Teaching my machines

This a page linking to Artificial Intelligence (AI) resources. As you’ve already learned, I started building computers when I was 15 years old and have never stopped. To that end I continue to research and develop tools for myself and for my clients and students at the very same frenetic pace the industry finds itself in today. If you want to learn more about AI you’ve landed on the right page. Check back here often – this content will change VERY rapidly …

You’re going to have to make sure you have no gaps in your educational background before you can follow along with what I’m saying here and of course there is going to be a lot, A LOT, that you have to read, research and practice to catch up with me and the rest of the AI community ‘out there.’ That said – here is a bunch of resources that you can use in addition to the courses I linked to on my What I’m Studying page. Go to that page and follow along with the Udemy suggestions I made there then come back here to dive deeper down this rabbit hole. Get ready I’m linking to machine learning courses, bootcamps, books, tools, interview questions, cheat sheets, MLOps platforms, and more to master ML.

Let’s go …

A Beginners Course

Microsoft offers a comprehensive classic machine learning 12-week program comprising 26 lessons and 52 quizzes. It is an ideal starting point for newcomers. Even if you have no prior experience with machine learning you can build core competencies using Scikit-learn and Python. Each lesson features supplemental materials including pre- and post-quizzes, written instructions, solutions, assignments, and other resources to complement the hands-on activities. Follow this course and then come back here to proceed.

Microsoft themselves suggest that you fork the entire repo to your own GitHub account and complete the exercises on your own or with a group. Don’t know how to use GitHub? Here read and follow along with this:

  • Start with a pre-lecture quiz.
  • Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
  • Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the /solution folders in each project-oriented lesson.
  • Take the post-lecture quiz.
  • Complete the challenge.
  • Complete the assignment.
  • After completing a lesson group, visit the Discussion Board and “learn out loud” by filling out the appropriate PAT rubric. A ‘PAT’ is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.

Below this line are links to more advanced stuff. The links a little random right now; I’m working on that. If you follow along remember – YMMV – you’ve been warned.

Machine Learning – I found Kylie Ling’s YT course here a good one to learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.