A Beginner’s Guide to Artificial Intelligence and Machine Learning


What really is “artificial intelligence”? “Machine learning”? Let’s cut through the meaningless buzzwords, and talk the real talk.

Regardless of your background, you’ll walk away with the fundamentals for discussing, learning, and practicing machine learning. We’ll cover many topics and takeaways, in 3 sections:

  1. The Big Picture: Start with the big picture, discussing what AI is, what ML is, and then how to learn ML.
  2. Learn, Build, and Practice: Focus in on ML, dissecting ML problems and practice learning ML concepts. We’ll learn about different fundamental ideas like linear regression, bias-variance, featurization, and regularization.
  3. Apply Your Knowledge: Zoom back out, discussing different ML topics and practice breaking down AI products into component ML problems.

Your project for this course is to break down a machine learning problem that you find exciting, into its component parts. You’ll learn about these component parts — data, model, objective, and algorithm — in Lesson 3. (For now, you can skip the algorithm.) This will help bolster your understanding of the fast-moving AI/ML world.

Here are examples of ML problems that you could try breaking down:

  • Classifying cat or dog from images
  • Predicting probability of winning a tournament for an athlete in a sport (e.g., tennis, golf, swimming)
  • Detecting emotion in a phone call recording

Acknowledgments: B-roll in introduction from Pexels (shvets). Icons in cover from flaticon (Freepik, Pixel Perfect).

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