My early courses in AI and Machine Learning at Stanford, Deep Learning, and University of Toronto were rigorous and mathematical. I often took a break by getting outside perspectives from teachers of courses on Udemy and YouTube when I encountered tough challenges.

Stanford University
In the 1990s, I took several graduate level courses from Stanford focusing on two areas:
- Advanced Systems
- Databases
I felt humbled and thankful studying under many great world-class leaders in these technologies. Professors like
- Craig Partridge–one of the inventors of the Internet.
- Dr. Rosenblum who founded VMware with his wife,
- Nick Parlante, extraordinary at teaching Software Engineering in C,
- Serge Abitiboul who taught Databases and Transaction Processing with great rigor based on relational calculus and relational algebra.
Truly the best of Masters and Doctorate level courses.
AI: Year 2016 and Beyond

On August 7, 2018, I completed this course with Professor Andrew Ng with perfect scores throughout.
Andrew developed this course for students to work in Matlab or Octave which is a public domain or free clone of Matlab.
This course was approximately 59 hours long and covered the following:
- Logistic Regression (and Linear Regression, of course)
- Artificial Neural Networks
- Machine Learning (ML) Algorithms
- Machine Learning
University of Toronto and Dr. Geoffrey Hinton

Neural Networks for Machine Learning with University of Toronto
PhDs consider this course challenging. And, Geoffrey Hinton, a man considered by some as the “Godfather of AI” taught this course. In fact, Hinton simultaneously invented backpropagation with Yann Lecun.
Sadly, Dr. Hinton, one of my all time favorite professors considered this course outdated, and I felt broken hearted upon learning he and Coursera had discontinued it. I loved his his rigor and precision and contribution to the field.
So, I completed approximately 94% of his course with almost a perfect score, but an ambiguity in the Quiz in Lecture 9 left a question as to whether this was correct. Because of that, I want to go back and complete the last part of it, but I needed to take a break to go through my AI Bootcamp at Caltech and complete my MBA with HEV.
As with the previous course, Dr. Hinton gave this course using Matlab or Octave. I never encountered another course which covered energy based networks such as the unsupervised Hopfield and Restricted Boltzman Networks.
This was perhaps the most rigorous course aside from the AI and Machine Learning Bootcamp I took later from Caltech which was less academic and more focused on Data Science in Business.
AI and Machine Learning courses Through Dr. Andrew Ng’s Deep Learning
Stanford Professor Andrew Ng helped found Deep Learning and Coursera for on-line learning from major universities. I completed the following three courses with perfect scores throughout:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
- Structuring Machine Learning Projects
Dr. Ng taught these courses using Python, standard libraries such as numpy, pandas, and Keras or Tensorflow which later merged under Tensorflow 2.0.
More coursework in AI and Machine Learning

Caltech’s AI and Machine Learning Bootcamp through Simplilearn
Upon receiving a scholarship for the AI and Machine Learning Bootcamp with Simplilearn and Caltech, I went through that program and found it to be very rigorous and practical from a business standpoint.
AI and Machine Learning Through Udemy

Through Udemy, I took The Complete Self-Driving Car Course – Applied Deep Learning and completed it on Nov. 1, 2018.
This course was quite cursory–likely a very small, watered-down summary of Udacity’s excellent Self-Driving Car programs which I considered taking along with their courses on Autonomous Flight. However, due to limited time, I chose to focus on my MBA and Caltech’s AI and Data Science Bootcamp.
AI and Prompt Engineering at HEV

I also completed an AI Course in my MBA program with Haroun Education Ventures (HEV).
I strongly recommend this very enterpreneurial non-conventional MBA as it will actually teach you what you need practically to run or start a business, how to invest, how to manage a business, how to work with venture capitalists, and many of the things you would never learn in most business schools.
Some business owners and students who previously earned their more conventional MBAs from schools such as Harvard have chosen to go through this program as well.
During my MBA program, a world-class expert in AI taught an excellent, but easy and practical course involving prompting. Unlike other programs, this course did not weigh business students down with mathematical rigor. They focused more on prompt engineering.
Some tend to scoff on that part of AI as prompt engineering is considerably easier–a skill hardly worthy of an engineering association. Yet, business-practical skills get the work done and pay the bills. And it takes practice to get the feel for how to create the most ideal prompts as one might practice piano or photography.
In summary, this program focused on Large Language Models (LLMs) and prompt engineering. And I have been able to put to use extensively almost daily to help with research, most of all, but also improving my writing skills, identifying top categories and tags and niche topics for my websites.
Prompt engineering is perhaps a skill everyone has these days, but it is extremely useful.
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