12 Coursera Free Courses Machine Learning for Everyone in 2025

Are you looking for Coursera Free Courses Machine Learning?… If yes, then this article is for you. In this article, you will find the 12 Best Coursera Free Courses Machine Learning for you. For these courses, You don’t need to pay a single buck. So give your few minutes to this article and check out these Coursera Free Courses Machine Learning.

Now without any further ado, let’s get started-

Coursera Free Courses Machine Learning

1. Machine Learning– Stanford University

Rating- 4.9/5

Time to Complete- 60 hours

Level- Beginner

This is one of the Best Courses for Machine Learning on Coursera. This course is created by Andrew Ng the Co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University.

This Course provides you a broad introduction to machine learning, data-mining, and statistical pattern recognition. All the math required for Machine Learning is well discussed in this course.

This course uses the open-source programming language Octave. Octave gives an easy way to understand the fundamentals of Machine Learning.

Who Should Enroll?

• This Course is Most Suitable for Complete Beginners. But people with some basic understanding of ML can also enroll.

Interested to Enroll?

If yes, then check out the details here- Machine Learning

2. K-Means Clustering in Python– University of London

Rating- 4.6/5

Time to Complete- 29 hours

Level- Beginner

This is a free course offered by Coursera, where you will learn the core concepts of Data Science and covers basic mathematics, statistics, and programming skills.

In this course, you will implement the K-means algorithm using Python programming. This course is a perfect balance between theory and practice and a good and useful course for learning the basics of data science.

Who Should Enroll?

• Those who are beginners with at least at high-school level mathematics knowledge.

Interested to Enroll?

If yes, then check out all details here- K-Means Clustering in Python

3. Predicting heart disease using Machine Learning– Coursera Community

Rating- 4.2/5

Time to Complete- 50 minutes

Level- Beginner

This is Free Coursera Guided Project. In this project, you will develop a predictive model that can accurately predict the presence or absence of heart disease from clinical and laboratory data using a K-Nearest-Neighbors Classifier.

Who Should Enroll?

• Those who are beginner in Machine Learning and familiar with Python and basic ML concepts.

Interested to Enroll?

If yes, then check out the details here- Predicting heart disease using Machine Learning

4. Introduction to Embedded Machine Learning– Edge Impulse

Rating- 4.8/5

Time to Complete- 17 hours

Level- Intermediate

This is not a beginner-level course. In this course, you will understand the working of machine learning, the basics of neural networks, and the deployment of the neural networks to microcontrollers, which is known as embedded machine learning or TinyML.

Who Should Enroll?

• Those who don’t have prior machine learning knowledge but familiar with Arduino and microcontrollers.

Interested to Enroll?

If yes, then check out the details here-  Introduction to Embedded Machine Learning

5. Computer Vision with Embedded Machine Learning– Edge Impulse

Rating- NA

Time to Complete- 31 hours

Level- Intermediate-Level

This is another Free Coursera course to learn how deep learning with neural networks can be used to classify images and detect objects in images and videos.

In this course, you will use convolutional neural networks (CNNs) to classify images and detect objects. Then you will deploy your CNN model to a microcontroller and/or single-board computer.

Who Should Enroll?

• Those who are familiar with Python programming language and basic ML concepts.

Interested to Enroll?

If yes, then check out the details here- Computer Vision with Embedded Machine Learning

6. Computational Neuroscience– University of Washington

Rating- 4.6/5

Time to Complete- 26 hours

Level- Beginner

This course is more focused on Deep Learning and Artificial Neural networks. In this course, you will learn basic computational methods for understanding the functions of nervous systems.

The instructors will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course.

Who Should Enroll?

• Those who are beginner and interested in learning how the brain processes information.

Interested to Enroll?

If yes, then check out the details here- Computational Neuroscience

7. Practical Crowdsourcing for Efficient Machine Learning– Yandex

Rating- NA

Time to Complete- 17 hours

Level- Beginner

This course will teach you how to perform efficient and scalable data labeling for ML and various business processes. You will also understand the applicability, benefits, and limits of the crowdsourcing approach.

