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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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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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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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

4.31 4K+ learners 2 hrs

Mathematics for Machine Learning

Skills: Chain Rule, Introduction to Functions, Line Concept, Maxima and Minima of a function, Lies Planes and Hyperplanes

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BASICS

4.33 5.7K+ learners 1.5 hrs

Introduction to Scikit Learn

Skills: Scikit learn, Installing Scikit learn

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BASICS

4.43 2.3K+ learners 1 hr

Supervised Machine Learning Tutorial

Skills: Supervised Machine Learning, Linear Regression, Characteristics of Supervised Machine Learning

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PRO

1 coding exercise 1 project

Machine Learning Essentials with Python

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BASICS

Supervised Machine Learning with Tree Based Models

4.56 9.9K+ learners 2 hrs

Supervised Machine Learning with Tree Based Models

Skills: Scikit Learn Library, Decision Tree, Random Forest, Demonstration for Algorithms

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BASICS

Machine Learning Model Deployment using Flask

4.34 9.7K+ learners 1 hr

Machine Learning Model Deployment using Flask

Skills: Flask,Model Deployment

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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

4.63 3.9K+ learners 1.5 hrs

Machine Learning Landscape

Skills: Machine Learning Landscape

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BASICS

4.53 3.2K+ learners 1 hr

Support Vector Machines

Skills: Introduction to Machine Learning, Kernel Functions, SVM Demo in Python

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BASICS

4.62 4.8K+ learners 2.5 hrs

Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

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BASICS

4.53 4.5K+ learners 2 hrs

Machine Learning Pipeline

Skills: Introduction to Machine Learning, Understanding the ML Pipeline, Data Preparation, Formatting Data, Data Transformation, Building ML models, Analyzing ML models

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BASICS

4.65 716 learners 1.5 hrs

Introduction to XGBoost

Skills: Python skills, Basic ML concepts

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BASICS

Mathematics for Machine Learning

4.31 4K+ learners 2 hrs

Skills: Chain Rule, Introduction to Functions, Line Concept, Maxima and Minima of a function, Lies Planes and Hyperplanes

BASICS

Introduction to Scikit Learn

4.33 5.7K+ learners 1.5 hrs

Skills: Scikit learn, Installing Scikit learn

BASICS

Supervised Machine Learning Tutorial

4.43 2.3K+ learners 1 hr

Skills: Supervised Machine Learning, Linear Regression, Characteristics of Supervised Machine Learning

PRO

Machine Learning Essentials with Python

4.8 1.5K+ learners 12 hrs

BASICS

Supervised Machine Learning with Tree Based Models

4.56 9.9K+ learners 2 hrs

Skills: Scikit Learn Library, Decision Tree, Random Forest, Demonstration for Algorithms

BASICS

Machine Learning Model Deployment using Flask

4.34 9.7K+ learners 1 hr

Skills: Flask,Model Deployment

BASICS

Regression Analysis Using R

4.53 30.4K+ learners 2.5 hrs

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

BASICS

Machine Learning Landscape

4.63 3.9K+ learners 1.5 hrs

Skills: Machine Learning Landscape

BASICS

Support Vector Machines

4.53 3.2K+ learners 1 hr

Skills: Introduction to Machine Learning, Kernel Functions, SVM Demo in Python

BASICS

Machine Learning Modelling

4.62 4.8K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

BASICS

Machine Learning Pipeline

4.53 4.5K+ learners 2 hrs

Skills: Introduction to Machine Learning, Understanding the ML Pipeline, Data Preparation, Formatting Data, Data Transformation, Building ML models, Analyzing ML models

BASICS

Introduction to XGBoost

4.65 716 learners 1.5 hrs

Skills: Python skills, Basic ML concepts

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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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BASICS

4.65 716 learners 1.5 hrs

Introduction to XGBoost

Skills: Python skills, Basic ML concepts

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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

