Data Science Resources for interview preparation and learning
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updated Sep 20, 2026
README
Data Science Resources
For interview preparation and learning
Table of Contents:
- Interview Preparation
- Algorithms and Data Structures
- Programming
- SQL
- Machine Learning
- MLOps
- Deep Learning
- Generative AI
- NLP
- Computer Vision
- Graphs
- Reinforcement Learning
- RecSys
- Time Series
- Big Data
- System Design
- Machine Learning System Design
- Math
- Other
Interview Preparation
Questions
Data Science
- OVER 100 Data Scientist Interview Questions and Answers by Terence Shin
- Data Science Interviews by Alexey Grigoriev
- Data-Science-Interview-Questions by Youssef Hosni
- Data-Science-Interview-Preparation-Resources by Youssef Hosni
- Data science interview questions with answers
- 120 Data Science Interview Questions
- Вопросы для интервью по специальности Data Science
- Interview Questions from interviewquery.com
- Вопросы с собеседовании по машинному обучению
- 100 вопросов для подготовки к собесу Data Science
- The Data Science Interview Book
- Collection of Interview Questions
- Top 100 Data science interview questions
- 100 вопросов c собесов в Data Science и ML
- ML-Interview
Machine Learning
- Machine Learning FAQ by Sebastian Raschka
- Machine Learning FAQ by X
- Minimum Viable Study Plan for Machine Learning Interviews
- A Guide for Machine Learning Technical Interviews (FAANG Companies)
- Introduction to Machine Learning Interviews Book by Chip Huyen + Answers
- Machine Learning Interviews by Susan Shu Chang
- 100 Machine Learning interview questions 2024
- Crack the top 40 machine learning interview questions
Deep Learning
- Вопросы тестов по курсу «Глубокое обучение» Александр Дьяконов
- 50 Deep Learning interview questions 2024
- 50 Computer Vision interview questions 2024
SQL
NLP
- 63 Must-Know LLMs Interview Questions
- 100 вопросов про NLP
- 50 NLP (Natural Language Processing) interview questions 2024
- LLM (ML) Job Interviews - Resources
Programming
- Awesome interview questions repository
- Coding Interview University
- Шпаргалка для технического собеседования
- Моя любимая задача для собеседований по программированию
- Top 100 Python ML Interview Questions
Behavioural interview
Courses
Questions
- Популярные HR вопросы
- Awesome Behavioral Interviews
- Amazon Behavioral Interview Questions Guide
- 41 Behavioural Interview Questions You Must Know (Best Answers Included)
- The 50 Amazon Behavioral Interview Questions with Answers
- Behavioural Questions Answered
Mock Interviews and Pieces of Advice
- Валерий Бабушкин:
- Episode 07: Intro to Behavioural Interviews
- Dan Croitor
- Tips for answering few tricky behavioural interview questions
- How to Answer Top Interview Questions
- Interview Warmup by Google
English
- Как пройти собеседование на английском языке | StarTalk
- Лена Кочева и Таня Дурова - Как эффективно подготовиться к собеседованию на английском
- Как я устроился в Амазон и перестал переживать за свой английский
Home Assignments
- Тестовые задания
- Сборник тестовых заданий для Product Analyst и Data Analyst
- Тестовые задания по DS
- Список тестовых заданий для прокачки
Tips
- How I Got 15 More Data Science Interviews in One Month?
- Как я проходил собеседования на Machine Learning Engineer
- Google ML engineer interview: the only post you’ll need to read
- Amazon Data Scientist Interview Guide
- The Trimodal Nature of Software Engineering Salaries in the Netherlands and Europe
- Гайд по подготовке CV+Portfolio+Self Presentation+Home task
- Методика STAR для прохождения структурированных собеседований
- Гостевое выступление Тати Габрусевой, Staff Machine Learning Engineer, NLP, LinkedIn 25.05.2022
- Полезные ссылки: Как проходить интервью в DS от Айры
- FAANG Interview. Бортовые заметки сообщества
- You should Review These Topics Before Data Science Technical Interview
- Crack the Amazon Data Scientist Interviews | Ex-FAANG Data Scientist by Dan Lee
- Дайджест уходящего года: релокейт в Европу и США, главное о карьере и сверхзанятости
- How I got in to Amazon, Microsoft, Google. All from studying these resources
- Articles, books and videos to help get well-paying tech jobs by TechPays
- How I Cracked the Meta Machine Learning Engineering Interview
- What we look for in a resume by Chip Huyen
- Не принимай оффер в Data Science, пока…
- Стратегия поиска работы за границей: что писать, с кем говорить и к чему готовиться
- Data Science Interview Guide
- Tech Interview Handbook. Free curated interview preparation materials for busy people
- Разбор резюме Data Scientist в прямом эфире
- А как собеседоваться в 2023?
- Amazon, Microsoft, Facebook, Tesla, Lyft — история поиска работы мечты, или «Вредные» советы для карьерного развития
- Как нанимать сотрудников класса А? Выжимка из книги "Who: The A Method For Hiring"
- Your personal guide to Software Engineering technical interviews
- Что делать при отказе на собеседовании?
- FAANG Interview Chronicles. My experience and recommendations
- How to Crack Machine learning Interviews at FAANG!
- How to Find a Data Science Job in 2024 (with experience)
- Applied ML Scientist. Юра — Рыночек 0:10 + Applied ML Scientist. 2 тайм. Юра — Рыночек 2:16 + Домашнее задание vs. Онлайн-кодинг
- Как я готовился к собеседованию на позицию Senior ML Engineer by Zarin Gleb
- How I landed 18 FAANG+ software engineer offers after not interviewing for 5 years
- На что обращать внимание на алгоритмических секциях собеседований
- Что нужно знать, чтобы найти работу, пройти собеседование и выбрать оффер
- Interview Query is the best adaptive learning platform for Data Scientists by Data Scientists
- Как не провалить интервью. Исследование из Стэнфорда о пользе самоуверенности
- Собеседования в ML
- Евгений Смирнов | Десять вопросов, которые нужно задать перед трудоустройством
- Стрим про карьеру, собеседования и бигтех
- Guide to ML Engineer Job Hunting
- ML/Research Engineer interviews 2025
Resources
- На этой странице вы узнаете про Data Science направление в Маркете, наш стек технологий, этапы собеседований и материалы, которые могут пригодиться
- IT-собеседование в Тинькофф
- Материалы для подготовки по машинному обучению от Тинькофф
- Что надо знать сотруднику Цельса?
- Программа Академии Data Science от Тинькофф
Courses
Other
- Data Scientist total compensation and salaries in the Netherlands
- Programmer Competency Matrix
- Just know stuff (or, how to achieve success in a machine learning PhD)
- A Guide to Data Roles
- Dropbox Engineering Career Framework
- Nurturing a non-linear career
- Path to Senior Engineer Handbook
- Interview Principals
- The difference between good and great engineers
- Understanding and Cracking Data Science Interviews
- Собеседования от karpov.courses
- Data-Science-Interview-Resources
Algorithms and Data Structures
Platforms
- LeetCode
foundations- Leetcode Patterns
foundationsList of questions with patterns + tips - LeetCode Explore
foundations
- Leetcode Patterns
- Codewars
- HackerRank
- CodeAbbey
- CodeRun
lang:rusИнструмент для подготовки к очному собеседованию в Яндексе. Задачи очень похожи на те, что будут на интервью. - Other
Courses
- Algoprog
lang:rus - Яндекс:
- Computer Science Center:
- Подготовься к алгоритмическому собеседованию за 30 недель
- Introduction To Algorithms by MIT
foundations - Algorithms + Data Structures from CS50's Introduction to Computer Science
Resources
- Тренировки по алгоритмам от Яндекса:
- NeetCode. A better way to prepare for coding interviews.
