Free Machine learning Courses Online with Certificates (2026)

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Great Learning Academy’s free machine learning courses cover Python fundamentals, data preparation, and model training with NumPy, Pandas, scikit learn, and TensorFlow. Learn algorithms like linear regression, logistic regression, decision trees, random forests, and SVMs. Build and test models through practical projects, then strengthen evaluation skills and deploy models in real-world machine learning workflows.

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UNIVERSITY

Great Learning

Generative AI for Practitioners

12 weeks • Online

UNIVERSITY

Walsh College

MS in Artificial Intelligence & Machine Learning

2 Years • Online

PRO

NEW

1 project

End-to-End NLP with Python: Build Chatbots and LLM Applications

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PRO

1 project

Google Gemini Practical AI for Working Professionals

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UNIVERSITY

MIT IDSS

AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact

12 weeks • Online

UNIVERSITY

MIT Professional Education

No Code and Agentic AI

14 Weeks • Online

Learn from MIT Faculty

UNIVERSITY

Johns Hopkins University

Applied Generative AI and Agentic AI

16 weeks • Online

PRO

18 coding exercises 3 projects

Master Artificial Intelligence

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136 coding exercises 6 projects

Master Data Science & Machine Learning in Python

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51 coding exercises 3 projects

Master Python programming

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1 coding exercise 1 project

Machine Learning Essentials with Python

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4.51 474.6K+ Learners 1.5 hrs

Python for Machine Learning

Skills: NumPy Arrays, NumPy Operations, NumPy Math, Saving & Loading NumPy, Pandas Series, Pandas DataFrame, Pandas Functions (Mean, Median, Max, Min), Data Manipulation, Supervised Learning, Unsupervised Learning, Machine Learning with Python

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BASICS

4.57 98K+ Learners 11 hrs

Machine Learning with Python

Skills: Python, Statistics, Reinforcement learning, Machine learning

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PRO

NEW

1 project

End-to-End NLP with Python: Build Chatbots and LLM Applications

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BASICS

Supervised Machine Learning with Logistic Regression and Naïve Bayes

4.43 21.9K+ Learners 2 hrs

Supervised Machine Learning with Logistic Regression and Naïve Bayes

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

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Probability and Probability Distributions for Machine Learning

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Probability and Probability Distributions for Machine Learning

Skills: Marginal Probability, Bayes Theorem , Binomial Distribution, Normal Distribution, Poisson Distribution

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BASICS

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Basics of Machine Learning

Skills: Introduction to Machine Learning, Supervised Machine Learning, Linear Regression, Pearson's Coefficient, Coefficient of Determinant

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BASICS

4.46 77.9K+ Learners 1 hr

Introduction to Machine Learning

Skills: Learn the fundamentals of machine learning, including supervised and unsupervised learning, regression, and recommendation systems. Join this free machine learning course to apply these skills in real-world business scenarios.

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PRO

1 project

Google Gemini Practical AI for Working Professionals

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BASICS

4.44 3.9K+ Learners 1.5 hrs

Predictive Analytics

Skills: Basics of Predictive Analytics, Industry Application, Linear Regression, Hands-on,

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BASICS

Artificial Intelligence and Machine Learning Projects

4.45 4.4K+ Learners 1.5 hrs

Artificial Intelligence and Machine Learning Projects

Skills: Machine Learning Algorithms

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BASICS

4.49 32.5K+ Learners 1.5 hrs

Machine Learning Algorithms

Skills: Classification (Logistic Regression, Decision Trees, SVM), Regression (Linear, Ridge, Lasso), Clustering (K-means, Hierarchical), model evaluation, cross validation

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BASICS

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Basics of Unsupervised Machine Learning

Skills: Basics of Python

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Python for Machine Learning

4.51 474.6K+ learners 1.5 hrs

BASICS

Machine Learning with Python

4.57 98K+ learners 11 hrs

Skills: Python, Statistics, Reinforcement learning, Machine learning

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End-to-End NLP with Python: Build Chatbots and LLM Applications

4.67 518 learners 12.5 hrs

BASICS

Supervised Machine Learning with Logistic Regression and Naïve Bayes

4.43 21.9K+ learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

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Probability and Probability Distributions for Machine Learning

4.49 20.9K+ learners 1.5 hrs

Skills: Marginal Probability, Bayes Theorem , Binomial Distribution, Normal Distribution, Poisson Distribution

