Today, we're happy to share our Machine Learning Crash Course (MLCC) with the world. MLCC is one of the most popular courses created for Google engineers. Our engineering education team has delivered this course to more than 18,000 Googlers, and now you can take it too! The course develops intuition around fundamental machine learning concepts.
What does the course cover?
MLCC covers many machine learning fundamentals, starting with loss and gradient descent, then building through classification models and neural nets. The programming exercises introduce TensorFlow. You'll watch brief videos from Google machine learning experts, read short text lessons, and play with educational gadgets devised by instructional designers and engineers.
How much does it cost?
MLCC is free.
I don't get it. Why are you offering MLCC to everyone?
We believe that the potential of machine learning is so vast that every technical person should learn machine learning fundamentals. We're offering the course in English, Spanish, Korean, Mandarin, and French.
Does the real world make an appearance in the course?
Yes, MLCC ends with short lessons on designing real-world machine learning systems. MLCC also contains sections enabling you to learn from the mistakes that our experts have made.
Do I have enough mathematical background to understand MLCC?
Understanding a little algebra and a little elementary statistics (mean and standard deviation) is helpful. If you understand calculus, you'll get a bit more out of the course, but calculus is not a requirement. MLCC contains a helpful section to refresh your memory on the background math.
Is this a programming course?
MLCC contains some Python programming exercises. However, those exercises comprise only a small percentage of the course, which non-programmers may safely skip.
I'm new to Python. Will the programming exercises be too hard for me?
Many of the Google engineers who took MLCC didn't know any Python but still completed the exercises. That's because you'll write only a few lines of code during the programming exercises. Instead of writing code from scratch, you'll primarily manipulate the values of existing variables. That said, the code will be easier to understand if you can program in Python.
But how will I learn machine learning concepts without programming?
MLCC relies on a variety of media and hands-on interactive tools to build intuition in fundamental machine learning concepts. You need a technical mind, but you don't need programming skills.
How can I show off my machine learning skills?
As your knowledge about Machine Learning grows, you can test your skill by helping others. We're also kicking off a Kaggle competition to help DonorsChoose.org. DonorsChoose.org is an organization that empowers public school teachers from across the country to request materials and experiences they need to help their students grow. Teachers submit hundreds of thousands of project proposals each year; 500,000 proposals are expected in 2018.
Currently, DonorsChoose.org relies on a large number of volunteers to screen the proposals. The Kaggle competition hopes to help DonorsChoose.org use ML to accelerate the screening process, which will enable volunteers to make better use of their time. In addition, this work should help increase the consistency of decisions about projects.
Is MLCC Google's only machine learning educational project?
MLCC is merely one of many ways to learn about machine learning. To explore the universe of machine learning educational opportunities from Google, see our new Learn with Google AI program at g.co/learnwithgoogleai. To start on MLCC, see g.co/machinelearningcrashcourse.