- The Algorithms. Open Source resource for learning Data Structures & Algorithms and their implementation in any Programming Language
- Алгоритмы и структуры данных простыми словами
- Алгоритмика
- Leetcode. Company-wise questions
- Code Abbey Problems
- Unlocking Algorithm Efficiency: A Comprehensive Guide to Time and Space Complexity
- Data Structures Reference
- An Executable Data Structures Cheat Sheet for Interviews
- Coding Interview Guide
- Algorithmic Thinking
- Algorithm Notes
- Coding Interview University
- Tech Interview Cheat Sheet
- Comprehensive Data Structure and Algorithm Study Guide
- Data Structures & Algorithms by Google
- Design and Analysis of Algorithms
- Algorithms for Competitive Programming
Articles
- Как проходят алгоритмические секции на собеседованиях в Яндекс
- How to effectively use LeetCode to prepare for interviews
- How To Answer Algorithm Interview Questions? The REACTO Method
Books
- Grokking Algorithms. An illustrated guide for programmers and other curious people
- Elements of Programming Interviews in Python: The Insiders' Guide
- Cracking the Coding Interview: 189 Programming Questions and Solutions
- Problem Solving with Algorithms and Data Structures using Python by Brad Miller and David Ranum, Luther College
- Competitive Programmer's Handbook by Antti Laaksonen
- Competitive Programming by Steven Halim
Programming
General
Clean Code
- Мартин Р. Чистый код: создание, анализ и рефакторинг / Robert C. Martin. Clean Code: A Handbook of Agile Software Craftsmanship
- Стив Макконнелл. Совершенный код. Мастер-класс / Steve McConnell. Code Complete: A Practical Handbook of Software Construction
Python
Courses
- CS50’s Introduction to Programming with Python
foundations - CS50’s Introduction to Artificial Intelligence with Python
foundations - Основы Python
- Python: основы и применение
- Программирование на Python
- Поколение Python:
Theory
- Python Tutorial for Beginners (with mini-projects)
- What the f*ck Python! Exploring and understanding Python through surprising snippets
- Comprehensive Python Cheatsheet
- Python Koans. An interactive tutorial for learning the Python programming language by making tests pass
- Full Speed Python. Learning Python using a practical approach
- The Hitchhiker’s Guide to Python!
- A collection of design patterns and idioms in Python
- Python Cheatsheet
- Write faster Python code, and ship your code faster
Questions
- 53 Python Interview Questions and Answers
- Python: вопросы на собеседовании:
Other
Practice
- Задачи по Python и машинному обучению
- Project Based Learning
- FastAPI Best Practices
- Python Training by J.P.Morgan
SQL
Courses
- Интерактивный тренажер по SQL
- Пакет SQL курсов:
- Основы SQL
- Продвинутый SQL
- Проектирование баз данных
- PostgreSQL Tutorial for Beginners
- Оконные функции SQL
- SQL Tutorial
- The Ultimate SQL Guide
- CS50’s Introduction to Databases with SQL
Practice
- Онлайн тренажер SQL Academy
- Ace the SQL Interview
- Practice SQL
- SQLBolt. Learn SQL with simple, interactive exercises.
- SQL Tutorial by w3schools
- PostgreSQL Exercises
- The Querynomicon. An Introduction to SQL for Wary Data Scientists
- SQL Challenges
- SQL Squid Game
Machine Learning
Sites
- Machine Learning Mastery by Jason Brownlee
- Машинное обучение для людей. Разбираемся простыми словами
- Анализ малых данных
- Kaggle Competitions
- The Illustrated Machine Learning
- MLU-EXPLAIN
- ML Code Challenges
Courses
- Open Machine Learning Course by Yury Kashnitsky
- Машинное обучение (курс лекций, К.В.Воронцов)
- Прикладные задачи анализа данных (курс лекций, А.Г.Дьяконов) video
- Алгоритмы Машинного обучения с нуля
- Stanford CS229: Machine Learning by Andrew Ng
- Kaggle Learn
- Google Machine Learning Courses
- End to End Machine Learning by Brandon Rohrer
- Машинное Обучение в Python: Большой Курс для Начинающих
- Обучение работе с ML‑сервисами от Yandex Cloud
- Introduction to Machine Learning (I2ML)
- MACHINE LEARNING @ VU
- Practical Machine Learning
- Machine Learning Engineer Learning Path
- Machine Learning Course Notes
Books
- Учебник по машинному обучению от ШАД
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Machine Learning Simplified: A gentle introduction to supervised learning by Andrew Wolf
- The Kaggle Book
- Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
- Clean Machine Learning Code
- Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values
- Interpretable Machine Learning. A Guide for Making Black Box Models Explainable by Christoph Molnar
- Machine Learning Q and AI. Expand Your Machine Learning & AI Knowledge With 30 In-Depth Questions and Answers by Sebastian Raschka
- Reliable Machine Learning: Applying SRE Principles to ML in Production by Cathy Chen
- Models Demystified. A Practical Guide from Linear Regression to Deep Learning
- Machine Learning from Scratch. Derivations in Concept and Code
- Supervised Machine Learning for Science by Christoph Molnar & Timo Freiesleben
- Machine Learning Refined: Notes, Exercises, Presentations, and Sample Chapters
- Дьяконов А.Г. "Машинное обучение и анализ данных"
- Виктор Владимирович Китов. Машинное обучение
Cheetsheets
- Supervised Learning
- Unsupervised Learning
- Tips and Tricks
- Machine learning cheat sheet
- Machine Learning Glossary
Articles
- Anthology of Modern Machine Learning
- Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
- ML Papers of The Week by DAIR.AI
- word2vec Parameter Learning Explained
- Ilya 30u30
- Introduction to Machine Learning by Laurent Younes
- A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets
Applied ML
- geographic Data Science with Python
- WTTE-RNN - Less hacky churn prediction
- Прикладной анализ данных в социальных науках
- Ансамбли в машинном обучении
- Reflecting on 18 years at Google
- Machine Learning for Imbalanced Data
- Валидация моделей машинного обучения
- Do Machine Learning Models Memorize or Generalize?
Blogs
- Applying Machine Learning by Eugene Yan
- Eugene Yan
- Data Science Project Quick-Start
- Writing is Learning: How I Learned an Easier Way to Write
- Uncommon Uses of Python in Commonly Used Libraries
- Andrey Lukyanenko
- A third life of a personal pet-project for handwritten digit recognition
- Dan Bader
- Matthew Brett
- Introducing principal component analysis
- Amit Chaudhary
- Andrej Karpathy
- Jay Alammar
- Lilian Weng
- Data science blogs
- Keep the gradient flowing by Fabian Pedregosa
- Irrational Exuberance by Will Larson
Feature Engineering
- Лекция по курсу ММО - 24.03.2021, Отбор признаков (Feature selection)
- Kaggle Tips for Feature Engineering and Selection | by Gilberto Titericz | Kaggle Days Meetup Madrid
- featurewiz is the best feature selection library for boosting your machine learning performance with minimal effort and maximum relevance using the famous MRMR algorithm
- Feature Ranking and Selection
- Feature Engineering A-Z
- Feature Selection in Machine Learning by Soledad Galli
Tutorials
- CatBoost - An In-Depth Guide
- Введение в библиотеку Transformers и платформу Hugging Face
- Build a Telegram chatbot with any AI model under the hood
- The Illustrated Machine Learning
- ML Primer by Boris Tseytlin
- Decision Trees. The unreasonable power of nested decision rules
- Ensemble Methods and Decision Trees
- Awesome CatBoost
Blog posts
- Векторное представление товаров Prod2Vec: как мы улучшили матчинг и избавились от кучи эмбеддингов
- Some characteristics of best-in-class ML portfolio projects
- Как метод подмены задачи борется с несовершенством данных (и мира)
- Feature Selection — Exhaustive Overview by Danny Butvinik
- A highly anticipated Time Series Cross-validator is finally here
- Интерпретация моделей и диагностика сдвига данных: LIME, SHAP и Shapley Flow
- Мое первое серебро на Kaggle или как стабилизировать ML модель и подпрыгнуть на 700 мест вверх
- Soccer Analytics 2022 Review
- Эй-Яй, крипта, MLOps и командный пет-проджект by yorko
- Understanding UMAP
- A new perspective on Shapley values, part I: Intro to Shapley and SHAP
- A new perspective on Shapley values, part II: The Naïve Shapley method
- 10 первых ошибок в карьере ML-инженера
- Understanding the Bias-Variance Tradeoff by Seema Singh
- The “Bias-Variance Trade-Off” Explained Practically (In Python)
- Модельный риск: как увеличить эффективность работы ML моделей в большой компании
- Эффективные ансамбли
- Reflecting on 18 years at Google
- Как не перестать быть data driven из-за data driften, или Пару слов о дрейфе данных
- В чём польза теоремы Байеса — или как управлять неопределённостью
- AI by Hand with Prof. Tom Yeh for AI Professionals
Other
- StatQuest with Josh Starmer
- A new perspective on Shapley values:
- Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning by Sebastian Raschka
- How to avoid machine learning pitfalls: a guide for academic researchers by Michael A. Lones
- Core Machine Learning Skills
- Discover machine learning, data science & robotics competitions
MLOps
General
- FastAPI for Machine Learning: Live coding an ML web application with the creator of FastAPISebastián Ramírez
- Build your MLOps stack
- MLOps и production подход к ML исследованиям
- MLOps Guide
- MLOps guide by Chip Hyyen
- MLOps Zoomcamp
- MLOps и production подход к ML исследованиям 2.0: Видео + Курс
- THE ULTIMATE DOCKER COMPOSE CHEAT SHEET
- Practitioner's guide to MLOps by Google
Other
Deep Learning
Books
- The Little Book of Deep Learning by François Fleuret
- Deep Learning with Python by François Chollet
- Multimodal Deep Learning
- Dive into Deep Learning I prefer going through this book using Amazon SageMaker
- Understanding Deep Learning by Simon J.D. Prince
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger
- Deep Learning. Foundations and Concepts by Chris Bishop and Hugh Bishop
- Mastering PyTorch by Ashish Ranjan Jha
- The Tensor Cookbook by Thomas Dybdahl Ahle
- Deep Learning and Computational Physics
- Виктор Владимирович Китов. Глубокое обучение
Courses
- Deep Learning Specialization
- MIT 6.S191 Introduction to Deep Learning
- Full Stack Deep Learning - Course 2022
- 11-785 Introduction to Deep Learning from Carnegie Mellon University
- Neuromatch Academy: Deep Learning
- Efficient Deep Learning Systems by Yandex School of Data Analysis
- Short Courses by DeepLearning.AI
- TinyML and Efficient Deep Learning Computing
- Practical Deep Learning
- Practical Deep Learning for Coders part 2: Deep Learning Foundations to Stable Diffusion
- PyTorch for Deep Learning & Machine Learning (video) + Learn PyTorch for Deep Learning: Zero to Mastery book (site)
- Deep Learning Fundamentals by Sebastian Raschka and Lightning AI
- Future of AI is Foundation Models & Self-Supervised Learning
- Artificial Intelligence for Beginners
- 11-785 Introduction to Deep Learning + 11785 Spring 2024 Lectures
- Stanford CS 230 ― Deep Learning
- Convolutional Neural Networks
- Recurrent Neural Networks
- Deep Learning Tips and Tricks
- CS 330: Deep Multi-Task and Meta Learning
- Deep Learning with Catalyst
- Practical DL
- Deep Learning from the Foundations by fast.ai
- PyTorch Tutorials - Complete Beginner Course
- Школа глубокого обучения
- Neural Networks: Zero to Hero by Andrej Karpathy
- CUDA Mode: Lectures + Video
- Deep Learning IP-Paris
- CS565600 Deep Learning. Fundamentals of machine learning, deep learning, and AI
- Introduction to Deep Learning (I2DL) (IN2346)
- CS 886: Recent Advances on Foundation Models
- DEEP LEARNING COURSE
- CS236 Deep Generative Models
- 10-202: Introduction to Modern AI
Tutorials
- Introduction to Deep Learning by Sebastian Raschka
- Коллекция ручных задачек о нейросетях
- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks by Sebastian Raschka
- Deep Learning Tuning Playbook by Google
- A Step by Step Backpropagation Example
- PyTorch Fundamentals by Microsoft
- labml.ai Annotated PyTorch Paper Implementations
- Grokking PyTorch
- The Incredible PyTorch
- AI Fundamentals. Concepts, Definitions, Terms
- A Guide to Production Level Deep Learning
- A Gentle Introduction to torch.autograd
- Introduction to deep learning
- PyTorch internals
Blogs & Blog posts
- WHAT IS TORCH.NN REALLY? by Jeremy Howard, fast.ai
- О «раздутом пузыре» нейросетей
- Cтатьи от команды DeepSchool
- Полезные материалы про PyTorch
- AstraBlog
Other
Generative AI
NLP
Books
- Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra adn Thomas Wolf
- Speech and Language Processing by Dan Jurafsky and James H. Martin
- Transformers for Natural Language Processing by Denis Rothman
Courses
- Нейронные сети и обработка текста
- Stanford CS224N: NLP with Deep Learning + Videos + Notes
- NLP Course | For You by Lena Voita + YSDA Natural Language Processing course
- Hugging Face course
- Natural Language Processing course by Valentin Malykh
- Stanford LSA 311: Computational Lexical Semantics by Dan Jurafsky
- Stanford CS224U: Natural Language Understanding
- Введение в обработку естественного языка
- Stanford CS 224V Conversational Virtual Assistants with Deep Learning
- CS11-711 Advanced Natural Language Processing (at Carnegie Mellon University's Language Technology Institute) + Video + Assignments
- Linguistics for Language Technology
General
- Recommendations for Getting Started with NLP by Elvis
- Чат по NLP
- awesome-nlp. A curated list of resources dedicated to Natural Language Processing
- NLP Cheatsheet: Master NLP
Large Language Models (LLMs) / Transformers
- Stanford Webinar - GPT-3 & Beyond
- Transformer Recipe by Elvis Saravia
- Transformer, explained in detail by Igor Kotenkov
- The Practical Guides for Large Language Models
- State of GPT by Andrej Karpathy
- LLM University by Cohere
- CS324 - Large Language Models
- Generative AI exists because of the transformer. This is how it writes
- Training & Fine-Tuning LLMs for Production
- LLMOps: Building Real-World Applications With Large Language Models
- A Survey of Large Language Models
- Transformers Tutorials
- Ruformers/Руформеры
- Схема энкодера архитектуры Трансформер
- Large Language Model Course
- Insights from Finetuning LLMs with Low-Rank Adaptation by Sebastian Raschka
- LLM Bootcamp - Spring 2023
- ChatGPT Course – Use The OpenAI API to Code 5 Projects
- Self-Attention & Transformers (CS 224n: Natural Language Processing with Deep Learning)
- Build a Large Language Model (From Scratch) by Sebastian Raschka
- Hands-on LLMs Course
- LLaMA-Factory. Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)
- Open LLMs. A list of open LLMs available for commercial use
- Overview of Large Language Models
- Mastering RAG: How To Architect An Enterprise RAG System
- Исчерпывающий гайд по опенсорсным языковым моделям
- Open-Source AI Cookbook
- Building LLM applications for production
- LLM Visualization
- The Illustrated Transformer by Jay Alammar
- Learn to Train and Deploy a Real-Time Financial Advisor
- The Annotated Transformer + Neural networks by 3Blue1Brown
- Elicit Machine Learning Reading List
- LLM Zoomcamp
- llama3 implemented from scratch
- LLM Twin Course: Building Your Production-Ready AI Replica
- Educational resources on LLMs
- A Visual Guide to Quantization. Demystifying the Compression of Large Language Models
- Super Study Guide: Transformers & Large Language Models
- Advanced RAG Techniques: Elevating Your Retrieval-Augmented Generation Systems
- CUDA-Free Inference for LLMs
- Изучаю LLM by Evgenii Nikitin
- What is the Role of Small Models in the LLM Era: A Survey
- Building LLMs from the Ground Up: A 3-hour Coding Workshop
- AI Prompt Engineering: A Deep Dive
- LLM Resources Hub
- 🤗 AI Agents Course by Hugging Face
- LLM Engineering Essentials
- Hugging Face Reasoning Course
- LLM Engineering Essentials course by Nebius Academy
- CS336: Language Modeling from Scratch + Video
Embeddings
- Новый взгляд на оценку русскоязычных моделей: обновлённый бенчмарк ruMTEB и лидерборд
- What are embeddings by Vicki Boykis
- Learn to Love Working with Vector Embeddings by Pinecone
- The 1950-2024 Text Embeddings Evolution Poster
Reading papers with AI
- Explainpaper
- ChatPDGF
- Reimagine Research
- Discover scientific knowledge and stay connected to the world of science
- Get scientific answers by asking millions of research papers
- Otio
Prompt Engineering
- Prompt Engineering Guide + Prompt Engineering Guide
- Prompt injection with Gandalf
- Prompt Engineering
- Prompt Engineering Guide
- Awesome ChatGPT Prompts
- Advanced Prompt Engineering
- Prompt engineering Guide by Open.ai
- Prompt Of The Year: 2023
- Anthropic's Prompt Engineering Interactive Tutorial
- The Prompt Report: A Systematic Survey of Prompting Techniques
- Prompt Engineering Guide by Antrophic
- Claude 101
- AI Coding Playbook
Tutorials
- Train and Fine-Tune Sentence Transformers Models
- Working With Text Data using Sklearn + Text feature extraction using Sklearn
- minbpe. Minimal, clean code for the (byte-level) Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization
Blog posts
- Мультиклассификация экстремально коротких текстов классическими методами машинного обучения
- Рейтинг русскоязычных энкодеров предложений
- Как определять пользовательские намерения, о которых мы узнали 5 минут назад
- Самая большая BERT-подобная модель на русском, которая поместится на ваш компьютер
- ChatGPT как инструмент для поиска: решаем основную проблему
- GPT in 60 Lines of NumPy
- What Is ChatGPT Doing … and Why Does It Work?