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Basics of Machine Learning

4.39 149.4K+ learners 2.5 hrs

Skills: Introduction to Machine Learning, Supervised Machine Learning, Linear Regression, Pearson's Coefficient, Coefficient of Determinant

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Introduction to Machine Learning

4.46 77.9K+ learners 1 hr

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Google Gemini Practical AI for Working Professionals

4.67 221 learners 6.5 hrs

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Predictive Analytics

4.44 3.9K+ learners 1.5 hrs

Skills: Basics of Predictive Analytics, Industry Application, Linear Regression, Hands-on,

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Artificial Intelligence and Machine Learning Projects

4.45 4.4K+ learners 1.5 hrs

Skills: Machine Learning Algorithms

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Machine Learning Algorithms

4.49 32.5K+ learners 1.5 hrs

Skills: Classification (Logistic Regression, Decision Trees, SVM), Regression (Linear, Ridge, Lasso), Clustering (K-means, Hierarchical), model evaluation, cross validation

BASICS

Basics of Unsupervised Machine Learning

4.2 1.3K+ learners 7 hrs

Skills: Basics of Python

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Basics of Unsupervised Machine Learning

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Skills: Machine Learning, Supervised Learning

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Basics of Machine Learning

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Machine Learning with Python

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Introduction to Machine Learning

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Skills: Descriptive Statistics, Measures of Dispersion Range and IQR,,Central Tendency and 3 Ms,The Empirical Rule and Chebyshev Rule,Correlation Analysis

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Machine Learning Algorithms

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Regression Analysis Using R

Skills: Linear Regression, Concept of Multicollinearity, R Square, Predictive Modeling

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Supervised Machine Learning with Logistic Regression and Naïve Bayes

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

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Basics of Unsupervised Machine Learning

Skills: Basics of Python

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Introduction to XGBoost

Skills: Python skills, Basic ML concepts

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4.45 4.4K+ Learners 1.5 hrs

Artificial Intelligence and Machine Learning Projects

Skills: Machine Learning Algorithms

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Predictive Analytics for Machine Learning

Skills: Predictive Analytics, Feature Engineering, Deep Feature Synthesis, Model Selection Techniques

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Python IDEs for Machine Learning

Skills: Introduction to Python IDEs, Spyder, Google Colab, Jupyter Notebook

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4.64 256 Learners 1.5 hrs

Cross Validation in Machine Learning

Skills: Cross-validation, K-fold CV and LOOCV, ROC-AUC curve

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4.47 914 Learners 1 hr

Feature Engineering for Machine Learning

Skills: Introduction to Feature Engineering, Under sampling, Over sampling

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Applications of Data Science & Machine Learning

Skills: Statistical analysis, Deep Learning, how to work and process large and unstructured data sets, and Data Visualization and among others.

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Skills: Introduction to NumPy, Pandas and Data Visualization in Python

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4.64 1K+ Learners 1 hr

LDA in Entertainment Industry

Skills: Application of LDA, Building Pipelines, Data Balancing, Data Validation

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Skills: Machine Learning Landscape

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Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

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4.61 1.8K+ Learners 1 hr

k-fold Cross Validation

Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation

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Introduction to Supervised Learning

Skills: Machine Learning, Supervised Learning

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Python for Machine Learning

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4.39 149.4K+ Learners 2.5 hrs

Basics of Machine Learning

Skills: Introduction to Machine Learning, Supervised Machine Learning, Linear Regression, Pearson's Coefficient, Coefficient of Determinant

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BASICS

4.57 98K+ Learners 11 hrs

Machine Learning with Python

Skills: Python, Statistics, Reinforcement learning, Machine learning

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Introduction to Machine Learning

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Statistics for Machine Learning

Skills: Descriptive Statistics, Measures of Dispersion Range and IQR,,Central Tendency and 3 Ms,The Empirical Rule and Chebyshev Rule,Correlation Analysis

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Machine Learning Algorithms

Skills: Classification (Logistic Regression, Decision Trees, SVM), Regression (Linear, Ridge, Lasso), Clustering (K-means, Hierarchical), model evaluation, cross validation

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4.53 30.4K+ Learners 2.5 hrs

Regression Analysis Using R

Skills: Linear Regression, Concept of Multicollinearity, R Square, Predictive Modeling

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4.43 21.9K+ Learners 2 hrs

Supervised Machine Learning with Logistic Regression and Naïve Bayes

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

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Learner reviews of the Free Machine Learning Courses

Our learners share their experiences of our courses

4.49

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Abhi Dey

5.0

★★★★ ★

India

“An Engaging Introduction to Python for Machine Learning”

This course provided a fantastic introduction to machine learning concepts through Python, blending theory with practical exercises that reinforced learning at every step. I appreciated how complex ideas were broken down into understandable parts, with ample real-world examples and projects to apply what was taught. The step-by-step approach to data preprocessing, model building, and evaluation made it easy to follow along and grasp each concept. Overall, it's an excellent resource for anyone looking to enter the field of machine learning with a solid Python base.

