AI for all humans: A course to delight and inspire!

Making Friends with Machine Learning was a legendary internal-only Google course specially created to inspire beginners and amuse experts. Today, it is available to everyone! You can now enjoy it by following these links:

Part 1 — Introduction to ML: bit.ly/mfml_part1

Part 2 — Life of a Machine Learning Project: bit.ly/mfml_part2

Part 3 — AI from Prototype and Production: bit.ly/mfml_part3

Part 4 — Opening the Black Box: bit.ly/mfml_part4

About the course

The course is designed to give you the tools you need for effective participation in machine learning for solving business problems and for being a good citizen in an increasingly AI-fueled world.  MFML is perfect for all humans; it focuses on conceptual understanding (rather than the mathematical and programming details) and guides you through the ideas that form the basis of successful approaches to machine learning. It has something for everyone! 

After completing this course, you will:

gain an intuitive and correct understanding of core machine learning concepts.

understand the flavor of several popular machine learning methods.

avoid common errors in machine learning.

know how machine learning can help your endeavors.

gain insight into the steps involved in leading machine learning projects from conception to launch and beyond.

Chapter by chapter

Part 1 — Introduction to ML

Part 2 — Life of a Machine Learning Project

Part 3 — AI from Prototype and Production

Part 4 — Opening the Black Box

While the first 3 parts focused on giving you the concepts and roadmaps to lead a successful applied machine learning project, Part 4 indulges your curiosity about what’s going on under the hood. The final chapter covers the intuition behind:

Clustering and k-Means

Lazy learning and k-NN

Perceptron

Maximal Margin Classifier

Support Vector Classifier

Support Vector Machines

Decision Trees

Boosted Aggregation

Random Forests

Ensemble Models

Naive Bayes

Linear Regression

Logistic Regression

Neural Networks / Deep Learning

You’ll gain insights into a whole bunch of algorithms… all without having to study the equations. To be fair, those equations are what’s already in all the textbooks, so the goal of this course was to give you something you can’t get elsewhere. It’s all about intuition and conceptual understanding. Luckily, after you’ve absorbed the intuition, those equations will make much more sense if/when you do choose to study them.