- From GPT-3 to ChatGPT: Training Language Models on Instructions and Human Feedback
- Кто такие LLM-агенты и что они умеют?
Articles
- Word2Vec, Mikolov et al., Efficient Estimation of Word Representations in Vector Space
- FastText, Bojanowski et al., Enriching Word Vectors with Subword Information
- Attention, Bahdanau et al., Neural Machine Translation by Jointly Learning to Align and Translate
- Transformers, Vaswani et al., Attention Is All You Need
- BERT, Devlin et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- GPT-2, Radford et al., Language Models are Unsupervised Multitask Learners
- GPT-3, Brown et al, Language Models are Few-Shot Learners
- LaBSE, Feng et al., Language-agnostic BERT Sentence Embedding
- CLIP, Radford et al., Learning Transferable Visual Models From Natural Language Supervision
- RoPE, Su et al., RoFormer: Enhanced Transformer with Rotary Position Embedding
- LoRA, Hu et al., LoRA: Low-Rank Adaptation of Large Language Models
- InstructGPT, Ouyang et al., Training language models to follow instructions with human feedback
- Scaling laws, Hoffmann et al., Training Compute-Optimal Large Language Models
- FlashAttention, Dao et al., FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- NLLB, NLLB team, No Language Left Behind: Scaling Human-Centered Machine Translation
- Q8, Dettmers et al., LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
- Self-instruct, Wang et al., Self-Instruct: Aligning Language Models with Self-Generated Instructions
- Alpaca, Taori et al., Alpaca: A Strong, Replicable Instruction-Following Model
- LLaMA, Touvron, et al., LLaMA: Open and Efficient Foundation Language Models
Packages
- Turbo-Alignment
Turbo-Alignment is a library designed to streamline the fine-tuning and alignment of large language models, leveraging advanced techniques to enhance efficiency and scalability - LitGPT
Every LLM is implemented from scratch with no abstractions and full control, making them blazing fast, minimal, and performant at enterprise scale.
Computer Vision
- Нейронные сети и компьютерное зрение
- CS231n: Deep Learning for Computer Vision + Videos
- EECS 442: Computer Vision + Videos
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola and William T. Freeman
Graphs
Reinforcement Learning
RecSys
Courses
Books
- К. Фальк. Рекомендательные системы на практике / Practical Recommender Systems by Kim Falk
- Personalized Machine Learning
Other
- Авито. Рекомендации
- Recommenders. Best Practices on Recommendation Systems
- Рекомендательные системы
- Рекомендательные системы: идеи, подходы, задачи
Packages
Time Series
- Временные ряды
- Topic 9. Time Series Analysis with Python
- Прогнозирование временных рядов
- Time Series
- Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM, Scikit-learn and CatBoost by Joaquín Amat Rodrigo, Javier Escobar Ortiz
- ARIMA and SARIMAX models with Python by Joaquín Amat Rodrigo, Javier Escobar Ortiz
- Груздев А.В., Рафферти Г. Прогнозирование временных рядов с помощью Prophet, sktime, ETNA и Greykite
- Forecasting: Principles and Practice (3rd ed)
Big Data
Books
- Перрен Ж.Ж. Spark в действии / Spark in Action by Jean-Georges Perrin
- Learning Spark
- Data Analysis with Python and PySpark
Other
- Анализируем данные с помощью фреймворка Spark
- PySpark для аналитика. Как правильно просить ресурсы и как понять, сколько нужно брать
- PySpark для аналитика. Как выгружать данные с помощью toPandas и его альтернатив
- Spark Architecture by FaangTalk
- SPARK для «малышей»
System Design
- The System Design Primer
- Algorithms you should know before you take system design interviews
- Top 14 System Design interview questions for software engineers
- System Design for Interviews and Beyond
- System Design
- System Design by Karan Pratap Singh
- System Design 101
- Systems Design Interview Guide
- Designing Distributed Systems
- System Design Interview – Step By Step Guide
- Designing Data-Intensive Applications and its related books
- A Senior Engineer's Guide to the System Design Interview
- Intro to System Design
- System Design Newsletter
- Awesome System Design
- Design Docs at Google
- System Design Fight Club
- What happens when you type google.com into your browser's address box and press enter?
Machine Learning System Design
- Stanford CS 329S: Machine Learning Systems Design
- ML System Design
- Шаблон ML System Design Doc от телеграм-канала Reliable ML ML System Design - ML System Design Doc. Лекция-бонус от Reliable ML
- Machine learning design primer by Ibragim Badertdinov
- How to Write Design Docs for Machine Learning Systems -> ml-design-doc
- Что я бы хотел знать про ML System Design раньше
- Почему анализ ошибок – это начало разработки ML системы, а не конец?
- ML Systems Design Interview Guide
- Machine Learning System Design by Valerii Babushkin and Arseny Kravchenko
- Designing Machine Learning Systems by Chip Huyen
- Machine Learning Engineering Online Book
- ML system design: 500 case studies to learn from
- Machine Learning System Design Interview by Ali Aminian, Alex Xu + Solutions
- The 9-Step ML System Design Formula
- CSCE 585 Machine Learning Systems: Lectures + Video
- Machine Learning in Production: From Models to Products
- ML System Design by Machine Learning REPA
- ML for Developers
- Machine Learning System Design Doc Examples
- Machine Learning in Production by Carnegie Mellon University
- Crack ML System Design Interviews Like a Pro
Math
General
- В Data Science не нужна математика (Почти)
- Mathematics Of Machine Learning | MIT
- How to beat maths anxiety
- Engineering Math: Differential Equations and Dynamical Systems
- Mathematics for Machine Learning
- The Complete Mathematics of Neural Networks and Deep Learning
- Математика для анализа данных
- Pen and Paper Exercises in Machine Learning by Michael Gutmann
Linear Algebra
- Introduction to Linear Algebra by Gilbert Strang
- The Matrix Calculus You Need For Deep Learning
- Linear Algebra for Data Science
- immersive linear algebra by J. Ström, K. Åström, and T. Akenine-Möller
- Linear Algebra Review and Reference by Zico Kolter
- Linear Algebra for Data Science
- Engineering Math: Vector Calculus and Partial Differential Equations
- Engineering Math: Crash Course in Complex Analysis
Probability and Statistics
- Основы статистики + Основы статистики. Часть 2 + Основы статистики. Часть 3
- Statistics and probability from Khan Academy
- CS109: Probability for Computer Scientists + Course Book + Video
- Математическая статистика и AB-тестирование
- 12 бесплатных материалов по статистике – разберется каждый
- Математическая статистика. Начало
- Z Statistics by Justin Zeltzer
- The Beginner's Guide to Statistical Analysis by Scribbr
- Прикладная статистика. Репозиторий для линейки онлайн-курсов по статистике
- Multivariate statistics
- R2D3 is an experiment in expressing statistical thinking with interactive design
- Statistical Rethinking
- Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks by Will Kurt
- Seeing theory. A visual introduction to probability and statistics
- The Book of Statistical Proofs
- Lessons in Statistical Thinking by Daniel Kaplan
- Прикладная статистика
- Statistics For Applications + Video
- Вся основная теория по A/B-экспериментам
- Statistics 110: Probability
- Introduction to Statistical Learning using Python
- Economertrics Notes by Peter Hull
- The Cartoon Guide to Statistics
- Probability Bootcamp
Causal Inference
- Causal Inference: What If
- Causal Inference and Its Applications in Online Industry
- Applied Causal Inference Powered by ML and AI
A/B Tests
Courses
- A/B TESTING
- Курс по Прикладной статистике от Академии Аналитиков Авито
- A/B Testing & Experimentation Roadmap
General
- Trustworthy Online Controlled Experiments by Ron Kohavi
- Подборка ссылок по A/B тестированию от Валеры Бабушкина
- Most cited sources in A/B Testing by Ron Kohavi
- A/B Testing RoadMap
- Чеклист А/Б эксперимента + Шаблон проведения А/Б эксперимента
