Are you looking for Coursera Free Courses Machine Learning?… If yes, then this article is for you. In this article, you will find the 8 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
And here the list ends. So, these are the 8 Coursera Free Courses Machine Learning. I will keep adding more free courses to this list.
Conclusion
I hope these 8 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!
NOTE- Some of the links in the post are Affiliate Links. This means if you click on the link and purchase the course, I will receive an affiliate commission at no extra cost to you😊.