2211CS020218- K.LAXMI NARASIMHA

5.0

★★★★ ★

India

“The Course Was Well-Structured and Provided a Solid Foundation in Python for Machine Learning”

I appreciated the clarity of the explanations and the hands-on approach of the course. The interactive quizzes and practical projects helped reinforce my learning effectively. The support and feedback provided throughout the course were invaluable, making the learning process both engaging and rewarding.

Sanjana Lakkarsu

5.0

★★★★ ★

India

“Python for Machine Learning Course”

Python for Machine Learning is an excellent course that provides clear explanations and practical examples, making complex concepts easy to grasp. It effectively covers essential libraries like NumPy, pandas, and scikit-learn, offering a solid foundation for beginners and enhancing skills for advanced learners. Highly recommended!

Sadeepa Gajanayaka

5.0

★★★★ ★

“The Python Course Was Well-Structured and Informative, Offering Clear Explanations That Enhanced My Programming Skills Effectively”

The Python course exceeded my expectations with its comprehensive content and engaging format. The instructor provided clear explanations and practical examples, which made complex concepts easy to understand. The hands-on exercises reinforced my learning, allowing me to apply my skills effectively. Overall, it was an enriching experience that significantly boosted my programming confidence.

Aameshi Godara

4.0

★★★ ★ ☆

India

“Easy to Understand and Follow with Good Explanation”

The explanation was clear and easy to understand, making complex concepts accessible. The step-by-step breakdown of the code and correction of syntax errors were particularly helpful. This thorough and concise guide provided a solid grasp of how NumPy arrays work, making it ideal for learners of all levels.

EC23B1134 GURU PRASAD SUBRAMANIAN

5.0

★★★★ ★

India

“Comprehensive and Practical ML Learning Experience”

This course offered a well-structured curriculum covering both Python fundamentals and advanced machine learning concepts. The instructors explained complex topics with clarity, supported by hands-on assignments and quizzes that solidified my understanding. The practical examples and case studies helped me connect theory with real-world applications. Highly recommend it to aspiring ML professionals.

NITHIN KUMAR VIJAYAKUMAR

5.0

★★★★ ★

India

“Overall, I Had a Positive Learning Experience and Would Recommend This Course to Others”

Overall, I found the course to be well-structured and informative. The content was relevant and covered essential topics comprehensively. The lectures were clear, and the instructor did a great job explaining complex concepts and facilitating discussions.

Harshit Chugh

5.0

★★★★ ★

India

“The Course Was Well-Structured and Easy to Follow. The Instructor Was Knowledgeable and Engaging.”

The course was well-structured and easy to follow. The instructor was knowledgeable and engaging. The course material was relevant and up-to-date. The assignments were challenging but fair. The discussions were insightful and thought-provoking.

Stephanie Infante

4.0

★★★ ★ ☆

United States

“Python for Machine Learning is a Must!”

I was not expecting much from this course, but I was wrong. Everyone should take this course.

Divya DN

4.0

★★★ ★ ☆

India

“Efficient Array Operations and Data Manipulation”

Python's clear syntax and readability make it an ideal language for beginners and experienced developers alike. Its extensive ecosystem, including libraries like NumPy, Pandas, and Scikit-learn, provides a robust toolkit for data preprocessing, modeling, and evaluation. From simple linear regression to complex deep neural networks, Python can handle a wide range of machine learning tasks. Its versatility and scalability have made it the language of choice for many data scientists and researchers.

Meet your faculty

Meet industry experts who will teach you relevant skills in artificial intelligence

Bharani has been working in the field of data science for the last 2 years. He has expertise in languages such as Python, R and Java. He also has expertise in the field of deep learning and has worked with deep learning frameworks such as Keras and TensorFlow. He has been in the technical content side from last 2 years and has taught numerous classes with respect to data science.