- Practitioner’s Guide to Statistical Tests by VK Team
Blog posts
- Шесть причин, почему ваши A/B-тесты не работают
- АБ-тесты — это не только ценный мех… Но еще и процессы
- Стратификация. Как разбиение выборки повышает чувствительность A/B теста
- 50 оттенков линейной регрессии, или почему всё, что вы знаете об A/B тестах, помещается в одно уравнение
Metrics
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Data Science Resources for interview preparation and learning
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updated Sep 20, 2026
README
Data Science Resources
For interview preparation and learning
Table of Contents:
- Interview Preparation
- Algorithms and Data Structures
- Programming
- SQL
- Machine Learning
- MLOps
- Deep Learning
- Generative AI
- NLP
- Computer Vision
- Graphs
- Reinforcement Learning
- RecSys
- Time Series
- Big Data
- System Design
- Machine Learning System Design
- Math
- Other
Interview Preparation
Questions
Data Science
- OVER 100 Data Scientist Interview Questions and Answers by Terence Shin
- Data Science Interviews by Alexey Grigoriev
- Data-Science-Interview-Questions by Youssef Hosni
- Data-Science-Interview-Preparation-Resources by Youssef Hosni
- Data science interview questions with answers
- 120 Data Science Interview Questions
- Вопросы для интервью по специальности Data Science
- Interview Questions from interviewquery.com
- Вопросы с собеседовании по машинному обучению
- 100 вопросов для подготовки к собесу Data Science
- The Data Science Interview Book
- Collection of Interview Questions
- Top 100 Data science interview questions
- 100 вопросов c собесов в Data Science и ML
- ML-Interview
Machine Learning
- Machine Learning FAQ by Sebastian Raschka
- Machine Learning FAQ by X
- Minimum Viable Study Plan for Machine Learning Interviews
- A Guide for Machine Learning Technical Interviews (FAANG Companies)
- Introduction to Machine Learning Interviews Book by Chip Huyen + Answers
- Machine Learning Interviews by Susan Shu Chang
- 100 Machine Learning interview questions 2024
- Crack the top 40 machine learning interview questions
Deep Learning
- Вопросы тестов по курсу «Глубокое обучение» Александр Дьяконов
- 50 Deep Learning interview questions 2024
- 50 Computer Vision interview questions 2024
SQL
NLP
- 63 Must-Know LLMs Interview Questions
- 100 вопросов про NLP
- 50 NLP (Natural Language Processing) interview questions 2024
- LLM (ML) Job Interviews - Resources
Programming
- Awesome interview questions repository
- Coding Interview University
- Шпаргалка для технического собеседования
- Моя любимая задача для собеседований по программированию
- Top 100 Python ML Interview Questions
Behavioural interview
Courses
Questions
- Популярные HR вопросы
- Awesome Behavioral Interviews
- Amazon Behavioral Interview Questions Guide
- 41 Behavioural Interview Questions You Must Know (Best Answers Included)
- The 50 Amazon Behavioral Interview Questions with Answers
- Behavioural Questions Answered
Mock Interviews and Pieces of Advice
- Валерий Бабушкин:
- Episode 07: Intro to Behavioural Interviews
- Dan Croitor
- Tips for answering few tricky behavioural interview questions
- How to Answer Top Interview Questions
- Interview Warmup by Google
English
- Как пройти собеседование на английском языке | StarTalk
- Лена Кочева и Таня Дурова - Как эффективно подготовиться к собеседованию на английском
- Как я устроился в Амазон и перестал переживать за свой английский
Home Assignments
- Тестовые задания
- Сборник тестовых заданий для Product Analyst и Data Analyst
- Тестовые задания по DS
- Список тестовых заданий для прокачки
Tips
- How I Got 15 More Data Science Interviews in One Month?
- Как я проходил собеседования на Machine Learning Engineer
- Google ML engineer interview: the only post you’ll need to read
- Amazon Data Scientist Interview Guide
- The Trimodal Nature of Software Engineering Salaries in the Netherlands and Europe
- Гайд по подготовке CV+Portfolio+Self Presentation+Home task
- Методика STAR для прохождения структурированных собеседований
- Гостевое выступление Тати Габрусевой, Staff Machine Learning Engineer, NLP, LinkedIn 25.05.2022
- Полезные ссылки: Как проходить интервью в DS от Айры
- FAANG Interview. Бортовые заметки сообщества
- You should Review These Topics Before Data Science Technical Interview
- Crack the Amazon Data Scientist Interviews | Ex-FAANG Data Scientist by Dan Lee
- Дайджест уходящего года: релокейт в Европу и США, главное о карьере и сверхзанятости
- How I got in to Amazon, Microsoft, Google. All from studying these resources
- Articles, books and videos to help get well-paying tech jobs by TechPays
- How I Cracked the Meta Machine Learning Engineering Interview
- What we look for in a resume by Chip Huyen
- Не принимай оффер в Data Science, пока…
- Стратегия поиска работы за границей: что писать, с кем говорить и к чему готовиться
- Data Science Interview Guide
- Tech Interview Handbook. Free curated interview preparation materials for busy people
- Разбор резюме Data Scientist в прямом эфире
- А как собеседоваться в 2023?
- Amazon, Microsoft, Facebook, Tesla, Lyft — история поиска работы мечты, или «Вредные» советы для карьерного развития
- Как нанимать сотрудников класса А? Выжимка из книги "Who: The A Method For Hiring"
- Your personal guide to Software Engineering technical interviews
- Что делать при отказе на собеседовании?
- FAANG Interview Chronicles. My experience and recommendations
- How to Crack Machine learning Interviews at FAANG!
- How to Find a Data Science Job in 2024 (with experience)
- Applied ML Scientist. Юра — Рыночек 0:10 + Applied ML Scientist. 2 тайм. Юра — Рыночек 2:16 + Домашнее задание vs. Онлайн-кодинг
- Как я готовился к собеседованию на позицию Senior ML Engineer by Zarin Gleb
- How I landed 18 FAANG+ software engineer offers after not interviewing for 5 years
- На что обращать внимание на алгоритмических секциях собеседований
- Что нужно знать, чтобы найти работу, пройти собеседование и выбрать оффер
- Interview Query is the best adaptive learning platform for Data Scientists by Data Scientists
- Как не провалить интервью. Исследование из Стэнфорда о пользе самоуверенности
- Собеседования в ML
- Евгений Смирнов | Десять вопросов, которые нужно задать перед трудоустройством
- Стрим про карьеру, собеседования и бигтех
- Guide to ML Engineer Job Hunting
- ML/Research Engineer interviews 2025
Resources
- На этой странице вы узнаете про Data Science направление в Маркете, наш стек технологий, этапы собеседований и материалы, которые могут пригодиться
- IT-собеседование в Тинькофф
- Материалы для подготовки по машинному обучению от Тинькофф
- Что надо знать сотруднику Цельса?
- Программа Академии Data Science от Тинькофф
Courses
Other
- Data Scientist total compensation and salaries in the Netherlands
- Programmer Competency Matrix
- Just know stuff (or, how to achieve success in a machine learning PhD)
- A Guide to Data Roles
- Dropbox Engineering Career Framework
- Nurturing a non-linear career
- Path to Senior Engineer Handbook
- Interview Principals
- The difference between good and great engineers
- Understanding and Cracking Data Science Interviews
- Собеседования от karpov.courses
- Data-Science-Interview-Resources
Algorithms and Data Structures
Platforms
- LeetCode
foundations- Leetcode Patterns
foundationsList of questions with patterns + tips - LeetCode Explore
foundations
- Leetcode Patterns
- Codewars
- HackerRank
- CodeAbbey
- CodeRun
lang:rusИнструмент для подготовки к очному собеседованию в Яндексе. Задачи очень похожи на те, что будут на интервью. - Other
Courses
- Algoprog
lang:rus - Яндекс:
- Computer Science Center:
- Подготовься к алгоритмическому собеседованию за 30 недель
- Introduction To Algorithms by MIT
foundations - Algorithms + Data Structures from CS50's Introduction to Computer Science
Resources
- Тренировки по алгоритмам от Яндекса:
- NeetCode. A better way to prepare for coding interviews.
- The Algorithms. Open Source resource for learning Data Structures & Algorithms and their implementation in any Programming Language
- Алгоритмы и структуры данных простыми словами
- Алгоритмика
- Leetcode. Company-wise questions
- Code Abbey Problems
- Unlocking Algorithm Efficiency: A Comprehensive Guide to Time and Space Complexity
- Data Structures Reference
- An Executable Data Structures Cheat Sheet for Interviews
- Coding Interview Guide
- Algorithmic Thinking
- Algorithm Notes
- Coding Interview University
- Tech Interview Cheat Sheet
- Comprehensive Data Structure and Algorithm Study Guide
- Data Structures & Algorithms by Google
- Design and Analysis of Algorithms
- Algorithms for Competitive Programming
Articles
- Как проходят алгоритмические секции на собеседованиях в Яндекс
- How to effectively use LeetCode to prepare for interviews
- How To Answer Algorithm Interview Questions? The REACTO Method
Books
- Grokking Algorithms. An illustrated guide for programmers and other curious people
- Elements of Programming Interviews in Python: The Insiders' Guide
- Cracking the Coding Interview: 189 Programming Questions and Solutions
- Problem Solving with Algorithms and Data Structures using Python by Brad Miller and David Ranum, Luther College
- Competitive Programmer's Handbook by Antti Laaksonen
- Competitive Programming by Steven Halim
Programming
General
Clean Code
- Мартин Р. Чистый код: создание, анализ и рефакторинг / Robert C. Martin. Clean Code: A Handbook of Agile Software Craftsmanship
- Стив Макконнелл. Совершенный код. Мастер-класс / Steve McConnell. Code Complete: A Practical Handbook of Software Construction
Python
Courses
- CS50’s Introduction to Programming with Python
foundations - CS50’s Introduction to Artificial Intelligence with Python
foundations - Основы Python
- Python: основы и применение
- Программирование на Python
- Поколение Python:
Theory
- Python Tutorial for Beginners (with mini-projects)
- What the f*ck Python! Exploring and understanding Python through surprising snippets
- Comprehensive Python Cheatsheet
- Python Koans. An interactive tutorial for learning the Python programming language by making tests pass
- Full Speed Python. Learning Python using a practical approach
- The Hitchhiker’s Guide to Python!
- A collection of design patterns and idioms in Python
- Python Cheatsheet
- Write faster Python code, and ship your code faster
Questions
- 53 Python Interview Questions and Answers
- Python: вопросы на собеседовании:
Other
Practice
- Задачи по Python и машинному обучению
- Project Based Learning
- FastAPI Best Practices
- Python Training by J.P.Morgan
SQL
Courses
- Интерактивный тренажер по SQL
- Пакет SQL курсов:
- Основы SQL
- Продвинутый SQL
- Проектирование баз данных
- PostgreSQL Tutorial for Beginners
- Оконные функции SQL
- SQL Tutorial
- The Ultimate SQL Guide
- CS50’s Introduction to Databases with SQL
Practice
- Онлайн тренажер SQL Academy
- Ace the SQL Interview
- Practice SQL
- SQLBolt. Learn SQL with simple, interactive exercises.
- SQL Tutorial by w3schools
- PostgreSQL Exercises
- The Querynomicon. An Introduction to SQL for Wary Data Scientists
- SQL Challenges
- SQL Squid Game
Machine Learning
Sites
- Machine Learning Mastery by Jason Brownlee
- Машинное обучение для людей. Разбираемся простыми словами
- Анализ малых данных
- Kaggle Competitions
- The Illustrated Machine Learning
- MLU-EXPLAIN
- ML Code Challenges
Courses
- Open Machine Learning Course by Yury Kashnitsky
- Машинное обучение (курс лекций, К.В.Воронцов)
- Прикладные задачи анализа данных (курс лекций, А.Г.Дьяконов) video
- Алгоритмы Машинного обучения с нуля
- Stanford CS229: Machine Learning by Andrew Ng
- Kaggle Learn
- Google Machine Learning Courses
- End to End Machine Learning by Brandon Rohrer
- Машинное Обучение в Python: Большой Курс для Начинающих
- Обучение работе с ML‑сервисами от Yandex Cloud
- Introduction to Machine Learning (I2ML)
- MACHINE LEARNING @ VU
- Practical Machine Learning
- Machine Learning Engineer Learning Path
- Machine Learning Course Notes
Books
- Учебник по машинному обучению от ШАД
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Machine Learning Simplified: A gentle introduction to supervised learning by Andrew Wolf
- The Kaggle Book
- Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
- Clean Machine Learning Code
- Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values
- Interpretable Machine Learning. A Guide for Making Black Box Models Explainable by Christoph Molnar
- Machine Learning Q and AI. Expand Your Machine Learning & AI Knowledge With 30 In-Depth Questions and Answers by Sebastian Raschka
- Reliable Machine Learning: Applying SRE Principles to ML in Production by Cathy Chen
- Models Demystified. A Practical Guide from Linear Regression to Deep Learning
- Machine Learning from Scratch. Derivations in Concept and Code
- Supervised Machine Learning for Science by Christoph Molnar & Timo Freiesleben
- Machine Learning Refined: Notes, Exercises, Presentations, and Sample Chapters
- Дьяконов А.Г. "Машинное обучение и анализ данных"
- Виктор Владимирович Китов. Машинное обучение
Cheetsheets
- Supervised Learning
- Unsupervised Learning
- Tips and Tricks
- Machine learning cheat sheet
- Machine Learning Glossary
Articles
- Anthology of Modern Machine Learning
- Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
- ML Papers of The Week by DAIR.AI
- word2vec Parameter Learning Explained
- Ilya 30u30
- Introduction to Machine Learning by Laurent Younes
- A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets
Applied ML
- geographic Data Science with Python
- WTTE-RNN - Less hacky churn prediction
- Прикладной анализ данных в социальных науках
- Ансамбли в машинном обучении
- Reflecting on 18 years at Google
- Machine Learning for Imbalanced Data
- Валидация моделей машинного обучения
- Do Machine Learning Models Memorize or Generalize?
Blogs
- Applying Machine Learning by Eugene Yan
- Eugene Yan
- Data Science Project Quick-Start
- Writing is Learning: How I Learned an Easier Way to Write
- Uncommon Uses of Python in Commonly Used Libraries
- Andrey Lukyanenko
- A third life of a personal pet-project for handwritten digit recognition
- Dan Bader
- Matthew Brett
- Introducing principal component analysis
- Amit Chaudhary
- Andrej Karpathy
- Jay Alammar
- Lilian Weng
- Data science blogs
- Keep the gradient flowing by Fabian Pedregosa
- Irrational Exuberance by Will Larson
Feature Engineering
- Лекция по курсу ММО - 24.03.2021, Отбор признаков (Feature selection)
- Kaggle Tips for Feature Engineering and Selection | by Gilberto Titericz | Kaggle Days Meetup Madrid
- featurewiz is the best feature selection library for boosting your machine learning performance with minimal effort and maximum relevance using the famous MRMR algorithm
- Feature Ranking and Selection
- Feature Engineering A-Z
- Feature Selection in Machine Learning by Soledad Galli
Tutorials
- CatBoost - An In-Depth Guide
- Введение в библиотеку Transformers и платформу Hugging Face
- Build a Telegram chatbot with any AI model under the hood
- The Illustrated Machine Learning
- ML Primer by Boris Tseytlin
- Decision Trees. The unreasonable power of nested decision rules
- Ensemble Methods and Decision Trees
- Awesome CatBoost
Blog posts
- Векторное представление товаров Prod2Vec: как мы улучшили матчинг и избавились от кучи эмбеддингов
- Some characteristics of best-in-class ML portfolio projects
- Как метод подмены задачи борется с несовершенством данных (и мира)
- Feature Selection — Exhaustive Overview by Danny Butvinik
- A highly anticipated Time Series Cross-validator is finally here
- Интерпретация моделей и диагностика сдвига данных: LIME, SHAP и Shapley Flow
- Мое первое серебро на Kaggle или как стабилизировать ML модель и подпрыгнуть на 700 мест вверх
- Soccer Analytics 2022 Review
- Эй-Яй, крипта, MLOps и командный пет-проджект by yorko
- Understanding UMAP
- A new perspective on Shapley values, part I: Intro to Shapley and SHAP
- A new perspective on Shapley values, part II: The Naïve Shapley method
- 10 первых ошибок в карьере ML-инженера
- Understanding the Bias-Variance Tradeoff by Seema Singh
- The “Bias-Variance Trade-Off” Explained Practically (In Python)
- Модельный риск: как увеличить эффективность работы ML моделей в большой компании
- Эффективные ансамбли
- Reflecting on 18 years at Google
- Как не перестать быть data driven из-за data driften, или Пару слов о дрейфе данных
- В чём польза теоремы Байеса — или как управлять неопределённостью
- AI by Hand with Prof. Tom Yeh for AI Professionals
Other
- StatQuest with Josh Starmer
- A new perspective on Shapley values:
- Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning by Sebastian Raschka
- How to avoid machine learning pitfalls: a guide for academic researchers by Michael A. Lones
- Core Machine Learning Skills
- Discover machine learning, data science & robotics competitions
MLOps
General
- FastAPI for Machine Learning: Live coding an ML web application with the creator of FastAPISebastián Ramírez
- Build your MLOps stack
- MLOps и production подход к ML исследованиям
- MLOps Guide
- MLOps guide by Chip Hyyen
- MLOps Zoomcamp
- MLOps и production подход к ML исследованиям 2.0: Видео + Курс
- THE ULTIMATE DOCKER COMPOSE CHEAT SHEET
- Practitioner's guide to MLOps by Google
Other
Deep Learning
Books
- The Little Book of Deep Learning by François Fleuret
- Deep Learning with Python by François Chollet
- Multimodal Deep Learning
- Dive into Deep Learning I prefer going through this book using Amazon SageMaker
- Understanding Deep Learning by Simon J.D. Prince
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger
- Deep Learning. Foundations and Concepts by Chris Bishop and Hugh Bishop
- Mastering PyTorch by Ashish Ranjan Jha
- The Tensor Cookbook by Thomas Dybdahl Ahle
- Deep Learning and Computational Physics
- Виктор Владимирович Китов. Глубокое обучение
Courses
- Deep Learning Specialization
- MIT 6.S191 Introduction to Deep Learning
- Full Stack Deep Learning - Course 2022
- 11-785 Introduction to Deep Learning from Carnegie Mellon University
- Neuromatch Academy: Deep Learning
- Efficient Deep Learning Systems by Yandex School of Data Analysis
- Short Courses by DeepLearning.AI
- TinyML and Efficient Deep Learning Computing
- Practical Deep Learning
- Practical Deep Learning for Coders part 2: Deep Learning Foundations to Stable Diffusion
- PyTorch for Deep Learning & Machine Learning (video) + Learn PyTorch for Deep Learning: Zero to Mastery book (site)
- Deep Learning Fundamentals by Sebastian Raschka and Lightning AI
- Future of AI is Foundation Models & Self-Supervised Learning
- Artificial Intelligence for Beginners
- 11-785 Introduction to Deep Learning + 11785 Spring 2024 Lectures
- Stanford CS 230 ― Deep Learning
- Convolutional Neural Networks
- Recurrent Neural Networks
- Deep Learning Tips and Tricks
- CS 330: Deep Multi-Task and Meta Learning
- Deep Learning with Catalyst
- Practical DL
- Deep Learning from the Foundations by fast.ai
- PyTorch Tutorials - Complete Beginner Course
- Школа глубокого обучения
- Neural Networks: Zero to Hero by Andrej Karpathy
- CUDA Mode: Lectures + Video
- Deep Learning IP-Paris
- CS565600 Deep Learning. Fundamentals of machine learning, deep learning, and AI
- Introduction to Deep Learning (I2DL) (IN2346)
- CS 886: Recent Advances on Foundation Models
- DEEP LEARNING COURSE
- CS236 Deep Generative Models
- 10-202: Introduction to Modern AI
Tutorials
- Introduction to Deep Learning by Sebastian Raschka
- Коллекция ручных задачек о нейросетях
- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks by Sebastian Raschka
- Deep Learning Tuning Playbook by Google
- A Step by Step Backpropagation Example
- PyTorch Fundamentals by Microsoft
- labml.ai Annotated PyTorch Paper Implementations
- Grokking PyTorch
- The Incredible PyTorch
- AI Fundamentals. Concepts, Definitions, Terms
- A Guide to Production Level Deep Learning
- A Gentle Introduction to torch.autograd
- Introduction to deep learning
- PyTorch internals
Blogs & Blog posts
- WHAT IS TORCH.NN REALLY? by Jeremy Howard, fast.ai
- О «раздутом пузыре» нейросетей
- Cтатьи от команды DeepSchool
- Полезные материалы про PyTorch
- AstraBlog
Other
Generative AI
NLP
Books
- Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra adn Thomas Wolf
- Speech and Language Processing by Dan Jurafsky and James H. Martin
- Transformers for Natural Language Processing by Denis Rothman
Courses
- Нейронные сети и обработка текста
- Stanford CS224N: NLP with Deep Learning + Videos + Notes
- NLP Course | For You by Lena Voita + YSDA Natural Language Processing course
- Hugging Face course
- Natural Language Processing course by Valentin Malykh
- Stanford LSA 311: Computational Lexical Semantics by Dan Jurafsky
- Stanford CS224U: Natural Language Understanding
- Введение в обработку естественного языка
- Stanford CS 224V Conversational Virtual Assistants with Deep Learning
- CS11-711 Advanced Natural Language Processing (at Carnegie Mellon University's Language Technology Institute) + Video + Assignments
- Linguistics for Language Technology
General
- Recommendations for Getting Started with NLP by Elvis
- Чат по NLP
- awesome-nlp. A curated list of resources dedicated to Natural Language Processing
- NLP Cheatsheet: Master NLP
Large Language Models (LLMs) / Transformers
- Stanford Webinar - GPT-3 & Beyond
- Transformer Recipe by Elvis Saravia
- Transformer, explained in detail by Igor Kotenkov
- The Practical Guides for Large Language Models
- State of GPT by Andrej Karpathy
- LLM University by Cohere
- CS324 - Large Language Models
- Generative AI exists because of the transformer. This is how it writes
- Training & Fine-Tuning LLMs for Production
- LLMOps: Building Real-World Applications With Large Language Models
- A Survey of Large Language Models
- Transformers Tutorials
- Ruformers/Руформеры
- Схема энкодера архитектуры Трансформер
- Large Language Model Course
- Insights from Finetuning LLMs with Low-Rank Adaptation by Sebastian Raschka
- LLM Bootcamp - Spring 2023
- ChatGPT Course – Use The OpenAI API to Code 5 Projects
- Self-Attention & Transformers (CS 224n: Natural Language Processing with Deep Learning)
- Build a Large Language Model (From Scratch) by Sebastian Raschka
- Hands-on LLMs Course
- LLaMA-Factory. Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)
- Open LLMs. A list of open LLMs available for commercial use
- Overview of Large Language Models
- Mastering RAG: How To Architect An Enterprise RAG System
- Исчерпывающий гайд по опенсорсным языковым моделям
- Open-Source AI Cookbook
- Building LLM applications for production
- LLM Visualization
- The Illustrated Transformer by Jay Alammar
- Learn to Train and Deploy a Real-Time Financial Advisor
- The Annotated Transformer + Neural networks by 3Blue1Brown
- Elicit Machine Learning Reading List
- LLM Zoomcamp
- llama3 implemented from scratch
- LLM Twin Course: Building Your Production-Ready AI Replica
- Educational resources on LLMs
- A Visual Guide to Quantization. Demystifying the Compression of Large Language Models
- Super Study Guide: Transformers & Large Language Models
- Advanced RAG Techniques: Elevating Your Retrieval-Augmented Generation Systems
- CUDA-Free Inference for LLMs
- Изучаю LLM by Evgenii Nikitin
- What is the Role of Small Models in the LLM Era: A Survey
- Building LLMs from the Ground Up: A 3-hour Coding Workshop
- AI Prompt Engineering: A Deep Dive
- LLM Resources Hub
- 🤗 AI Agents Course by Hugging Face
- LLM Engineering Essentials
- Hugging Face Reasoning Course
- LLM Engineering Essentials course by Nebius Academy
- CS336: Language Modeling from Scratch + Video
Embeddings
- Новый взгляд на оценку русскоязычных моделей: обновлённый бенчмарк ruMTEB и лидерборд
- What are embeddings by Vicki Boykis
- Learn to Love Working with Vector Embeddings by Pinecone
- The 1950-2024 Text Embeddings Evolution Poster
Reading papers with AI
- Explainpaper
- ChatPDGF
- Reimagine Research
- Discover scientific knowledge and stay connected to the world of science
- Get scientific answers by asking millions of research papers
- Otio
Prompt Engineering
- Prompt Engineering Guide + Prompt Engineering Guide
- Prompt injection with Gandalf
- Prompt Engineering
- Prompt Engineering Guide
- Awesome ChatGPT Prompts
- Advanced Prompt Engineering
- Prompt engineering Guide by Open.ai
- Prompt Of The Year: 2023
- Anthropic's Prompt Engineering Interactive Tutorial
- The Prompt Report: A Systematic Survey of Prompting Techniques
- Prompt Engineering Guide by Antrophic
- Claude 101
- AI Coding Playbook
Tutorials
- Train and Fine-Tune Sentence Transformers Models
- Working With Text Data using Sklearn + Text feature extraction using Sklearn
- minbpe. Minimal, clean code for the (byte-level) Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization
Blog posts
- Мультиклассификация экстремально коротких текстов классическими методами машинного обучения
- Рейтинг русскоязычных энкодеров предложений
- Как определять пользовательские намерения, о которых мы узнали 5 минут назад
- Самая большая BERT-подобная модель на русском, которая поместится на ваш компьютер
- ChatGPT как инструмент для поиска: решаем основную проблему
- GPT in 60 Lines of NumPy
- What Is ChatGPT Doing … and Why Does It Work?
