• star

    4.6

  • star

    4.89

  • star

    4.94

  • star

    4.7

  • star

    4.6

  • star

    4.89

  • star

    4.94

  • star

    4.7

Free Machine Learning Courses

img icon BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 21.8K+ learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

img icon BASICS
Machine Learning with Python
star   4.57 96.5K+ learners 11 hrs

Skills: Python, Statistics, Reinforcement learning, Machine learning

img icon BASICS
Probability and Probability Distributions for Machine Learning
star   4.49 20.7K+ learners 1.5 hrs

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

img icon BASICS
Basics of Machine Learning
star   4.39 148.3K+ learners 2.5 hrs

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

img icon BASICS
Predictive Analytics
star   4.44 3.8K+ learners 1.5 hrs

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

img icon BASICS
Artificial Intelligence and Machine Learning Projects
star   4.44 4.2K+ learners 1.5 hrs

Skills: Machine Learning Algorithms

img icon BASICS
Machine Learning Algorithms
star   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

img icon BASICS
Basics of Unsupervised Machine Learning
star   4.2 1.3K+ learners 7 hrs

Skills: Basics of Python

img icon BASICS
Advanced Statistics for Machine Learning
star   4.49 11.4K+ learners 6 hrs

Skills: Advanced Statistics, Hypothesis testing, Type-I and Type-II error

img icon BASICS
Customer Segmentation using Clustering
star   4.49 1.9K+ learners 1 hr

Skills: Clustering Techniques like KMeans, DBSCAN, MeanShift, Agglomerative

img icon BASICS
Data Preparation for Machine Learning
star   4.48 7.4K+ learners 1 hr

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

img icon BASICS
Excel Regression Analysis
partner logo
star   4.47 19.1K+ learners 1 hr

Skills: Hands on knowledge of Regression Analysis using Excel

free icon BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

free icon BASICS
Machine Learning with Python
star   4.57 96.5K+ learners 11 hrs

Skills: Python, Statistics, Reinforcement learning, Machine learning

free icon BASICS
Probability and Probability Distributions for Machine Learning

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

free icon BASICS
Basics of Machine Learning
star   4.39 148.3K+ learners 2.5 hrs

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

free icon BASICS
Predictive Analytics
star   4.44 3.8K+ learners 1.5 hrs

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

free icon BASICS
Artificial Intelligence and Machine Learning Projects

Skills: Machine Learning Algorithms

free icon BASICS
Machine Learning Algorithms
star   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

free icon BASICS
Basics of Unsupervised Machine Learning
star   4.2 1.3K+ learners 7 hrs

Skills: Basics of Python

free icon BASICS
Advanced Statistics for Machine Learning
star   4.49 11.4K+ learners 6 hrs

Skills: Advanced Statistics, Hypothesis testing, Type-I and Type-II error

free icon BASICS
Customer Segmentation using Clustering
star   4.49 1.9K+ learners 1 hr

Skills: Clustering Techniques like KMeans, DBSCAN, MeanShift, Agglomerative

free icon BASICS
Data Preparation for Machine Learning
star   4.48 7.4K+ learners 1 hr

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

free icon BASICS
Excel Regression Analysis
star   4.47 19.1K+ learners 1 hr

Skills: Hands on knowledge of Regression Analysis using Excel

Learn Machine Learning for Free

These free machine learning courses online give you a practical learning path from data preparation to model building with Python. You learn how to prevent data leakage, balance datasets, and use k-fold cross-validation, then build strong fundamentals with NumPy arrays and operations, Pandas dataframes, and core data manipulation. You also strengthen EDA and visualization skills using Matplotlib, Seaborn, Plotly, SciPy, and scikit learn, backed by statistics and probability for better evaluation.