4.5 3.3K+ learners 1.5 hrs

Machine Learning Projects

Skills: Exploratory Data Analysis, Machine Learning Algorithms

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BASICS

4.31 4K+ learners 2 hrs

Mathematics for Machine Learning

Skills: Chain Rule, Introduction to Functions, Line Concept, Maxima and Minima of a function, Lies Planes and Hyperplanes

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BASICS

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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BASICS

4.48 7.4K+ learners 1 hr

Data Preparation for Machine Learning

Skills: Data Leakage, Data Balancing, K-fold Cross Validation, Model Building

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BASICS

4.54 1.6K+ learners 1 hr

Feature Engineering Importance

Skills: Feature Engineering, Feature Selection

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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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BASICS

4.62 4.8K+ 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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BASICS

4.56 2.3K+ learners 0.5 hr

Support Vector Machine in Hindi

Skills: Support Vector Machine - in Hindi

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BASICS

4.51 472.6K+ 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

4.39 148.6K+ 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

4.58 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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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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BASICS

Probability and Probability Distributions for Machine Learning

4.49 20.8K+ learners 1.5 hrs

Probability and Probability Distributions for Machine Learning

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

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New

BASICS

4.2 1.3K+ learners 7 hrs

Basics of Unsupervised Machine Learning

Skills: Basics of Python

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BASICS

4.65 716 learners 1.5 hrs

Introduction to XGBoost

Skills: Python skills, Basic ML concepts

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BASICS

4.44 4.3K+ learners 1.5 hrs

Artificial Intelligence and Machine Learning Projects

Skills: Machine Learning Algorithms

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BASICS

4.5 3.3K+ learners 1.5 hrs

Machine Learning Projects

Skills: Exploratory Data Analysis, Machine Learning Algorithms

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BASICS

4.31 4K+ learners 2 hrs

Mathematics for Machine Learning

Skills: Chain Rule, Introduction to Functions, Line Concept, Maxima and Minima of a function, Lies Planes and Hyperplanes

View Course

BASICS

1.4K+ learners 2.5 hrs

Python IDEs for Machine Learning

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

View Course

BASICS

4.48 7.4K+ learners 1 hr

Data Preparation for Machine Learning

Skills: Data Leakage, Data Balancing, K-fold Cross Validation, Model Building

View Course

BASICS

4.54 1.6K+ learners 1 hr

Feature Engineering Importance

Skills: Feature Engineering, Feature Selection

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Trending

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

View Course

BASICS

4.62 4.8K+ learners 2.5 hrs

Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

View Course

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

View Course

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?

View Course

BASICS

4.56 2.3K+ learners 0.5 hr

Support Vector Machine in Hindi

Skills: Support Vector Machine - in Hindi

View Course

Popular

BASICS

4.51 472.6K+ learners 1.5 hrs

Python for Machine Learning

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BASICS

4.39 148.6K+ 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

View Course

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

View Course

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

View Course

BASICS

4.53 30.4K+ learners 2.5 hrs

Regression Analysis Using R

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

View Course

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

View Course

BASICS

4.49 20.8K+ learners 1.5 hrs

Probability and Probability Distributions for Machine Learning

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

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Learner reviews of the Free Machine Learning Courses

Our learners share their experiences of our courses

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Abishek SK (RA2111003040136)

4.0

★★★ ★ ☆

India

“The Hands-On Projects and Real-World Datasets Provided Excellent Practical Experience”

This course was a great introduction to Python for Machine Learning. I really enjoyed the clear explanations of core concepts like supervised learning, unsupervised learning, and deep learning. The hands-on labs with libraries like TensorFlow, Keras, and Scikit-learn helped solidify my understanding. I also appreciated the focus on real-world applications and the opportunities to work on projects that applied these techniques to real datasets. Overall, it's a great course for anyone looking to build a strong foundation in machine learning with Python.