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30+ years of experience in data science, ML, and analytics.
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Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

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30+ years of experience in data science, ML, and analytics.
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Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

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15+ years of industry experience in AI, machine learning, NLP
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Published researcher, speaker, and author of 'R Machine Learning Projects

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20+ years of expertise in AI, machine learning, and analytics
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Director - Academics at Great Learning

Frequently Asked Questions

What will I learn in these free machine learning courses?

These free Machine Learning courses online provide a comprehensive foundation in AI and data science. You will cover core concepts such as supervised, unsupervised, and reinforcement learning. Specifically, you will gain hands-on experience with algorithms like linear and logistic regression, decision trees, random forests, and k-means clustering. This structured approach makes them the best free machine learning courses for those wanting to bridge the gap between theory and real-world application.

Are these free machine learning courses online suitable for complete beginners?

Yes. We offer a best free machine learning course for beginners that starts with basic Python programming and essential statistics. You don't need a heavy coding background to start; the curriculum is designed to guide you from the ground up, making these free courses in machine learning accessible to students and career-switchers alike.

What specific technical skills will I gain from these free ml courses?

By enrolling in these free ml courses, you will acquire high-demand skills, including:

Data Preprocessing: Cleaning and structuring raw data for model training.
Supervised Learning: Building predictive models for classification and regression.
Unsupervised Learning: Discovering hidden patterns through clustering and dimensionality reduction.
Deep Learning: An introduction to neural networks and computer vision.
Model Evaluation: Using metrics like accuracy, precision, recall, and F1-score to tune performance.

Will I have lifetime access to these free Machine Learning courses with certificates?

Yes. You will have lifetime access to these courses after enrolling in them and access to certificates after completing the course.

Which tools and libraries are covered in the curriculum?

Our free machine learning courses online focus on industry-standard tools. You will learn to use Python as your primary language, along with powerful libraries such as NumPy and Pandas for data manipulation, Matplotlib and Seaborn for visualization, and Scikit-learn for implementing advanced ML algorithms.

Will I get a certificate after completing these free Machine Learning courses?

All courses are free, A certificate is available for a nominal fee upon successful completion of the course.

How long does it take to complete these free machine learning courses online?

Most of our high-impact modules range from 1.5 to 3 hours of video content. This "sprint-style" learning allows you to gain a specific, marketable skill, making these the best free machine learning courses for busy professionals.

How much do these free Machine Learning courses cost Online?

These are free courses; you can enroll and learn for free online.

Are the free machine learning courses self-paced?

Yes. Every course in the academy is entirely self-paced. Once you sign up, you get lifetime access to the video lectures and reading materials. This flexibility is perfect for anyone looking for free machine learning courses online that can be completed alongside a full-time job or university studies.

Do these courses include hands-on projects?

Absolutely. Practical application is a core focus. You will work on real-world datasets to solve problems such as predicting house prices, detecting fraudulent transactions, and segmenting customers for marketing. This hands-on experience ensures that our free courses in machine learning provide more than just theoretical knowledge.

Is there a specific machine learning course for healthcare or finance?

While the foundational courses are broad, the techniques you learn, such as predictive modeling and anomaly detection, are directly applicable to these sectors. Many learners use these free ml courses as a springboard to specialized roles in medical diagnostics or financial risk analysis.

Can I take multiple free ml courses at the same time?

Yes. You can enroll in as many courses as you wish. Many students choose to take a Python course alongside a Linear Regression module to strengthen their programming and mathematical foundations simultaneously.

Why take Machine Learning free courses from Great Learning Academy?

Great Learning Academy offers a wide range of high-quality, completely free Machine Learning courses. From beginner to advanced level, these free courses are designed to help you improve your Machine Learning and technology-related skills and achieve your goals. All these courses come with a certificate of completion, so you can demonstrate your new skills to the world. Start learning today and discover the benefits of free Machine Learning courses!

Who are eligible to take these free Machine Learning courses?

These courses have no prerequisites. Anybody can learn from these courses for free online.

What are the steps to enroll in these free Machine Learning courses?

To learn Machine Learning basics and advanced concepts from these courses, you need to,

Go to the course page
Click on the "Enroll for Free" button
Start learning the Machine Learning course for free online.

How do I progress from basic ML concepts to end-to-end AI projects?

Start working on projects where you clean data, train models, measure performance, and present results. The Post Graduate Program in Artificial Intelligence and Machine Learning offers guided projects and advanced study for learners ready to move beyond individual algorithms.