Machine Learning

Showcasing of my machine learning research, experiement and development

Title	Domain	Link
Intuitive Ensemble Learning Guide with Gradient Boosting as a Study Case	Ensemble	link
Designing ML Orchestration Systems for Startups	Orchestration	link
Free Advanced Machine Learning PhD at Cambridge	PhD	link
How to build your own AutoML library in Python from scratch	AutoML	link
Creating the Whole Machine Learning Pipeline with PyCaret	Pycaret	link
How to solve 90% of NLP problems: a step-by-step guide	NLP	link
Predicting long-term user engagement from short-term behavior	Analytics	link
Building an image search service from scratch	CV	link
ML Visuals	Visuals	link
BudgetML		link
Pyspark	Distributed Computing	link

My Repositories

1. Datacamp
2. Kaggle

Roadmap

1. RAPIDS
2. Pytorch
3. On the Opportunities and Risks of Foundation Models
4. OpenMMLab
5. Machine Learning Engineer Learning Path

AI Transparency

1. PySyft
2. Saidot

Curated list

0. Machine Learning for Beginners

Starter

0. Practical Mchine Learning by Stanford
1. Your First Machine Learning Project in Python Step-By-Step
2. Made with ML ***
3. Awesome MLOps
4. Applied ML
5. Distill.pub
6. ML-For-Beginners
7. Applied ML
8. ApplyingML
9. CS 329S: Machine Learning Systems Design ***
10. ML Sys Seminars***
11. MLU-EXPLAIN*

Realtime Systems

1. The Four Innovation Phases of Netflix’s Trillions Scale Real-time Data Infrastructure

MLOps

1. Python MLOps Cookbook
2. MLOps-Basics***

Book

• The Machine & Deep Learning Compendium

Thoughts

• How to Train Really Large Models on Many GPUs?
• A friendly introduction to machine learning compilers and optimizers
• Why data scientists shouldn’t need to know Kubernetes

Recipies

• ML recipies