- From GPT-3 to ChatGPT: Training Language Models on Instructions and Human Feedback
- Кто такие LLM-агенты и что они умеют?
Articles
- Word2Vec, Mikolov et al., Efficient Estimation of Word Representations in Vector Space
- FastText, Bojanowski et al., Enriching Word Vectors with Subword Information
- Attention, Bahdanau et al., Neural Machine Translation by Jointly Learning to Align and Translate
- Transformers, Vaswani et al., Attention Is All You Need
- BERT, Devlin et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- GPT-2, Radford et al., Language Models are Unsupervised Multitask Learners
- GPT-3, Brown et al, Language Models are Few-Shot Learners
- LaBSE, Feng et al., Language-agnostic BERT Sentence Embedding
- CLIP, Radford et al., Learning Transferable Visual Models From Natural Language Supervision
- RoPE, Su et al., RoFormer: Enhanced Transformer with Rotary Position Embedding
- LoRA, Hu et al., LoRA: Low-Rank Adaptation of Large Language Models
- InstructGPT, Ouyang et al., Training language models to follow instructions with human feedback
- Scaling laws, Hoffmann et al., Training Compute-Optimal Large Language Models
- FlashAttention, Dao et al., FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- NLLB, NLLB team, No Language Left Behind: Scaling Human-Centered Machine Translation
- Q8, Dettmers et al., LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
- Self-instruct, Wang et al., Self-Instruct: Aligning Language Models with Self-Generated Instructions
- Alpaca, Taori et al., Alpaca: A Strong, Replicable Instruction-Following Model
- LLaMA, Touvron, et al., LLaMA: Open and Efficient Foundation Language Models
Packages
- Turbo-Alignment
Turbo-Alignment is a library designed to streamline the fine-tuning and alignment of large language models, leveraging advanced techniques to enhance efficiency and scalability - LitGPT
Every LLM is implemented from scratch with no abstractions and full control, making them blazing fast, minimal, and performant at enterprise scale.
Computer Vision
- Нейронные сети и компьютерное зрение
- CS231n: Deep Learning for Computer Vision + Videos
- EECS 442: Computer Vision + Videos
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola and William T. Freeman
Graphs
Reinforcement Learning
RecSys
Courses
Books
- К. Фальк. Рекомендательные системы на практике / Practical Recommender Systems by Kim Falk
- Personalized Machine Learning
Other
- Авито. Рекомендации
- Recommenders. Best Practices on Recommendation Systems
- Рекомендательные системы
- Рекомендательные системы: идеи, подходы, задачи
Packages
Time Series
- Временные ряды
- Topic 9. Time Series Analysis with Python
- Прогнозирование временных рядов
- Time Series
- Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM, Scikit-learn and CatBoost by Joaquín Amat Rodrigo, Javier Escobar Ortiz
- ARIMA and SARIMAX models with Python by Joaquín Amat Rodrigo, Javier Escobar Ortiz
- Груздев А.В., Рафферти Г. Прогнозирование временных рядов с помощью Prophet, sktime, ETNA и Greykite
- Forecasting: Principles and Practice (3rd ed)
Big Data
Books
- Перрен Ж.Ж. Spark в действии / Spark in Action by Jean-Georges Perrin
- Learning Spark
- Data Analysis with Python and PySpark
Other
- Анализируем данные с помощью фреймворка Spark
- PySpark для аналитика. Как правильно просить ресурсы и как понять, сколько нужно брать
- PySpark для аналитика. Как выгружать данные с помощью toPandas и его альтернатив
- Spark Architecture by FaangTalk
- SPARK для «малышей»
System Design
- The System Design Primer
- Algorithms you should know before you take system design interviews
- Top 14 System Design interview questions for software engineers
- System Design for Interviews and Beyond
- System Design
- System Design by Karan Pratap Singh
- System Design 101
- Systems Design Interview Guide
- Designing Distributed Systems
- System Design Interview – Step By Step Guide
- Designing Data-Intensive Applications and its related books
- A Senior Engineer's Guide to the System Design Interview
- Intro to System Design
- System Design Newsletter
- Awesome System Design
- Design Docs at Google
- System Design Fight Club
- What happens when you type google.com into your browser's address box and press enter?
Machine Learning System Design
- Stanford CS 329S: Machine Learning Systems Design
- ML System Design
- Шаблон ML System Design Doc от телеграм-канала Reliable ML ML System Design - ML System Design Doc. Лекция-бонус от Reliable ML
- Machine learning design primer by Ibragim Badertdinov
- How to Write Design Docs for Machine Learning Systems -> ml-design-doc
- Что я бы хотел знать про ML System Design раньше
- Почему анализ ошибок – это начало разработки ML системы, а не конец?
- ML Systems Design Interview Guide
- Machine Learning System Design by Valerii Babushkin and Arseny Kravchenko
- Designing Machine Learning Systems by Chip Huyen
- Machine Learning Engineering Online Book
- ML system design: 500 case studies to learn from
- Machine Learning System Design Interview by Ali Aminian, Alex Xu + Solutions
- The 9-Step ML System Design Formula
- CSCE 585 Machine Learning Systems: Lectures + Video
- Machine Learning in Production: From Models to Products
- ML System Design by Machine Learning REPA
- ML for Developers
- Machine Learning System Design Doc Examples
- Machine Learning in Production by Carnegie Mellon University
- Crack ML System Design Interviews Like a Pro
Math
General
- В Data Science не нужна математика (Почти)
- Mathematics Of Machine Learning | MIT
- How to beat maths anxiety
- Engineering Math: Differential Equations and Dynamical Systems
- Mathematics for Machine Learning
- The Complete Mathematics of Neural Networks and Deep Learning
- Математика для анализа данных
- Pen and Paper Exercises in Machine Learning by Michael Gutmann
Linear Algebra
- Introduction to Linear Algebra by Gilbert Strang
- The Matrix Calculus You Need For Deep Learning
- Linear Algebra for Data Science
- immersive linear algebra by J. Ström, K. Åström, and T. Akenine-Möller
- Linear Algebra Review and Reference by Zico Kolter
- Linear Algebra for Data Science
- Engineering Math: Vector Calculus and Partial Differential Equations
- Engineering Math: Crash Course in Complex Analysis
Probability and Statistics
- Основы статистики + Основы статистики. Часть 2 + Основы статистики. Часть 3
- Statistics and probability from Khan Academy
- CS109: Probability for Computer Scientists + Course Book + Video
- Математическая статистика и AB-тестирование
- 12 бесплатных материалов по статистике – разберется каждый
- Математическая статистика. Начало
- Z Statistics by Justin Zeltzer
- The Beginner's Guide to Statistical Analysis by Scribbr
- Прикладная статистика. Репозиторий для линейки онлайн-курсов по статистике
- Multivariate statistics
- R2D3 is an experiment in expressing statistical thinking with interactive design
- Statistical Rethinking
- Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks by Will Kurt
- Seeing theory. A visual introduction to probability and statistics
- The Book of Statistical Proofs
- Lessons in Statistical Thinking by Daniel Kaplan
- Прикладная статистика
- Statistics For Applications + Video
- Вся основная теория по A/B-экспериментам
- Statistics 110: Probability
- Introduction to Statistical Learning using Python
- Economertrics Notes by Peter Hull
- The Cartoon Guide to Statistics
- Probability Bootcamp
Causal Inference
- Causal Inference: What If
- Causal Inference and Its Applications in Online Industry
- Applied Causal Inference Powered by ML and AI
A/B Tests
Courses
- A/B TESTING
- Курс по Прикладной статистике от Академии Аналитиков Авито
- A/B Testing & Experimentation Roadmap
General
- Trustworthy Online Controlled Experiments by Ron Kohavi
- Подборка ссылок по A/B тестированию от Валеры Бабушкина
- Most cited sources in A/B Testing by Ron Kohavi
- A/B Testing RoadMap
- Чеклист А/Б эксперимента + Шаблон проведения А/Б эксперимента
- Practitioner’s Guide to Statistical Tests by VK Team
Blog posts
- Шесть причин, почему ваши A/B-тесты не работают
- АБ-тесты — это не только ценный мех… Но еще и процессы
- Стратификация. Как разбиение выборки повышает чувствительность A/B теста
- 50 оттенков линейной регрессии, или почему всё, что вы знаете об A/B тестах, помещается в одно уравнение
Metrics
Other
algorithms-and-data-structures
data-science
deep-learning
machine-learning
python
sql