Madhu Yadav

4.0

★★★ ★ ☆

India

“Python for Machine Learning (NumPy and Pandas)”

The "Python for Machine Learning" course at Great Learning offers a thorough introduction to key machine learning concepts and practical applications. The course content is well-structured, covering essential topics such as data preprocessing, feature engineering, and model evaluation. The hands-on projects and coding exercises provide valuable practical experience, reinforcing theoretical knowledge. The instructor's clear explanations and real-world examples greatly enhance the learning experience.

Mouli Gunisetti

5.0

★★★★ ★

India

“Comprehensive Introduction to Pandas and NumPy in Machine Learning”

I recently completed the Python for Machine Learning course with Great Learning, and I found it very informative and practical. The introduction to pandas and NumPy was particularly helpful, and the exercises gave me a solid hands-on experience with these libraries. The instructors were clear and made the concepts easy to understand. However, I would have appreciated more complex real-world examples in the exercises. Overall, it was a great learning experience!

Suvedha Balachandran

5.0

★★★★ ★

India

“Python for Machine Learning by Great Learning”

The "Python for Machine Learning" course at Great Learning offers an excellent foundation for anyone looking to explore the world of machine learning. The course provides a deep dive into Python programming, specifically tailored to the needs of machine learning applications. It covers key concepts such as data preprocessing, model building, and evaluating machine learning algorithms, using libraries like Pandas and NumPy. The course structure is well-organized, making it ideal for both beginners and those with some prior experience in Python.

windi ahmad

5.0

★★★★ ★

Indonesia

“Instructor, Topic Depth, Easy to Follow”

The instructor demonstrated a strong understanding of the topic, presenting complex concepts in a clear and engaging manner. The depth of coverage allowed for a thorough exploration of the subject, while the pacing was well-suited for learners at various levels. The use of practical examples and visual aids made the material easy to follow, enhancing comprehension. Overall, the instructor's ability to break down intricate ideas into digestible segments contributed significantly to a positive learning experience.

Dante Det

5.0

★★★★ ★

United States

“The Python for Machine Learning Certification is an Excellent Resource for Anyone Looking to Break into the Field of Machine Learning with a Strong Foundation in Python”

This certification strikes a great balance between theory and application, making it ideal for students, professionals, and anyone seeking to understand machine learning concepts through Python. While there’s room for improvement, the course delivers significant value and is a worthy investment in building foundational skills for a career in AI or data science.

Shannon Booth

5.0

★★★★ ★

United States

“Gaining a better understanding of both Numpy and Pandas”

I loved that I could take this course at my own pace, and the added notes feature to be able to type notes for myself along with the instructional videos as they were playing is very useful for me. I am the type that definitely learns through repetition and writing or typing things out for myself and being able to do that on the same screen while the video was playing helped to reinforce everything that was being discussed.

Jay Patel

5.0

★★★★ ★

United States

“Learning fundamentals of numpy and pandas the easy way”

Learning basic structure of python has many videos online, but this course was detailed about all pandas and numpy. It was great learning experiance.

727822TUAM040 PRAVEEN.A

5.0

★★★★ ★

India

“"Python for Machine Learning - Course Review”

The "Python for Machine Learning" course offers a comprehensive introduction to the key concepts and tools used in machine learning. It covers fundamental Python libraries such as pandas, NumPy, and scikit-learn, with hands-on examples to reinforce learning. The course excels at making complex topics like model training, evaluation, and data preprocessing accessible for beginners. Overall, it's a well-structured course that effectively bridges the gap between Python programming and machine learning applications, providing a solid foundation for further exploration in the fi

Vanessa Diaz

5.0

★★★★ ★

United States

“Python for Machine Learning Certification ”

The video lengths are suitable and manageable.Moreover, I genuinely enjoyed the instructor's explanations, as they were clear and insightful. He showed great examples that illustrated the concepts perfectly and provided the practical insights I was hoping to see. This approach made the learning experience both engaging and informative.

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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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,