Learning Resources

Table of Contents:

• Learning Resources
  • Interview preparation and knowledge checking
  • Machine Learning
  • Deep Learning
  • Python
  • Math
  • Statistics
  • SQL
  • NLP
  • Computer Vision
  • RecSys
  • Algorithms and Data Structures
  • Metrics
  • Best Kaggle Notebook & Competitions
  • Not popular, but cool python libraries
  • Assignments for ML & DL
  • Cheatsheets
  • Datasets
  • Other
  • Other lists of materials

Interview preparation and knowledge checking

• Machine Learning FAQ by Sebastian Raschka Questions
• Machine Learning FAQ by X Questions
• OVER 100 Data Scientist Interview Questions and Answers by Terence Shin Questions
• Материалы для подготовки по машинному обучению от Тинькофф Rus Questions
• DS & Python questions by Artem Ryblov Questions
• Introduction to Machine Learning Interviews Book by Chip Huyen
• Awesome interview questions repository
• Data Science Interviews by Alexey Grigoriev Questions
• Тестовые задания по DS Rus
• Minimum Viable Study Plan for Machine Learning Interviews Repository

Machine Learning

• Open Machine Learning Course by Yury Kashnitsky Free course + Paid additional assignments
  Открытый курс машинного обучения Rus Free course + Paid additional assignments
• StatQuest with Josh Starmer Videos
• Machine Learning Mastery by Jason Brownlee Free guides + Paid eBooks
• End to End Machine Learning by Brandon Rohrer Course
• Учебник по машинному обучению от ШАД Яндекс Rus Book
• Stanford CS 329S: Machine Learning Systems Design Course
• Designing Machine Learning Systems by Chip Huyen Book
• Machine Learning Simplified: A gentle introduction to supervised learning by Andrew Wolf Free Book
• Машинное обучение (курс лекций, К.В.Воронцов) Rus Course
• Stanford CS229: Machine Learning by Andrew Ng Course
• Прикладные задачи анализа данных, Александр Дьяконов + Video Rus Course

Deep Learning

• Dive into Deep Learning
  I prefer going through this book using Amazon SageMaker
• Deep Learning Specialization Paid Course
• Deep Learning with Python by François Chollet Paid Book
• MIT 6.S191 Introduction to Deep Learning Course

Python

• Python: основы и применение Rus Course
• Программирование на Python Rus Course

Math

• The Matrix Calculus You Need For Deep Learning

Statistics

• Основы статистики + Основы статистики. Часть 2 + Основы статистики. Часть 3 Rus Course
• Statistics and probability from Khan Academy Course
• CS109: Probability for Computer Scientists + Course Book Course
• Математическая статистика и AB-тестирование Repository

SQL

• Основы SQL + Продвинутый SQL + Проектирование баз данных Rus Paid Courses

• Интерактивный тренажер по SQL Rus Course

• Онлайн тренажер SQL Academy Rus Course

NLP

• Нейронные сети и обработка текста Rus Course
• Stanford CS224N: NLP with Deep Learning + Videos Course
• NLP Course | For You by Lena Voita + YSDA Natural Language Processing course Rus videos + Eng guides Course
• Hugging Face course Course
• Working With Text Data using Sklearn + Text feature extraction using Sklearn Guide
• Recommendations for Getting Started with NLP by Elvis Paid Recommendations
• Natural Language Processing course by Valentin Malykh Course
• Speech and Language Processing by Dan Jurafsky and James H. Martin Book
• Stanford LSA 311: Computational Lexical Semantics by Dan Jurafsky Course
• Stanford CS224U: Natural Language Understanding Course
• Введение в обработку естественного языка Rus Course
• Stanford CS 224V Conversational Virtual Assistants with Deep Learning Course
• Learn to Love Working with Vector Embeddings by Pinecone Course

Computer Vision

• Нейронные сети и компьютерное зрение Rus Course
• CS231n: Convolutional Neural Networks for Visual Recognition + Videos Course

RecSys

• Your first recsys by MTS Rus Course
• Your Second RecSys by MTS Rus Course

Algorithms and Data Structures

• LeetCode Explore Free + Paid Course
• Algoprog Rus Free + Paid Course
• Алгоритмы: теория и практика. Методы Free Rus Course
• Алгоритмы: теория и практика. Структуры данных Free Rus Course

Metrics

• Classification metrics (precision, recall, F1 and Matthews correlation coefficient) Twitter thread
• Classification metrics (precision, recall, F1 and Matthews correlation coefficient) vs Balanced Accuracy Twitter thread

Best Kaggle Notebook & Competitions

• Best Kaggle Competitions for Beginners Competitions
• Roadmap for Beginners Notebook
• The Best Tutorial for Beginners Notebook
• Learning Materials on Kaggle Notebook
• NLP:
  • Approaching (Almost) Any NLP Problem on Kaggle Notebook
• EDA Pipelines:
  • Titanic:
    • EDA To Prediction(DieTanic) Notebook
    • Titanic Top 4% with ensemble modeling Notebook
• Ensembling:
  • Introduction to Ensembling/Stacking in Python Notebook

Not popular, but cool python libraries

• Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks Package

Assignments for ML & DL

• Stanford CS224n: Natural Language Processing with Deep Learning Assignments
  • Assignment 1: Introduction to word vectors
  • Assignment 2: Derivatives and implementation of word2vec algorithm
  • Assignment 3: Dependency parsing and neural network foundations
  • Assignment 4: Neural Machine Translation with sequence-to-sequence, attention, and subwords
  • Assignment 5: Self-supervised learning and fine-tuning with Transformers
• Stanford CS231n: Convolutional Neural Networks for Visual Recognition & Course notes Assignments
  • Assignment 1: Image Classification, kNN, SVM, Softmax, Fully-Connected Neural Network
  • Assignment 2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets
  • Assignment 3: Image Captioning with Vanilla RNNs, LSTMs, Transformers, Network Visualization, Generative Adversarial Networks
• Stanford CS229: Machine Learning & Summer version & Assignments from Fall 2018 Assignments

Cheatsheets

• Stanford CS 229 ― Machine Learning Cheatsheet
  • Supervised Learning
  • Unsupervised Learning
  • Deep Learning
  • Machine Learning tips and tricks
• Stanford CS 230 ― Deep Learning Cheatsheet
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Deep Learning Tips and Tricks

Datasets

• 24 Useful Open Datasets for Natural Language Processing Datasets

Other

• Courses at Stanford relative to AI

Other lists of materials

• NLP Pandect Repository
• Start Career in DS: навигация по постам
• Extra materials for ml-mipt course Repository