Starting with data preparation, you will learn data leakage checks, data balancing, and k-fold cross-validation, then use NumPy and Pandas for data manipulation and exploration. You will build a strong foundation in statistics and probability, then train models such as linear regression, logistic regression, Naive Bayes, decision trees, random forests, SVMs, and k-means clustering using scikit learn. You will also learn visualization with Matplotlib, Seaborn, and Plotly, work with SciPy tools, complete prediction and EDA projects, and deploy a model using Flask, building skills that prepare you for neural networks, natural language processing, and TensorFlow workflows.

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

  • Machine Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines.

  • Programming and Libraries: Python, NumPy, Pandas, scikit learn, and TensorFlow.

  • Modeling and Evaluation: Data preprocessing, Model training, Model validation, and Performance evaluation metrics.

  • Project and Delivery Skills: Build and test machine learning models, Iterate and improve model performance.

  • Core Foundations: Neural networks fundamentals, and Natural language processing fundamentals.
down arrow img

Get started with these courses

img icon BASICS
Basics of Unsupervised Machine Learning
star   4.2 1.3K+ learners 7 hrs

Skills: Basics of Python

img icon BASICS
Introduction to XGBoost
star   4.66 710 learners 1.5 hrs

Skills: Python skills, Basic ML concepts

img icon BASICS
Artificial Intelligence and Machine Learning Projects
star   4.44 4.2K+ learners 1.5 hrs

Skills: Machine Learning Algorithms

img icon BASICS
Machine Learning Projects
star   4.5 3.3K+ learners 1.5 hrs

Skills: Exploratory Data Analysis, Machine Learning Algorithms

img icon BASICS
Mathematics for Machine Learning
star   4.31 4K+ learners 2 hrs

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

img icon BASICS
Python IDEs for Machine Learning
1.4K+ learners 2.5 hrs

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

img icon BASICS
Data Preparation for Machine Learning
star   4.48 7.4K+ learners 1 hr

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

img icon BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

img icon BASICS
LDA in Entertainment Industry
star   4.64 1K+ learners 1 hr

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

img icon BASICS
Machine Learning Landscape
star   4.63 3.9K+ learners 1.5 hrs

Skills: Machine Learning Landscape

img icon BASICS
Bagging and Boosting
star   4.62 2K+ learners 1 hr

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

img icon BASICS
Machine Learning Modelling
star   4.62 4.8K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

img icon BASICS
k-fold Cross Validation
star   4.61 1.8K+ learners 1 hr

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

img icon BASICS
Introduction to Supervised Learning
star   4.61 2.2K+ learners 1 hr

Skills: Machine Learning, Supervised Learning

img icon BASICS
Bias Variance Tradeoff
star   4.59 1.3K+ learners 0.5 hr

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

img icon BASICS
Support Vector Machine in Hindi
star   4.56 2.3K+ learners 0.5 hr

Skills: Support Vector Machine - in Hindi

img icon BASICS
Basics of Machine Learning
star   4.39 148.3K+ learners 2.5 hrs

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

img icon BASICS
Machine Learning with Python
star   4.57 96.5K+ learners 11 hrs

Skills: Python, Statistics, Reinforcement learning, Machine learning

img icon BASICS
Statistics for Machine Learning
star   4.58 43.7K+ learners 2 hrs

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

img icon BASICS
Machine Learning Algorithms
star   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

img icon BASICS
Regression Analysis Using R
star   4.53 30.3K+ learners 2.5 hrs

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

img icon BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 21.8K+ learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

img icon BASICS
Probability and Probability Distributions for Machine Learning
star   4.49 20.7K+ learners 1.5 hrs

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

img icon BASICS
Excel Regression Analysis
partner logo
star   4.47 19.1K+ learners 1 hr

Skills: Hands on knowledge of Regression Analysis using Excel

New

img icon BASICS
Basics of Unsupervised Machine Learning
star   4.2 1.3K+ learners 7 hrs

Skills: Basics of Python

img icon BASICS
Introduction to XGBoost
star   4.66 710 learners 1.5 hrs

Skills: Python skills, Basic ML concepts

img icon BASICS
Artificial Intelligence and Machine Learning Projects
star   4.44 4.2K+ learners 1.5 hrs

Skills: Machine Learning Algorithms

img icon BASICS
Machine Learning Projects
star   4.5 3.3K+ learners 1.5 hrs

Skills: Exploratory Data Analysis, Machine Learning Algorithms

img icon BASICS
Mathematics for Machine Learning
star   4.31 4K+ learners 2 hrs

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

img icon BASICS
Python IDEs for Machine Learning
1.4K+ learners 2.5 hrs

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

img icon BASICS
Data Preparation for Machine Learning
star   4.48 7.4K+ learners 1 hr

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

img icon BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

Trending

img icon BASICS
LDA in Entertainment Industry
star   4.64 1K+ learners 1 hr

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

img icon BASICS
Machine Learning Landscape
star   4.63 3.9K+ learners 1.5 hrs

Skills: Machine Learning Landscape

img icon BASICS
Bagging and Boosting
star   4.62 2K+ learners 1 hr

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

img icon BASICS
Machine Learning Modelling
star   4.62 4.8K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

img icon BASICS
k-fold Cross Validation
star   4.61 1.8K+ learners 1 hr

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

img icon BASICS
Introduction to Supervised Learning
star   4.61 2.2K+ learners 1 hr

Skills: Machine Learning, Supervised Learning

img icon BASICS
Bias Variance Tradeoff
star   4.59 1.3K+ learners 0.5 hr

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

img icon BASICS
Support Vector Machine in Hindi
star   4.56 2.3K+ learners 0.5 hr

Skills: Support Vector Machine - in Hindi

Popular

img icon BASICS
Basics of Machine Learning
star   4.39 148.3K+ learners 2.5 hrs

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

img icon BASICS
Machine Learning with Python
star   4.57 96.5K+ learners 11 hrs

Skills: Python, Statistics, Reinforcement learning, Machine learning

img icon BASICS
Statistics for Machine Learning
star   4.58 43.7K+ learners 2 hrs

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

img icon BASICS
Machine Learning Algorithms
star   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

img icon BASICS
Regression Analysis Using R
star   4.53 30.3K+ learners 2.5 hrs

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

img icon BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 21.8K+ learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

img icon BASICS
Probability and Probability Distributions for Machine Learning
star   4.49 20.7K+ learners 1.5 hrs

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

img icon BASICS
Excel Regression Analysis
partner logo
star   4.47 19.1K+ learners 1 hr

Skills: Hands on knowledge of Regression Analysis using Excel

Our learners also choose

Learner reviews of the Free Machine Learning Courses

Our learners share their experiences of our courses

4.47
68%
22%
6%
1%
2%
Reviewer Profile
MUBASHIR HABIB

5.0

“Comprehensive Overview of Supervised Machine Learning with Logistic Regression and Naïve Bayes”
Comprehensive Overview of Supervised Machine Learning with Logistic Regression and Naïve Bayes. The course on Supervised Machine Learning with Logistic Regression and Naïve Bayes offers a thorough and engaging exploration of these fundamental algorithms. It effectively balances theoretical concepts with practical applications, making it suitable for both beginners and those looking to deepen their understanding. **Key Highlights:** 1. **Clear Explanations:** The course provides clear and concise explanations of logistic regression and Naïve Bayes, breaking down complex concepts into easily digestible parts. 2. **Practical Examples:** Real-world examples and hands-on exercises help reinforce the learning, allowing students to apply the concepts to practical scenarios. 3. **Interactive Quizzes:** The quizzes are well-designed to test understanding and reinforce key concepts, ensuring that learners can assess their progress effectively. 4. **Comprehensive Content:** The course covers all essential aspects, from the assumptions and applications of each algorithm to the evaluation metrics used to assess their performance. 5. **Supportive Resources:** Additional resources and references are provided for those who wish to delve deeper into specific topics. Overall, this course is an excellent resource for anyone interested in mastering supervised machine learning techniques, particularly logistic regression and Naïve Bayes. It equips learners with the knowledge and skills needed to implement these algorithms confidently in various machine learning projects.
Reviewer Profile

