# What Google Recommends You do Before taking their ML or Data Science Course

## First steps to learning data science & ML — foundations of DS & ML

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Be it Andrew Ng’s ML/DL course on YouTube or any Data Science Bootcamp, you will need a certain degree of mathematical and statistical knowledge to not only understand but make a long-lasting, robust career as a data professional.

This is a short and precise guide for all autodidact and beginners in the field of Data Science and Machine Learning.

A common question that pops out from all my training programs, LinkedIn courses, videos on YT, or newsletters is that when they start learning DS/ML, after a certain point, they feel lost in mathematics or statistics and sometimes programming.

And I have always recommended learning or refreshing some mathematical concepts that underpin ML as it helps you build intuition which keeps you curious throughout your learning journey.

To back this claim, **here are the prerequisites and prework Google recommends before taking their Machine Learning Crash Course:**

I’d recommend you go through this article first and then look up all the links one by one and use this blog as a reference.

After going through the complete list of concepts and skills that are mentioned in the Google article, I also went through several books(Deep Learning by Ian Goodfellow, Deep Learning with Python by Francois Chollet, and several others) and I tried to distill the essentials into three branches that are needed to build a solid foundation for a career as a Data Analyst/Scientist/ML Engineer.

Following are the three pillars along with them a list of concepts that are needed to