In this course, you will design and run a full-cycle crowdsourcing project: from planning to getting labeled data.

Who Should Enroll?

• Those who have general understanding of ML and AI.

Interested to Enroll?

If yes, then check out the details here- Practical Crowdsourcing for Efficient Machine Learning

8. Brain Tumor Classification Using Keras– Coursera community

Rating- 4.5/5

Time to Complete- 2 hours

Level- Intermediate

This is another Free Coursera Guided Project. In this project, you will use an efficient net model and train it on a Brain MRI dataset. The objective of this project is to create an image classification model that can predict Brain MRI scans that belong to one of the four classes(Glioma Tumor, Meningioma Tumor, Pituitary Tumor, and No Tumor) with reasonably high accuracy.

Who Should Enroll?

• Those who are familiar with programming in Python and have a theoretical understanding of Convolutional Neural Networks, and optimization techniques.

Interested to Enroll?

If yes, then check out the details here- Brain Tumor Classification Using Keras

9. Machine Learning for All– University of London

Rating- 4.7/5

Time to Complete- 22 hours

This is a beginner-level course where you will get a basic idea of machine learning, even if you don’t have any background in math or programming. You will also get hands-on and use user-friendly tools developed at Goldsmiths, the University of London to actually train a machine learning model to recognize images.

But this course doesn’t cover programming-based machine learning tools like python and TensorFlow. That’s why anyone can do this course just to understand the fundamentals of machine learning.

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who just want to understand the basics of machine learning without any programming and mathematical understanding.

Interested to Enroll?

If yes, then check out all details here- Machine Learning for All

10. Machine Learning: Algorithms in the Real World Specialization– Alberta Machine Intelligence Institute

Rating- 4.6/5

Time to Complete- 4 months( If you spend 3 hours/week)

This is the specialization program where you will learn how to apply machine learning to data analysis and automation. This specialization program will teach you how to prepare data for effective machine learning applications.

You will also learn how to implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbors, and support vector machines are optimally used.

There are 4 courses in this specialization program. Now let’s see the details of the courses-

Courses List-

1. Introduction to Applied Machine Learning
2. Machine Learning Algorithms: Supervised Learning Tip to Tail
3. Data for Machine Learning
4. Optimizing Machine Learning Performance

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have a previous understanding of analytics, math (linear algebra, matrix multiplication), statistics, and beginner level python programming

Interested to Enroll?

If yes, then check out all details here- Machine Learning: Algorithms in the Real World Specialization

11. Introduction to Deep Learning– National Research University Higher School of Economics

Provider- National Research University Higher School of Economics

Rating- 4.6/5

Time to Complete- 34 hours

This course is the part of Advanced Machine Learning Specialization program. In this course, you will learn the fundamentals of modern neural networks and their applications in computer vision and natural language understanding.

This course will teach you all the important building blocks of neural networks including fully connected layers, convolutional and recurrent layers.

There is a project associated with this course, where you will implement a deep neural network for image captioning which solves the problem of giving a text description for an input image.

Extra Benefits-

• You will get a Shareable Certificate.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have basic knowledge of Python, linear algebra, and probability.

Interested to Enroll?

If yes, then You can Sign Up here.

12. Artificial Intelligence: An Overview– Politecnico di Milano

Time to Complete- 8 hours

Level- Beginner Level

This is a very basic course and provides a non-technical overview of the artificial intelligence field. You will learn the taxonomy of the know-how on AI in terms of techniques, software, and hardware methodologies.

The instructor will explain the need for national strategies on AI and identify the major Italian and European players on AI.

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.

Who Should Enroll?

• Those who have no prerequisite knowledge are required.

Interested to Enroll?

If yes, then check out all details here- Artificial Intelligence: An Overview

And here the list ends. So, these are the 12 Coursera Free Courses Machine Learning. I will keep adding more free courses to this list.

Conclusion

I hope these 12 Coursera Free Courses Machine Learning will help you to enhance your machine learning skills. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

Thank YOU!

Learn Machine Learning A to Z Basics

Though of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford