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

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Google Gemini Practical AI for Working Professionals

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Master Artificial Intelligence

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Master Data Science & Machine Learning in Python

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Master Python programming

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Machine Learning Essentials with Python

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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 96.8K+ learners 11 hrs

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Skills: Python, Statistics, Reinforcement learning, Machine learning

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

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Supervised Machine Learning with Logistic Regression and Naïve Bayes

4.43 21.9K+ learners 2 hrs

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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 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 77.6K+ 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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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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Artificial Intelligence and Machine Learning Projects

4.44 4.3K+ learners 1.5 hrs

Artificial Intelligence and Machine Learning Projects

Skills: Machine Learning Algorithms

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BASICS

4.49 32.3K+ 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

4.2 1.3K+ learners 7 hrs

Basics of Unsupervised Machine Learning

Skills: Basics of Python

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Python for Machine Learning

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4.57 96.8K+ 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

4.86 405 learners 12.5 hrs

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Supervised Machine Learning with Logistic Regression and Naïve Bayes

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Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

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Probability and Probability Distributions for Machine Learning

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

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Basics of Machine Learning

4.39 148.5K+ 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 77.6K+ learners 1 hr

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Google Gemini Practical AI for Working Professionals

4.83 145 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

4.44 4.3K+ learners 1.5 hrs

Skills: Machine Learning Algorithms

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Machine Learning Algorithms

4.49 32.3K+ learners 1.5 hrs

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

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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

Skills: Basics of Python

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Introduction to XGBoost

Skills: Python skills, Basic ML concepts

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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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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

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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

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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Python for Machine Learning and Data Science

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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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LDA in Entertainment Industry

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

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Machine Learning Landscape

Skills: Machine Learning Landscape

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

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Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

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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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Bias Variance Tradeoff

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

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Python for Machine Learning

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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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4.57 96.8K+ 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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4.58 43.7K+ 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 32.3K+ 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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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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4.2 1.3K+ learners 7 hrs

Basics of Unsupervised Machine Learning

Skills: Basics of Python

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4.66 716 learners 1.5 hrs

Introduction to XGBoost

Skills: Python skills, Basic ML concepts

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4.44 4.3K+ learners 1.5 hrs

Artificial Intelligence and Machine Learning Projects

Skills: Machine Learning Algorithms

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604 learners 2 hrs

Predictive Analytics for Machine Learning

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

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1.4K+ learners 2.5 hrs

Python IDEs for Machine Learning

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

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252 learners 1.5 hrs

Cross Validation in Machine Learning

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

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4.47 899 learners 1 hr

Feature Engineering for Machine Learning

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

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BASICS

4.65 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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4.65 10K+ 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

4.64 1K+ learners 1 hr

LDA in Entertainment Industry

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

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BASICS

4.63 3.9K+ 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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4.62 4.8K+ 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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BASICS

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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4.51 472.4K+ learners 1.5 hrs

Python for Machine Learning

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4.39 148.5K+ 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 96.8K+ learners 11 hrs

Machine Learning with Python

Skills: Python, Statistics, Reinforcement learning, Machine learning

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BASICS

4.46 77.6K+ learners 1 hr

Introduction to Machine Learning

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BASICS

4.58 43.7K+ 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 32.3K+ 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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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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Victor Binyi Tumenta

5.0

★★★★ ★

Germany

“Gained Foundational Knowledge of ML and Insights into Real-World Applications. Engaging!”

The "Introduction to Machine Learning" course offers comprehensive coverage of key concepts, including supervised/unsupervised learning and model evaluation. Complex topics are broken down into digestible modules, making it accessible to beginners, and the engaging instructor explains the concepts clearly.

Sai Kiran Palla

5.0

★★★★ ★

India

“Nice Explanation and Easy to Understand Even with Minimal Language Knowledge. Thank You!”

The Machine Learning classes are highly informative and engaging. The concepts are explained clearly and concisely, making them easy to understand even with minimal language knowledge. The practical examples and step-by-step guidance simplify complex topics effectively. Thank you for delivering such an excellent course that enhances both foundational and advanced knowledge in the fascinating field of machine learning!

LEX ANTHONY BOLOR

5.0

★★★★ ★

Philippines

“Introduction to Machine Learning is an Easy to Follow Course and I Enjoyed It”

In the machine learning course, I particularly enjoyed the hands-on experience of building and training models from scratch. The process of seeing a model evolve from a simple concept to a functional tool was both rewarding and fascinating. I was drawn to the power of algorithms to solve real-world problems, especially in areas like data analysis and pattern recognition. The discussions on neural networks and deep learning were especially engaging, as they opened my eyes to the potential of AI.

Omkar

5.0

★★★★ ★

India

“Overview of Fundamental Concepts, Techniques, and Practical Applications in Predictive Modeling, Data Processing, and Algorithm Implementation”

I liked how the beginner's machine learning course on Great Learning broke down complex concepts into simple, easy-to-understand explanations. The structured approach really helped me build my understanding step by step. I also appreciated the hands-on projects and practical examples, which made the concepts more relatable. The quizzes and assignments were great for reinforcing what I learned, and I found the real-world applications of algorithms in predictive modeling particularly interesting.

Tharun NS

5.0

★★★★ ★

India

“My Journey into Machine Learning: Insights, Challenges, and Skills Gained from the Introduction to Machine Learning Course”

My journey through the Introduction to Machine Learning course was transformative. I delved into the foundational concepts of algorithms, data processing, and model evaluation. Each module challenged me to think critically and apply what I learned through hands-on projects. I gained practical skills in Python and explored various techniques like regression, classification, and clustering. The experience not only enhanced my understanding of machine learning but also ignited my passion for data-driven problem-solving. I'm excited to apply these skills in real-world scenarios.

MOHAMMAD ASIF NASIM

5.0

★★★★ ★

India

“The Introduction to Machine Learning Course is Insane”

Machine Learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn from data, improve over time, and make predictions or decisions without being explicitly programmed. It focuses on developing algorithms that can identify patterns and relationships in data, allowing computers to perform tasks such as classification, prediction, and clustering.

Ramya P

5.0

★★★★ ★

India

“Introduction to Machine Learning Steps of Machine Learning Hackathon”

The Machine Learning course provided an excellent foundation in key concepts and techniques. It covered essential topics like supervised and unsupervised learning, neural networks, and model evaluation. The content was well-structured, balancing theory with hands-on practice using real-world datasets. The instructor explained complex ideas clearly, making the course suitable for beginners while still valuable for advanced learners. Tools like Python and libraries such as Scikit-learn and TensorFlow were effectively introduced.

Muzammil Mohammed

5.0

★★★★ ★

India

“One of the Best Courses Ever on Machine Learning”

This machine learning course was a treasure trove of insights. I loved the blend of theory and hands-on practice, real-world applications, and the engaging teaching style. The projects, in particular, were super helpful in solidifying the concepts. Overall, a fantastic learning experience!

Suraj Gaikwad

5.0

★★★★ ★

India

“Great Way to Gain Knowledge About Machine Learning”

Completing an Introduction to Machine Learning course provided a solid foundation in understanding the key concepts and techniques used in the field. The course covered essential topics such as supervised and unsupervised learning, data preprocessing, model selection, and evaluation metrics.

ZUL KHARNAIN PASHA

5.0

★★★★ ★

India

“Learning the Importance of Clean, Balanced, and Diverse Datasets Through Hands-On Experiments”

Effective feedback in machine learning involves evaluating the quality of the data, the model's performance, and its ability to generalize. Start by ensuring the dataset is clean, consistent, and balanced, as poor data quality can significantly impact results. Assess model performance using metrics like accuracy, precision, recall, or RMSE, and analyze confusion matrices to understand prediction errors.

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.

Free Machine Learning Courses

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Skills You’ll Gain in These Best Free Machine Learning Courses

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Learner reviews of the Free Machine Learning Courses

Meet your faculty

Mr. Bharani Akella

Dr. Abhinanda Sarkar

Dr. Abhinanda Sarkar

Dr. Sunil Kumar

Prof. Mukesh Rao

Frequently Asked Questions

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