BY Andrew Ng - Stanford University

This course provides a broad introduction to machine learning, data-mining, and statistical pattern recognition. Topics include: Supervised learning (support vector machines, kernels, neural networks), Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning), Best practices (bias/variance theory; innovation process) in machine learning and AI.

BY Andrew Ng - Stanford University

Specialization of five courses. I learned the foundations of Deep Learning, music generation, and natural language processing. I have master not only the theory, but also see how it is applied in industry. I practice all these ideas in Python and in TensorFlow. It helped me in finding creative ways to apply it to my work. Consequently, This helped me master Deep Learning, understand how to apply it, and build a career in AI.

By Fox & Carlos Guestrin - University of Washington

Foundations: A Case Study Approach Machine Learning: Regression Machine Learning: ClassificationMachine Learning: Clustering & Retrieval

BY Geoffrey Hinton

This course helped me grsap the deep understaning ans aspects of cutting-edge AI. I learned stuffs that helped me build AI systems that just weren't possible a few years ago.In this course, I learned the foundations of deep learning, Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks.

BY Google Cloud Team

In this Course, I found the answers to these questions in this course. What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets?

BY Jason A. Roy - University of Pennsylvania

"Correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question. During a period of 5 weeks, I learned how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods.