Master’s, or PhD programs in recognised Indian institutes. Benefits: Direct mentorship from Amazon ML scientists Real-world project experience Potential pathway to Amazon internships Amazon AWS AI & ML Scholars Program (Online) ML program designed in partnership with Udacity for underrepresented groups in tech Benefits: Full Udacity ML Nanodegree scholarship AWS promotional credits Mentorship opportunities Career support and networking Free Machine Learning Programs, Research Opportunities, and Internships for Undergraduates Microsoft Student Ambassadors For students passionate about building AI-driven solutions with Microsoft technologies. Benefits: Free Azure credits Access to Microsoft Learn AI modules Community and mentorship Top Companies and Organisations Offering ML Internships: Apple AI/ML Internships An opportunity to take part in the ongoing AI revolution at a big tech company. Kempner Institute Undergraduate Summer Internship Program in ML Research Engineering (by Harvard University) A 10-week summer internship program for current undergraduates. It presents a hands-on experience to building ML skills. Google AI Residency Program Formerly known as Google Brain Residency program. This is a year-long research training opportunity for those interested in pursuing a career in machine learning. Tech startups hire more interns to help train their AI models than big tech companies. Your chances of being hired or gaining hands-on experience are higher with startups. Platforms like Y Combinator, WellFound, Indeed and Glassdoor have posts on ML internships. Their requirements typically include: a deep understanding of AI, a portfolio of your projects (including Kaggle competitions you completed), evidence that you read ML research papers for updates – since the field is in its early days – and so on. Free Tools and Platforms for Practising Machine Learning Development Environments (Free) Google Colab: Free GPU access Kaggle Kernels: Integrated datasets and compute GitHub Codespaces: Cloud development environment Free Cloud Credits AWS Educate: $100+ in credits for students Google Cloud for Students: $300 credit Microsoft Azure for Students: $100 credit Open Source ML Libraries scikit-learn: Classical ML algorithms TensorFlow: Deep learning framework PyTorch: Research-focused deep learning Hugging Face: Pre-trained models and datasets Maximise Your Free ML Education 1. Build a Portfolio Document projects on GitHub Participate in Kaggle competitions (this gives you a hiring edge) Write blog posts about your learning journey – a learning hack that helps you learn better when you try to explain the concepts to others. 2. Network Strategically Attend virtual ML conferences (many offer free tickets) Participate in online study groups Connect with instructors and fellow students 3. Stay Current Follow ML Twitter personalities Subscribe to ML newsletters (The Batch, AI Research, more on Substack) Join online communities Read ML research papers for updates 4. Practice Consistently Set aside dedicated study time Work on projects regularly Participate in online discussions Don’t let the amount of material you have to study overwhelm you. Take it one at a time. Avoid burning out. Schedule breaks. Common Pitfalls to Avoid in Your ML Journey 1. Tutorial Hell Don’t just watch videos. Take notes and go back to them often. Build projects and apply what you learn. 2. Skipping Mathematics While you can use ML libraries without deep math knowledge, understanding the fundamentals helps with debugging and optimisation. 3. Not Practising with Real Data Academic datasets are clean. Practice with messy, real-world data from Kaggle or collect your own. Next Steps After Free Courses When to Consider Paid Options You want structured career support and job placement You need credentials for career advancement You want access to premium project datasets and infrastructure Building on Your Free Foundation Specialization certificates: These will give you a more structured path to earn the required skills. Advanced degrees: Consider an MSc in ML/AI if research interests you Industry bootcamps: Intensive job-focused training Finally… The abundance of free, high-quality machine learning education in 2025 means that financial constraints shouldn’t prevent anyone from entering this exciting field. However, the best course is the one you actually complete. Start with one program/skill, commit to finishing it, and build from there. The ML community is welcoming and collaborative. Take advantage of free resources, contribute back when you can, and help others along their journey. Your ML career starts with a single step. With these free resources, that step costs nothing but your time and dedication. Share this: Click to share on X (Opens in new window) X Click to share on Facebook (Opens in new window) Facebook Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Pinterest (Opens in new window) Pinterest Click to share on WhatsApp (Opens in new window) WhatsApp Like this:Like Loading... 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