Machine Learning For Absolute Beginners

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1. __rito__Jun 2023
   I studied Physics in college, so I mostly knew the Math.

   Here's what I used to self-learn:

   1. Machine Learning for Absolute Beginners by Oliver Theobald

   2. ISLR

   3. Machine Learning by Andrew Ng on Coursera

   4. Deep Learning by Andrew Ng on Coursera

   5. fast.ai

   6. Sebastian Raschka's PyTorch book

   Good Math refreshers:

   1. Mathematics for Machine Learning Specialization by Imperial College London

   2. Linear Algebra and Calculus series by 3blue1brown on YT

   Later I delved deep into Computer Vision for profession, and Edge AI for personal projects.

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2. __rito__Jun 2023
   - Have crystal clear Mathematical foundations, as in why this formula/method the way it is, rather than being able to solve college/HS test problems. Really solid footing in Differential Calculus and Linear Algebra is necessary.

   - Know the Statistical language that you learn from a basic college-level Stat 101 course. Be able to translate normal sentences into those using Statistical notation, and be able to read easily. Also, know basic Statistics.

   - You already know programming, I assume. Learn Python if you don't know already. It's really easy.

   - There are a number of paths you can go from there. Here's what I did.

   -- IBM Data Science Professional Certificate (not deep at all, but lays out the landscape well; did it in a week)

   -- Machine Learning for Absolute Beginners by Oliver Theobald which you can finish in an evening.

   -- Machine Learning Specialization by Andrew Ng on Coursera.

   -- Deep Learning Specialization by Andrew Ng on Coursera.

   -- fast.ai course.

   - Learn PyTorch really well. I suggest Sebastian Raschka's book.

   Now from here, you can chart your own path. You can choose NLProc, Vision, RL, or something else.

   I went towards Vision. And I do Edge AI as hobby.

   I was in the last year of college as a Physics undergrad, when I was hired to do Vision modelling/research for a non-flashy company in 2021. Finishing my CS Master's next month and starting to look for PhD. I worked in the same company for the ~2.5 years.

   EDIT: If you want a job in big tech, grind Leetcode, and learn about system design, study Machine Learning systems, and be able to design them. Chip Huyen has a good book as I hear. 6-7 rounds of interview is common in Meta/Google. DL hackathon awards, open source contributions are significantly helpful.

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3. rg111May 2022
   You don't say how much you already know.

   Your first stop would be Khan Academy and knowing your gaps.

   Fill your gaps.

   Learn HS level Calculus, Linear Algebra, and Statistics.

   You will need more Calculus and Linear Algebra later. But not now.

   Then try studying "Machine Learning for Absolute Beginners" book. It not very mathy.

   Then just keep going through ML courses. Learn what you need on the way.

   The "way" of math needed in Machine Learning is not the same "way" that brings you scores in school/college exam.

   You need absolutely crystal clear concepts in Linear Algebra, Multivariable Calculus, and in some areas of ML, Statistics.

   Corporate "Data Science" and Machine Learning research/projects are wildly different beasts. Learn what you will pursue, and decide your path based on that.

   And most importantly, you have to be patient. Machine Learning and Math for it takes time- not days or weeks, but months and years.

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