Скачать или смотреть Foundations for Machine Learning | Linear Algebra, Probability, Calculus, Optimization [Lecture 1]

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"Foundations for Machine Learning: New Course Launch"


In 2022, I graduated with a PhD in Mechanical Engineering from MIT.


Although a big component of my research was purely hands-on experiments, my exposure to foundational graduate-level ML courses at MIT, research courses, and Scientific Machine Learning via Julia gave me the confidence of a Machine Learning researcher.


I incorporated ML into my research, and it solved a problem that are otherwise difficult to solve theoretically or experimentally.



When I co-founded Vizuara AI Labs with Raj and Rajat, our core principles were depth and foundational knowledge. Now I have co-authored multiple AI-ML research papers and two of them are accepted to the upcoming NeurIPS workshop.


Behind all of this effort, there is the confidence that stems from knowing what happens underneath the ML algorithms.


Most of the online courses have little emphasis on fundamentals. People are so used to spending time on toy Kaggle projects. Very few people I know can build a neural network from scratch or explain what happens behind them.


For the last 4 months, I have been working to launch a new course on Vizuara's YouTube channel on "Foundations for Machine Learning." This will be a 45-hour course with ~65 lectures. I will be hosting all lectures on this playlist:    • Foundations for Machine Learning  


My singular goal with this course is to teach you the entire foundations required to learn ML from scratch.


There are no prerequisites. If you have basic logical thinking capability and a willingness to dedicate time, consistently, you can follow this course.


I have split the course into 5 modules.


1) First I will be covering the 4 mathematical pillars of ML: Linear Algebra, Probability, Statistics, and Calculus.


2) In the second module I cover the basic programming fundamentals for a complete beginner. I will teach you Python from scratch and it's some of the most important packages for ML including NumPy and PyTorch.


3) In the 3rd module we will learn about optimization and gradient descent. I wanted to dedicate an entire module to optimization because when you actually build ML models, you will be spending a lot of time on optimization.


4) In the 4th module, I will give you an overview of the AI landscape. What happened from 2010-2020 and what does it look like from 2020-2030? This overview will help you understand overall where ML, DL, NLP, CV, and GenAI are heading.


5) In the final module, I will cover the most important 2 steps you will have to master as a Data Scientist or ML engineer: processing data and communication via storytelling. I will teach you some of the most powerful preprocessing and visualization techniques.


I have already published the first lecture. Check out here. I am sure you will enjoy and learn a lot:    • Foundations for Machine Learning | Linear ...