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 Framing Descending into ML Refresh your memory on line fitting. Reducing Loss Introduction to TensorFlow Generalization Training and Test Sets Validation Set: Check Your Intuition Representation Feature Crosses Regularization for Simplicity Logistic Regression Classification Regularization for Sparsity Neural Networks Embeddings Static vs. Dynamic Training Static vs. Dynamic Inference Data Dependencies Fairness 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 InformationRelated ArticlesMore 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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