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:
Prerequisites and Prework | Machine Learning Crash Course
I have little or no machine learning background. We recommend going through all the material in order. I have some…
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