|Take Google's Machine Learning Crash Course|
|Written by Nikos Vaggalis|
|Friday, 28 January 2022|
Sometimes it is worth re-visiting things that matter. And this is one of them - a free course on Machine Learning for total beginners.
The Machine Learning Crash Course was first mentioned in "More Machine Learning Courses From Google" back in 2019 :
Google opened the doors to its Machine Learning Crash Course, which had already been taken by more than 18, 000 Googlers, in March 2018. This free course forms the starting point for anyone to learn about and practice ML concepts and comprises 15 hours of material, including instructional videos, interactive visualizations and exercises
While there's many and great Machine Learning courses, the intended audience varies but amongst the beginner friendly ones this one gets the crown. Although tackling it requires knowledge in a few things, namely Numpy and Pandas, there's two very quick onboarding tutorials available on those topics too. In any case only a basic understanding is necessary.
It takes a holisitc approach and aims to equip the students with the answers to the following essential questions:
Specifically it's objectives are three :
Learn to use ML as a tool to reduce the time you spend programming:
For any given problem you can come up after weeks of hard work with a reasonable program, or you can use an off-the-shelf machine learning tool, feed it some examples,
Second, it will allow you to customize your products, making them better for specific groups of people.:
Suppose I produced my English spelling corrector by writing code by hand, and it was so successful that I wanted to have versions in the 100 most popular languages. I would have to start almost from scratch for each language, and it would take years of effort.
But if I built it using machine learning, then moving to another language, to a first approximation, means just collecting data in that language and feeding it into the exact same machine learning model.
And third, machine learning lets you solve problems that you, as a programmer, have no idea how to do by hand:
As a human being, I have the ability to recognize my friends' faces and understand their speech, but I do all of this subconsciously.
So if you asked me to write down a program to do it, I'd be completely baffled.But these are tasks that machine learning algorithms do very well;I don't need to tell the algorithm what to do, I only need to show the algorithm lots of examples, and from that the task can be solved.
As such the complete syllabus that teaches these concepts is as follows:
Introduction to Machine Learning
Descending into ML
Refresh your memory on line fitting.
Introduction to TensorFlow
Training and Test Sets
Validation Set: Check Your Intuition
Regularization for Simplicity
Regularization for Sparsity
Static vs. Dynamic Training
Static vs. Dynamic Inference
Finally there's practical examples in which you must use your debugging skills:
To sum it up, it's the perfect course for total beginners that certainly deserves a second mention in order to inform those not already aware of it.
More Machine Learning Courses From Google
Take Stanford's Introduction to Robotics For Free
The Year of AI 2021 - The Best Papers
Microsoft's Machine Learning for Beginners
Free Resources For Machine Learning
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