Anyone looking to start learning machine learning has a plethora of resources at their disposal. However, with so many choices it can be difficult to know where to start. This blog post will outline four free machine learning courses from top US universities such as Harvard, Stanford, MIT, etc that are sure to get you on the right track.
List of Online Free Courses on Machine Learning
The following is a list of online free courses on machine learning from some of the top US universities:
- Harvard’s CS50p: Intro to Python (cs50.harvard.edu/python/2022/)
- MIT 6.S191: Intro to Deep Learning (https://introtodeeplearning.com/)
- Cornell Tech CS 5787: Applied machine learning course (https://cornelltech.github.io/cs5785-fall-2019/)
- Stanford’s Machine Learning Specialisation offered through Coursera (https://www.coursera.org/specializations/machine-learning-introduction)
- UC Berkley: Full stack Deep Learning (https://fullstackdeeplearning.com/)
- University of Washington CSE446 – Machine Learning (https://courses.cs.washington.edu/courses/cse446/20wi/)
Conclusion
There are a lot of machine learning courses out there, but these are just a few from some of the top universities in the world. They are all available for free, so there is no excuse not to get started on your machine learning journey today! I will continue posting more courses from time-to-time.
I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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I found it very helpful. However the differences are not too understandable for me