Many of you have started machine learning for some time, maybe a year or two. There are chances that if you would evaluate yourself now, in the theoretical aspects of machine learning, even the things that were very clear when you were learning, you might have forgotten those due to either not using them in practice or relying too much on high-level frameworks.
This 2 months curriculum is for those who are already in the field for some time and will help them in revising all the core concepts.
Machine Learning Skills Week 1: Mathematical Foundations
This week aims to make sure that you revise all the core mathematical concepts required for beginners to grasp Machine Learning.
Days 1–3: Linear Algebra
Stanford University has really good quick notes on Linear Algebra that are 30 pages long, i.e., made for revision and cover all the essential topics. You can find them here. Additionally, you can watch videos on the topics in the notes if you want, but these notes are enough to revise the concepts.
Days 3–6: Probability and Statistics
Probability and Statistics are the backbones of Machine Learning, and this short video by Edureka can be helpful in a quick refresher of Probability and Statistics. Alongside this video, you check these slides for probability refresher and this pdf by Stanford for quick revision.
Machine Learning Skills Weeks 2–3: Machine Learning and Deep Learning Intuition
I will follow the latest explanation of Professor Andrew Ng (CS229 Autumn 2018) from Stanford University for understanding the mathematics and working behind the Machine Learning Algorithms. Professor Andrew Ng is an adjunct professor at Stanford, but he has many other activities, so he is best described as a “Leading AI Researcher and Founder of Deeplearning.ai, Coursera, and other startups.”
For Deep Learning algorithms, I will be following CS231n by Professor Andrej Karpathy, who has done his Ph.D. from Stanford and has taught the famous CS231n in 2016, which is one of the most-watched courses on Deep Learning for Vision Systems. Andrej is now the senior director of AI at Tesla.
Some lectures are also given by Professor Fei Fei Li, who is the inaugural Sequoia Professor in the Computer Science Department at Stanford University and Co-Director of Stanford’s Human-Centered AI Institute.
Day 1: Linear Regression
One of the most common…
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