Machine Learning Materials

Table of Contents

• General
  • Courses
  • Books
  • Interview Preparation
• Deep Learning
  • Courses
  • Books
• Natural Language Processing
  • Courses
• Computer Vision
  • Courses
• Audio & Speech
  • Courses
• Reinforcement Learning
  • Courses
  • Books
• Data Analysis
  • Courses
  • Books
• Data Engineering
  • Courses
• Other
  • Python
  • Git
  • Docker

General

Courses

1. Elements of AI

• Link: https://course.elementsofai.com/
• Description: an excellent course for beginners with a unique presentation of the material. My personal recommendation!

2. Machine Learning Specialization

• Link: https://www.coursera.org/learn/machine-learning
• Description: a set of courses by Andrew Ng.
• Note: might be unavailable on Coursera because of its policy changes.

3. Открытый курс машинного обучения

• Ссылка: https://ods.ai/tracks/open-ml-course
• Описание: курс от активных членов сообщества Open Data Science.

4. Mathematics for Machine Learning: Linear Algebra

• Link: https://www.coursera.org/learn/linear-algebra-machine-learning

• Description:

  In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally  we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
  
  Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.
  
  At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.
  

• Note: might be unavailable on Coursera because of its policy changes.

5. Mathematics for Machine Learning: Multivariate Calculus

• Link: https://www.coursera.org/learn/multivariate-calculus-machine-learning

• Description:

  This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.
  

• Note: might be unavailable on Coursera because of its policy changes.

6. Amazon Machine Learning University

• Link: https://aws.amazon.com/ru/machine-learning/mlu/

Books

1. An Introduction to Information Retrieval

• Authors: Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze
• Link: https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

2. Pattern Recognition and Machine Learning (Information Science and Statistics)

• Authors: Christopher M. Bishop

3. Machine Learning: A Probabilistic Approach

• Authors: Kevin Murphy

4. The Elements of Statistical Learning: Data Mining, Inference, and Prediction

• Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman
• Link: https://hastie.su.domains/Papers/ESLII.pdf

5. Учебник по машинному обучению

• Авторы: Школа Анализа Данных (Яндекс)
• Ссылка: https://academy.yandex.ru/handbook/ml

Interview Preparation

1. Machine Learning Interviews Book

• Authors: Chip Huyen
• Link: https://huyenchip.com/ml-interviews-book/

2. Machine Learning Systems Design

• Authors: Chip Huyen
• Link: https://huyenchip.com/machine-learning-systems-design/toc.html

3. Материалы для подготовки по машинному обучению

• Авторы: Tinkoff
• Ссылка: https://www.tinkoff.ru/career/it/interview/ml/
• Описание: содержит множество материалов для подготовки к собеседованию на вакансию DS/ML, включая алгоритмы, System Design и прочее. Многие источники пересекаются с тем, что перечислено в этом документе.

Deep Learning

Courses

1. Yann LeCun's Deep Learning Course at CDS

• Link: https://cds.nyu.edu/deep-learning/
• Description: first check who Yann LeCun is, then think about whether this course can be bad.

2. YSDA Deep Learning

• Link: https://github.com/yandexdataschool/Practical_DL
• Description: Deep Learning course co-developed by YSDA, HSE, and Skoltech.

3. Deep Learning School

• Ссылка: https://www.dlschool.org/
• Описание:

  Школа глубокого обучения (Deep Learning School) — это образовательный проект ФПМИ МФТИ. Мы ведём курсы по искусственному интеллекту для школьников и студентов, интересующихся программированием и математикой. Занятия ведут студенты и выпускники Физтех-школы прикладной математики и информатики МФТИ. 
  

4. Deep Learning with Catalyst

• Link: https://github.com/catalyst-team/dl-course
• Description: Deep Learning course made by Deep Learning School, Tinkoff, and Catalyst team.

Books

1. Deep Learning

• Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Link: https://www.deeplearningbook.org/
• Description: this is literally the Bible of Deep Learning.

2. Глубокое обучение. Погружение в мир нейронных сетей

• Авторы: С. Николенко, А. Кадурин, E. Архангельская
• Описание: пожалуй, самая актуальная и хорошо составленная книга о глубоком обучении на русском языке.

3. Dive into Deep Learning

• Authors: mostly Amazon and Google employees
• Link: http://d2l.ai/
• Description:

  Interactive deep learning book with code, math, and discussions.
  