5.0

“Really enjoyed the lesson. It was easy to follow and well-structured”
I really enjoyed the lesson. It was easy to follow and well-structured, making the concepts clear and understandable. The explanations were concise, and the examples helped reinforce the material. Overall, it was an engaging and informative session that kept me interested throughout.
Reviewer Profile

4.0

Country Flag Nigeria
“It was fascinating and compelling.”
It was great learning machine learning through the models of regression and Naïve Bayes.
Reviewer Profile

5.0

Country Flag Nigeria
“It was a great experience and covers vast areas”
It covers a lot in different areas and is easy to follow. It is a great course.
Reviewer Profile

5.0

Country Flag India
“Solid Foundations in ML Using Python”
Great course! The concepts were explained clearly, and the practical examples really helped me understand machine learning with Python.
Reviewer Profile

5.0

Country Flag Philippines
“Gained strong Python skills in data manipulation, learned statistics and reinforcement learning, boosting problem-solving and confidence in tackling data challenges.”
I really enjoyed the hands-on approach of the course, especially working with real-world datasets. The practical exercises in data manipulation and reinforcement learning were particularly engaging. I appreciated how the course balanced theory with application, helping me build a solid understanding while giving me the confidence to solve complex problems. The case studies, like the Smart Taxi problem, were exciting and showed me the real-world potential of machine learning. Overall, I liked how the course helped me grow both my technical skills and my problem-solving abilities.
Reviewer Profile

5.0

Country Flag Philippines
“Gained hands-on Python skills in data manipulation, learned key statistics and reinforcement learning, boosting confidence in solving complex data challenges.”
I really enjoyed the practical focus of the course. It wasn't just about learning theories but applying them to real-world data challenges. The case studies, especially in reinforcement learning, were particularly engaging and showed how these concepts are used in dynamic environments. The hands-on exercises in Python, data manipulation, and statistics helped solidify my understanding, and I feel more prepared for future projects.
Reviewer Profile

5.0

Country Flag Brazil
“Transforming Knowledge: My Journey Through the Machine Learning Course!”
The Machine Learning course was an insightful and enriching experience. The content was well-structured, making complex concepts easier to understand. I feel more confident in applying machine learning techniques. Highly recommended for anyone looking to deepen their knowledge in this field!
Reviewer Profile

5.0

Country Flag India
“Overall, it was an engaging and rewarding experience that fostered personal and professional growth.”
The course was highly informative and well-structured, providing a clear understanding of the key concepts. The teaching approach was engaging, and the practical examples helped in applying the knowledge effectively. I appreciated the interactive sessions and the support provided by the instructor. Overall, an excellent learning experience!
Reviewer Profile

5.0

“Clear and Well-Structured Course Content”
I really appreciated the interactive elements of the course, which kept me engaged throughout. The instructor’s ability to break down difficult concepts into digestible pieces was impressive. Overall, a highly valuable learning experience.

Meet your faculty

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

instructor img

Dr. Abhinanda Sarkar

Senior Faculty & Director Academics, Great Learning
  • 30+ years of experience in data science, ML, and analytics.
  • Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.
instructor img

Mr. Bharani Akella

Data Scientist
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.
instructor img

Dr. Sunil Kumar

GM - Engineering Innovation
  • 15+ years of industry experience in AI, machine learning, NLP
  • Published researcher, speaker, and author of 'R Machine Learning Projects
instructor img

Prof. Mukesh Rao

Senior Faculty, Academics, Great Learning
  • 20+ years of expertise in AI, machine learning, and analytics
  • 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,

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