Top 20 free Data Science, ML and AI MOOCs on the Internet

Source: Artificial Intelligence on Medium

Machine Learning and AI

13. Machine Learning Crash Course — Google

This crash course is a self-study guide for aspiring machine learning practitioners and it features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. This is one of the courses under the Learn with Google AI initiative, encouraging all to learn AI.

14. Elements of AI — University of Helsinki

The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. It was made to encourage everyone to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.

15. Machine Learning — Andrew Ng

Machine Learning with Andrew Ng is one of the most popular online courses on the internet, it has it all. From the basics to neural networks and SVM, plus an application project at the end. The good thing about this course is Andrew Ng is an incredible teacher. The bad, it’s taught in MATLAB (I would prefer Python).

16. Practical Deep Learning for Coders — Fast.ai

Fast.ai is the online course to go if you want to learn deep learning for free. Everyone on the internet recommends it and it surely is a valuable resource for those who want to learn deep learning. This course utilizes Jupyter notebooks for your learning and PyTorch as the main tool for coding deep learning.

17. CS230 Deep Learning — Stanford

Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.

18. CS224N Natural Language Processing with Deep Learning — Stanford

Natural language processing (NLP) is one of the most important technologies of the information age and a crucial part of Data Science. Applications of NLP are ubiquitous — in web search, emails, language translation, chatbots, etc. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP.

What you’ll learn:

  • Design, implement and understand your neural network models.
  • PyTorch!

Watch it on Youtube here.

19. CS231n: Convolutional Neural Networks for Visual Recognition — Stanford

Computer Vision has become ubiquitous in our society, with applications in search, facial recognition, drones, and most notably, Tesla cars. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.

What you’ll learn:

  • Implement, train and debug their neural networks
  • Gain a detailed understanding of cutting-edge research in computer vision.

The final assignment involves training a multi-million parameter convolutional neural network and applying it to the largest image classification dataset (ImageNet).

Watch it on Youtube here.