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

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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PRO

136 coding exercises 6 projects

Master Data Science & Machine Learning in Python

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PRO

51 coding exercises 3 projects

Master Python programming

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PRO

1 coding exercise 1 project

Machine Learning Essentials with Python

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BASICS

4.64 1K+ Learners 1 hr

LDA in Entertainment Industry

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

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BASICS

4.61 2.2K+ Learners 1 hr

Introduction to Supervised Learning

Skills: Machine Learning, Supervised Learning

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BASICS

IPL Winner Prediction using Machine Learning

4.39 2.5K+ Learners 1 hr

IPL Winner Prediction using Machine Learning

Skills: Hands-on of IPL dataset

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BASICS

4.43 3.6K+ Learners 1.5 hrs

Decision Tree

Skills: Entropy, Heterogeneity, Shannon's Entropy, Preventing Overfitting

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BASICS

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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BASICS

Feature Engineering for Machine Learning

4.47 920 Learners 1 hr

Feature Engineering for Machine Learning

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

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BASICS

4.49 1.5K+ Learners 1 hr

Random Forest Regression

Skills: Random Forest Regression, Hands-on, Logistic Regression vs Random Forest , Linear Regression vs Random Forest

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BASICS

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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BASICS

4.59 1.3K+ Learners 0.5 hr

Bias Variance Tradeoff

Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?

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BASICS

Linear Discriminant Analysis Applications

4.42 2.7K+ Learners 1 hr

Linear Discriminant Analysis Applications

Skills: Feature Selection, Linear Discriminant Analysis with Python

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BASICS

4.52 522 Learners 1 hr

KNN and Naive Bayes Algorithm

Skills: K-Nearest Neighbor Algorithm,Naive Bayes Algorithm

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BASICS

4.37 1.4K+ Learners 2 hrs

Stochastic Gradient Descent

Skills: Objective of Gradient Descent, Gradient Descent Algorithm, Stochastic Gradient Descent, Stochastic Gradient Descent Working, Advantages and Disadvantages of Stochastic Gradient Descent,

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BASICS

LDA in Entertainment Industry

4.64 1K+ Learners 1 hr

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

BASICS

Introduction to Supervised Learning

4.61 2.2K+ Learners 1 hr

Skills: Machine Learning, Supervised Learning

BASICS

IPL Winner Prediction using Machine Learning

4.39 2.5K+ Learners 1 hr

Skills: Hands-on of IPL dataset

BASICS

Decision Tree

4.43 3.6K+ Learners 1.5 hrs

Skills: Entropy, Heterogeneity, Shannon's Entropy, Preventing Overfitting

BASICS

k-fold Cross Validation

4.61 1.8K+ Learners 1 hr

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

BASICS

Feature Engineering for Machine Learning

4.47 920 Learners 1 hr

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

BASICS

Random Forest Regression

4.49 1.5K+ Learners 1 hr

Skills: Random Forest Regression, Hands-on, Logistic Regression vs Random Forest , Linear Regression vs Random Forest

BASICS

Cross Validation in Machine Learning

4.64 256 Learners 1.5 hrs

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

BASICS

Bias Variance Tradeoff

4.59 1.3K+ Learners 0.5 hr

Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?

BASICS

Linear Discriminant Analysis Applications

4.42 2.7K+ Learners 1 hr

Skills: Feature Selection, Linear Discriminant Analysis with Python

BASICS

KNN and Naive Bayes Algorithm

4.52 522 Learners 1 hr

Skills: K-Nearest Neighbor Algorithm,Naive Bayes Algorithm

BASICS

Stochastic Gradient Descent

4.37 1.4K+ Learners 2 hrs

Skills: Objective of Gradient Descent, Gradient Descent Algorithm, Stochastic Gradient Descent, Stochastic Gradient Descent Working, Advantages and Disadvantages of Stochastic Gradient Descent,

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BASICS

4.2 1.3K+ Learners 7 hrs

Basics of Unsupervised Machine Learning

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4.65 738 Learners 1.5 hrs

Introduction to XGBoost

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Artificial Intelligence and Machine Learning Projects

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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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4.5 1.5K+ Learners 2.5 hrs

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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Feature Engineering for Machine Learning

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

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Applications of Data Science & Machine Learning

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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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LDA in Entertainment Industry

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Python for Machine Learning and Data Science

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

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Machine Learning Landscape

Skills: Machine Learning Landscape

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Bagging and Boosting

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

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4.62 4.9K+ Learners 2.5 hrs

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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4.61 2.2K+ Learners 1 hr

Introduction to Supervised Learning

Skills: Machine Learning, Supervised Learning

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4.59 1.3K+ Learners 0.5 hr

Bias Variance Tradeoff

Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?

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4.51 475K+ 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

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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

4.57 98.2K+ Learners 11 hrs

Machine Learning with Python

Skills: Python, Statistics, Reinforcement learning, Machine learning

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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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BASICS

4.58 43.9K+ Learners 2 hrs

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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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

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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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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4.2 1.3K+ Learners 7 hrs

Basics of Unsupervised Machine Learning

Skills: Basics of Python

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BASICS

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Introduction to XGBoost

Skills: Python skills, Basic ML concepts

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4.45 4.5K+ Learners 1.5 hrs

Artificial Intelligence and Machine Learning Projects

Skills: Machine Learning Algorithms

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BASICS

631 Learners 2 hrs

Predictive Analytics for Machine Learning

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

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BASICS

4.5 1.5K+ Learners 2.5 hrs

Python IDEs for Machine Learning

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

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BASICS

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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BASICS

4.47 920 Learners 1 hr

Feature Engineering for Machine Learning

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

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BASICS

4.66 1.2K+ Learners 1 hr

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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LDA in Entertainment Industry

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

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4.64 10.1K+ Learners 3 hrs

Python for Machine Learning and Data Science

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

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BASICS

4.63 4K+ Learners 1.5 hrs

Machine Learning Landscape

Skills: Machine Learning Landscape

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BASICS

4.62 2K+ Learners 1 hr

Bagging and Boosting

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

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BASICS

4.62 4.9K+ Learners 2.5 hrs

Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

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BASICS

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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BASICS

4.61 2.2K+ Learners 1 hr

Introduction to Supervised Learning

Skills: Machine Learning, Supervised Learning

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BASICS

4.59 1.3K+ Learners 0.5 hr

Bias Variance Tradeoff

Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?

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Popular

BASICS

4.51 475K+ Learners 1.5 hrs

Python for Machine Learning

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BASICS

4.39 149.5K+ 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 98.2K+ Learners 11 hrs

Machine Learning with Python

Skills: Python, Statistics, Reinforcement learning, Machine learning

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BASICS

4.46 77.9K+ Learners 1 hr

Introduction to Machine Learning

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BASICS

4.58 43.9K+ Learners 2 hrs

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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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

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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BASICS

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

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01fe22bec195

4.0

★★★ ★ ☆

India

“Smooth Sailing Through the Easy Flow of the Course”

The course was incredibly well-structured and easy to follow. The content was presented in a logical sequence, which made grasping each concept straightforward and intuitive. The explanations were clear and concise, and the pace was just right—neither too fast nor too slow. Each module built seamlessly on the previous one, reinforcing learning and making complex topics more accessible. Overall, the smooth flow of the course significantly enhanced my understanding and kept me engaged throughout.

Hemant verma

5.0

★★★★ ★

India

“Excellent Python Data Science Fundamentals Course”

The course covers NumPy and Pandas concepts very effectively. The quiz questions are well-structured and test practical knowledge. Topics like ndarray, DataFrame operations, iloc/loc indexing, and array manipulation are explained clearly. Highly recommended for beginners looking to build a strong foundation in Python data science.

Mohanmed Arif S

5.0

★★★★ ★

India

“Engaging Content, Well-Structured, and Highly Informative!”

This course is incredibly well-structured, providing clear and concise explanations that are easy to follow. The content is both engaging and informative, making complex topics understandable. The instructor's expertise is evident, and the practical examples help solidify the concepts. Overall, it's a valuable learning experience that I highly recommend.

SITS Yashoda Beladar

4.0

★★★ ★ ☆

India

“Comprehensive Machine Learning Course Utilizing Python”

Here's a short example of experience in Python for a machine learning course: Utilized Python to develop and deploy machine learning models, leveraging libraries like NumPy, Pandas, and scikit-learn to preprocess and visualize data, implement supervised and unsupervised learning algorithms, and evaluate model performance using metrics and cross-validation.

Ekalavya Kumar

5.0

★★★★ ★

India

“Transformative Learning: My Satisfying Experience with an Engaging Online Class”

Online learning in machine learning offers a flexible and accessible way to master complex concepts and techniques. With numerous platforms providing courses, tutorials, and hands-on projects, learners can engage at their own pace. This mode of education fosters collaboration through forums and discussion groups, enhancing understanding. However, the lack of in-person interaction can sometimes challenge motivation. Overall, online learning is an invaluable resource for anyone looking to deepen their knowledge in the rapidly evolving field of machine learning.

ANAND CH

5.0

★★★★ ★

India

“Comprehensive Foundation in Python Programming”

The engaging content, hands-on exercises, and supportive instructors make it an excellent choice for beginners and those looking to enhance their skills. Whether you’re diving into data science, web development, or automation, this course equips you with the tools to succeed.

Revathy Jess

5.0

★★★★ ★

India

“Hands-On Approach and Interactive Sessions”

I really appreciated how the course focused on practical applications and real-world scenarios. The clarity of explanations and step-by-step guidance made complex topics much easier to grasp. Additionally, the opportunity to collaborate with peers and discuss ideas helped deepen my understanding and made the learning experience enjoyable.

S Hemachandran

5.0

★★★★ ★

India

“Good to Learn This Course for Detailed ML in Python”

Machine learning (ML) in Python leverages libraries like scikit-learn, TensorFlow, and PyTorch to build models for tasks such as classification, regression, and clustering. Python's rich ecosystem simplifies data manipulation, model training, and evaluation, making it a popular choice for developing and deploying ML solutions.

Facundo Leonel Porcel de Peralta

5.0

★★★★ ★

“Great Start to Machine Learning Using Python”

This course provides you with the essential tools to kickstart your Machine Learning journey using Python in a dynamic and practical way. Perfect for those looking for an interactive approach from day one.

MOHAMADU RIYAS S

5.0

★★★★ ★

India

“Introduction to Machine Learning: A Practical Approach”

This course is very helpful in gaining a foundational understanding of machine learning. The content is easy to follow, and the practical examples are great for applying the concepts. I would love to see more courses like this to help students explore new topics and improve their skills.

Meet your faculty

Meet industry experts who will teach you relevant skills in Machine Learning

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.