Data-Science-and-Machine-Learning-Resources
List of Data Science and Machine Learning Resource that I frequently use
Agentic AI & Agents
Large Language Models (LLMs) & Generative AI
Basic Resources & Transformers
- The Illustrated Transformer
- Transformer Explained
- Visual Guide to Transformer Neural Networks
- Understanding Large Language Models
- Transformer models: an introduction and catalog — 2023 Edition
Advanced concepts
Research Papers
LLM Architectures
- Attention is all you need
- BERTL Pre-training of Deep Biirectional transformers for Language Understanding
- LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attension
- Paramter-Efficient Transfer Learning for NLP
- Distillation - step by step
- SQL-PaLM: IMPROVED LARGE LANGUAGE MODEL ADAPTATION FOR TEXT-TO-SQL
- Language Models are Few-Shot Learners
- FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS
- Large Language Models are Zero-Shot Reasoners
- Scaling Laws for Neural Language Models
- REACT: SYNERGIZING REASONING AND ACTING INLANGUAGE MODELS
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Retrieval augmented LLMs
Data Science
- Data Science Central
- Towards Data Science
- Analytics Vidhya
- Data Science 101
- Data Science News
- Data Science Plus
- Listen Data
- Data Science Specialization Course Notes
- Various Data Science Tutorials
- Probabilistic Programming & Bayesian Methods for Hackers
- Unofficial Google Data Science Blog
- Data Science Cheat Sheet
R
- R Cookbook
- R Blogdown
- ggplot2
- Headley Wickham
- Advance R
- R Package Documentation
- Parallel Processing in R
- Geo Computation with R
Python
- Learn Python Org
- Python Graph Gallery
- Collection of Jupyter Notebooks
- Streamlit library for ML visuals
- Python Machine learning Notebooks
- Automate Stuff with Python
- Python from NSA
- Awesome Python
- Awesome Python Github
- Comprehensice python cheatsheet
- Real Python
- Function Decorators
Machine Learning
- Google AI Blog
- kdnuggets
- Kaggle
- Math Works
- In depth introduction to machine learning - Hastie & Tibshirani
- UC Business Analytics R programming guide
- Machine Learning from CMU
- ML Cheatsheet - Stanford CS229
- Learning from Data
- The Learning Machine
- Machine Learning Plus
- Machine Learning Resources from Sebastian Raschka
- Machine Learning Notebooks
- Machine Learning for beginners
- Curated Machine Learning Resources
- Machine Learning Toolbox
- Rules of Machine Learning: Best Practices for ML Engineering from Google
- Machine Learning Crash Course
- Machine Learning Interviews
- Applied ML - Curated list of papers, articles, and blogs on data science & machine learning in production
- Best of Machine Learning - Python
- Machine Learning Glossary
- Awesome Machine Learning
- Explanable AI
- Fairness and Machine Learning
- Google Reseatch 2021: Themes and beyond
- Machine Learning Complete - Notebooks & demos
- Awesome AI: A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers
MLOps
Statistics & Probability
- Seeing Theory
- Applied Modern Statistical Learning Techniques
- Probability Theory & Mathematical Statistics
- Probability Distributions Overview
- Applied Data Mining and Statistical Learning (PSU)
- Intro to Statistics - Distributions, Power, Sample size, Effective trial design and mixed effect models
- Statistics How To
- Probability Distributions in R
- Mathematical Challenges
- Statistics Basics & Inference
Linear Algebra
Deep Learning
- Deep Learning Papers and read
- Convolutional Neural Network
- Convolutional Neural Network for Visual Recognition
- A simple introduction of ANN
- How backpropagation works
- UFLDL DeepLearning Tutorials
- Classification Results using Deep Learing
- VGGNet Architecture on Imagenet
- Deep Learning Book
- Andrej Karpathy
- Dive into Deep Learning
- Deep Learning Examples in PyTorch by Nvidia
- Deep Learning Examples in TensorFlow by Nvidia
- Curve Detectors
- Deep Learning Drizzle
- Full Stack Deep Learning - training machine learning models to deploying AI systems in the real world
- Practical Deep Learning by Fasi.ai
- Transformers from Scratch
Time Series
- Forecasting Principles and Practice
- How To Identify Patterns in Time Series Data
- Applied Time Series Characteristics
- CausalImpact using Baysian structure time series
- Time Series Notes (Oregon State University)
- Extracting Seasonality and Trend from Data: Decomposition using R
Text Analysis/NLP
- Text Processing - Steps, Tools & Examples
- Document Classification: 7 pragmatic approaches for small datasets
- Collection of Colab notebook based on deep learning & transformer models
- NLP on Spark
- NLP Index
Data Visualization
- Flowing Data
- Seaborn pair plots
- D3 js examples
- D3 js examples newer version
- Data Visualization Society
- A Comprehensive guide to data exploration
- Dash
Algorithms
- Regression (Glm)
- Forecasting using Time Series
- Types of Regressions
- Practice Algorithms
- Hidden Markov Models
- HMM Example: Dishonest Casino
- Hidden Markov Model Notes
- Kernals Trick(SVM)
- Boosting
Big Data
Spark
Frequently Visiting Blogs
- Chris Albon
- DS Lore
- Zack Stewart
- David Robinson
- Simply Statistics
- Citizen Statistics
- Civil Statistian
- R Studio Blog
- Data Science Plus
- R Weekly Org
- Andrew Gelman
- Edwin Chen’s Blog
- R Statistcis co
- Datacamp Community News
- Data Science and Robots - Brandon Rohrer
- Lavanya.ai
- Data Flair
- Fast.ai Blog
- Domino Blog for Code, ML and Data Science
- Data36
- AI Show
- Distill.pub
- Jay Alammar - Blog on NLP and Deep Learning
- Open AI Blog
Tech Blogs
- Netflix Tech Blog for Data Science
- Google AI Blog
- Gogole DeepMind
- AirBnb Engineering & Data Science
- Facebook Research
- Meta AI
- The Yhat Blog
- Uber Engineering
- AWS Machine Learning
- Microsoft AI
- Open AI
- Uber Engineering
- Linkedin AI
- XAI
- NVIDIA AI
- Databricks Data Science & Machine Learning
- Spotify Engineering Blog
- Github AI & ML
Academia ML Courses
- CS 229 ― Machine Learning
- Stat202 - Data Mining and analysis
- Columbia University Applied Machine Learning by Andreas Muller
Public Datasets
- Fig Share
- Quandl
- Quora
- Public Data Sources
- US Gov
- Our World Data
- UCI Machine Learning Repository
- KDNuggets datasets
- Jerry Smith - Data Science Insights
- Data Quest
- Amazon Product Data
- Sentiment Analysis Datasets
- Machine Learning A-Z: Download Practice Datasets
- Microsoft Research Open Data
- Data Hub
- Collection of NLP datasets
- John Snow Labs NLP & Healthcare datasets
- Open Source Audio datasets
Videos on Data
Free Ebooks
- Green Tea Press
- Machine learning and Data Science Books
- Time Series Analysis using R
- Free programming ebooks
- Machine Learning
- 65 Free machine learnign and data books
- Free Programming and ML pdf books
- Approaching any machine learning problem
Misc
- Machine Learning Cheat Sheet in R
- Which algorithn should one use?
- Papers with code
- Browse State of the art
- Data Science Projects
- Churn Prediction & Survival Analysis
- Stanford Machine Learning Projects
- Amazon Science Reasearch and blog
- Machine Learning Questions
- Graph database for beginners
- Top Github Repos
Jupyter Notebooks
- Survival Regression with Sci-kit learn
- Evaluating Survival Regression
- Jupyter Notebook by Domain
- Jupyter Notebooks - DS,ML,TF,AWS,Python
Data Science Interview Prep
- Data Science Interview Questions - Springboard
- Data Science Interviews by Category
- 120 Data Science Interview Questions
- Facebook Interview Prep
- Software/ML Engineer Interview Prep
- Tech Interview Handbook
- DS Interview Questions-Answers
- Interview Query
Data Science & Machine Learning Podcasts
Data Structure & Algorithms
- Geeks for Geeks
- Program Creek
- Career Cup
- A Gentle Introduction to Algorithm Complexity Analysis
- Always be Coding
- Competitive Programming Tutorials
- Python for Algorithms & Data Structure - Interview
- Skilled.dev
- Big O Cheatsheet
- The Algorithms Repo
- Interview Cake (Glossary)
- Algorithm & Coding Interviews
- SDE Skills
- Tech Interview Handbook