Learning

This repo is based on the amazing repo from amitness, go check!

📘 Overview

Category	Goal	Progress	Anchor
Philosophy	Principles guiding learning	5/5 resources	Jump
Frame ML Problem	Structure ML projects effectively	0/1	Jump
A/B Testing	Design & evaluate experiments	4/15	Jump
Experiment Mgmt	Tools & platforms	1/3	Jump
RecSys	Recommendation Systems resources	-	Jump
Math & Stats	Mathematical foundations	2/120+	Jump
MLOps	Production ML systems	0/2	Jump
Generative AI	LLMs & GenAI	1/1	Jump
Computer Vision	CV & IQA studies	5/5	Jump

Progress counts are approximate; nested playlist items counted individually.

🔍 Quick Access

Link	Description
Learning Philosophy	Core guiding principles
Frame ML Problem	How to structure ML work
A/B Testing	Experimentation theory & practice
Experiment Management	Platforms & tooling
Recommendation Systems	Dedicated separate repo
Math & Statistics	Calculus, linear algebra, probability
ML System / MLOps	Production & platforms
Generative AI	LLM courses
Computer Vision	CV resources & IQA papers

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Learning Philosophy

Status	Resource	Type	Notes
✅	Data Scientists Should Be More End-to-End	Article	End-to-end mindset
✅	Just in Time Learning	Article	Focused consumption
✅	Master Adjacent Disciplines	Article	T-shaped depth
✅	T-shaped skills	Wiki	Breadth + depth
✅	The Power of Tiny Gains	Article	Compounding 1%

Frame an ML Problem

Status	Resource	Platform	Notes
⬜	Coursera: Structuring Machine Learning Projects	Coursera	Project scoping

A/B Testing

Status	Resource	Type	Notes
⬜	Multi-Armed Bandit – A/B Testing Sans Regret	Article	MAB overview
⬜	When to Run Bandit Tests Instead of A/B/n Tests	Article	Bandit vs fixed
⬜	A/B Testing ML Models (Deployment Series 08)	Article	ML deployment
⬜	Datacamp: Customer Analytics & A/B Testing in Python	Course	Practical Python
⬜	Udacity: A/B Testing	Course	Design & eval
⬜	Udacity: A/B Testing for Business Analysts	Course	Biz focus
⬜	Hypothesis testing with Applications in Data Science 0:10:33	Video	Hypothesis intro
✅	Decision Making at Netflix (Part 1)	Article	Culture
✅	What is an A/B Test? (Part 2)	Article	Fundamentals
✅	False Positives & Statistical Significance (Part 3)	Article	Errors type I
✅	False Negatives & Power (Part 4)	Article	Errors type II
⬜	A/B Testing and Beyond (Netflix)	Article	Scaling experimentation
⬜	Quasi Experimentation at Netflix	Article	Causal inference
⬜	Universal Holdout Groups at Disney Streaming	Article	Global holdouts

Experiment Management Tools

Status	Resource	Type	Notes
⬜	Building an Intelligent Experimentation Platform Uber	Article	Platform design
⬜	Under the Hood of Uber’s Experimentation Platform	Article	Architecture
✅	DARWIN: Data Science and AI Workbench LinkedIn	Article	Internal tooling

Recommendation Systems

Status	Resource	Type	Notes
➡️	RecSys studies repo	Repo	search-ranking-recsys-studies

Math and Statistics

Simplified to reduce README size. Each block was moved to dedicated files in `math/`.

Subsection	File	Content
Essence of Calculus	math/calculus.md	Playlist chapters and progress
Essence of Linear Algebra	math/linear_algebra.md	Playlist chapters and progress
Neural Networks (3Blue1Brown)	math/neural_networks.md	Introductory series
MIT 18.06 (Prof. Strang)	math/mit_18_06.md	Lectures and notes
StatQuest Videos	math/statquest.md	Statistical fundamentals
Articles & Courses	math/articles_courses.md	References and courses

Legend remains: ✅ completed, ⬜ pending.

ML System / MLOps

Status	Resource	Type	Notes
⬜	The Magic of Merlin (Shopify)	Article	2022 platform
⬜	Coursera: Intro to ML in Production	Course	Deployment

Generative AI

Status	Resource	Type	Notes
✅	Coursera: Generative AI with LLMs	Course	Completed

Computer Vision

Status	Resource	Type	Notes
✅	Deep Learning for Video Summarization	Video	0:47:40

IQA (Image Quality Assessment)

Status	Resource	Type	Notes
✅	Browsing & Sorting Digital Pictures (Springer)	Paper	Classification & quality
✅	FSIM, SSIM, MSE, PSNR Comparative Study	Paper	Metric comparison
✅	BRISQUE Image Quality Assessment	Article	NR-IQA method
✅	Multimedia Features for Click Prediction (Ads)	Paper	Ad CTR features

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Footer legend: ✅ = completed, ⬜ = pending, ➡️ = external repository