4. Neural Networks and Deep Learning

• Authors: Michael Nielsen
• Link: http://neuralnetworksanddeeplearning.com/
• Description:

  Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.
  

Natural Language Processing

Courses

1. YSDA Natural Language Processing

• Link: https://github.com/yandexdataschool/nlp_course

2. Huawei Natural Language Processing

• Link: https://ods.ai/tracks/nlp-course

3. Нейронные сети и обработка текста

• Ссылка: https://stepik.org/course/54098/promo
• Описание:

  Авторы курса, эксперты Центра ИИ Samsung, доступным языком рассказывают, как начать работать с текстами при помощи нейросетей.
  

Computer Vision

Courses

1. The Ancient Secrets of Computer Vision

• Link: https://pjreddie.com/courses/computer-vision/
• Description:

  This class is a general introduction to computer vision. It covers standard techniques in image processing like filtering, edge detection, stereo, flow, etc. (old-school vision), as well as newer, machine-learning based computer vision. It was originally offered in the spring of 2018 at the University of Washington.
  

2. Нейронные сети и компьютерное зрение

• Ссылка: https://stepik.org/course/50352/promo
• Описание:

  Авторы курса, эксперты Samsung AI Center дадут базовые знания на примере решения задач компьютерного зрения.
  

Audio & Speech

Courses

1. Audio Signal Processing for Machine Learning

• Authors: Valerio Velardo
• Link: https://youtube.com/playlist?list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0
• Description:

  Master key audio signal processing concepts. Learn how to process raw audio data to power your audio-driven AI applications.
  

Reinforcement Learning

Courses

1. YSDA Practical RL

• Link: https://github.com/yandexdataschool/Practical_RL
• Description:

  An open course on reinforcement learning in the wild. Taught on-campus at HSE and YSDA and maintained to be friendly to online students (both english and russian).
  

2. CS234: Reinforcement Learning

• Link: http://web.stanford.edu/class/cs234/index.html

3. Deep RL Bootcamp

• Link: https://sites.google.com/view/deep-rl-bootcamp/lectures

4. Implementation of Reinforcement Learning Algorithms

• Link: https://github.com/dennybritz/reinforcement-learning
• Description:

  Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
  

Books

1. Reinforcement Learning. An Introduction (2nd edition)

• Authors: Richard S. Sutton, Andrew G. Barto
• Link: http://incompleteideas.net/book/RLbook2020.pdf
• Description: this is literally the Bible of Deep Learning.

2. Reinforcement Learning Textbook

• Авторы: Сергей Иванов
• Ссылка: https://arxiv.org/abs/2201.09746
• Описание: конспект (на 245 страниц!) по обучению с подкреплением на русском языке.

Data Analysis

Courses

1. Прикладные задачи анализа данных

• Авторы: Александр Дьяконов (ВМК, МГУ имени М.В. Ломоносова)
• Ссылка: https://github.com/Dyakonov/PZAD

Books

1. Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

• Authors: Ron Kohavi

Data Engineering

Courses

1. Data Engineering Zoomcamp

• Link: https://github.com/DataTalksClub/data-engineering-zoomcamp
• Description: free Data Engineering course from DataTalks.Club.

Other

Python

1. JetBrains Academy

• Link: https://hyperskill.org/
• Description: paid Python, SQL, and even math courses by JetBrains.

2. Курс информатики на Python 3 от МФТИ

• Ссылка: http://judge.mipt.ru/mipt_cs_on_python3/

3. Канал Тимофея Хирьянова

• Ссылка: https://www.youtube.com/c/%D0%A2%D0%B8%D0%BC%D0%BE%D1%84%D0%B5%D0%B9%D0%A5%D0%B8%D1%80%D1%8C%D1%8F%D0%BD%D0%BE%D0%B2

Git

1. Learn Git Branching

• Link: https://learngitbranching.js.org/
• Description: interactive git simulator.

2. Git Screencast

• Ссылка: https://learn.javascript.ru/screencast/git
• Описание: отличный скринкаст по git. Минус - довольно высокая скорость изложения материала, используйте паузу, чтобы успевать за автором.

Docker

1. Docker in Action (2nd edition)

• Authors: Jeff Nickoloff, Stephen Kuenzli