Machine training
Constantly updated selection of machinery resources.
Table of contents
- Library of the ML specialist + editorial choice:
- Additional materials to the course "Introduction to machine learning"
- Recommendations from teachers of specialization "machine learning and data analysis"
- Literature for admission to Shad
- A selection of scientific-book
- By topics:
- Big Data
- Dataviz
- Latex
- NLP
- Python, iPython, Scikit-LEARN ETC
- R
- Algorithms
- Linear algebra
- Neural networks, Deep Learning
- Statistics and probabilities theory
- Online courses (MOOC)
- Chats/public/channels about ml
- Data analysis calendar
- Machine Learning: Introductory Lecture - K.V. Vorontsov
- Lecture Notes and Code for Machine Learning Practical Course on CMC MSU
- 100+ Free Data Science Books - more than 100 free Data Science books
- Free o'reilly data science eBooks
- 100 repositories for machine learning
- AWESOME-MACHINE-LEARNING-A CURATED LIST of AWESESOME Machine Learning Frameworks, Libraries and Software
- Open Source Society University's Data Science Course - This is a Solid Path for Those of You Want to Complete a Data Sciente ONE OWN TIME, FOR FREE, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, Fore, for, for, for, for, for, for, for far, with couurses from the Best Universities in the World
- Data Science board in Trello - proven materials organized by topics (Expertise Tracks, programming languages, various tools)
- Machine Learning Resource Guide
- 17 resources for machine learning from a typical programmer
- 51 Toy Data Problem in Data Science
- Practical-Pandas-Projects-Project IDEAS For IMPROVING ONE's Python Data Analysis Skills
- Dive Into Machine Learning
- Dive Into Machine Learning Repo on Github
- Data Science Interview Questions - a huge list of questions for preparing for an interview for the Data Scientist position
- Many books on Natural Language Processing
- A list of open sources of data on which you can find free datasets
- What Shoup I Learn in Data Science in 100 Hours?
- Machine-Learning-For-Software-Vengineers-A Complete Daily Plan for Studying to Become A Machine Learning Engineer
- Tutorials on Topics in Machine Learning
- Constantly updated selection of links by Datasens
- Teach Yourself Machine Learning the Hard Way!
- An Article a Week - List of good articles on ml/ai/dl
- The Most Popular Programming Books Ever Mentence on Stackoverflow
- CookieCutter Data Science - A Logical, Reasonably Standardized, But Flexible Project Structure for Doing and Sharing Data Science Work
- AWESOSOME-DATASCIENCE-EDEAS-A List of Awesome and Proven Data Science Use Cases and Applications
- Machine-Learning-Surveys-A Curated List of Machine Learning Surveys, Tutorials and Books
- A Hands-on Data Science Crasse in Python by Bart de Vylder and Pieter Buteneers from Coscale
- Docker-Setup-A Curateed List Docker Images for Data Science Projects, With Easy Setup
- Notes on artificial integence-abstracts on various ml-rehed topics, from algebra to Bayes
Library of the ML specialist
- A course in Machine Learning - Hal Daumé III
- A Probabilistic Theory of Pattern Recognition - Devroye, Gyorfi, Lugosi (PDF)
- Applied Predictive Modeling - M. Kuhn, K. Johnson (2013)
- Bayesian Reasoning and Machine Learning - D.Barber (2015) (PDF)
- Core Concepts in Data Analysis: Summarization, Correlaration and Visualization - Boris Mirkin
- Data Mining and Analysis. Fundamental Concepts and Algorithms - Mjzaki, W.Meira Jr (2014) (PDF)
- Data Mining: Concepts and Techniques - Jiawei Han et. Al.
- Data Science for Dummies - Lillian Pierson (2015)
- Doing Data Science
- Elements of Statistical Learning - Hastie, Tibshirani, Friedman (PDF)
- Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh, And Ameet Talwalkar (2012)
- Frequent Pattern Mining - Charu C AGGARWAL, JIAWEI HAN (EDS.) (PDF)
- Gaussian Processes for Machine Learning - Carl E. Rasmugit Lssen, Christopher Ki Williams (PDF)
- Inductive Logic Programming: Techniques and Applications - Nada Lavrac, Saso Dzeroski
- Information Theory, Inference and Learning Algorithms - David Mackay
- Introduction to Information Retrieval - Manning, Rhagavan, Shutze (PDF)
- Introduction to Machine Learning - Nils J Nilsson (1997)
- Introduction to Machine Learning - Smola and Vishwanathan (PDF)
- Machine Learning Cheat Sheet - Soulmachine (2017) (PDF)
- Machine Learning in Action - Peter Harrington
- Machine Learning, Neural and Statistical Classification - D. Michie, DJ Spiegelhalter
- Machine Learning. The Art of Science of Algorithms that Make Sense of Data - P. Flach (2012)
- Machine Learning - Tom Mitchell
- Machine Learning - Andrew Ng
- Mining Massive Datasets - Jure Leskovec, Anand Rajaraman, Jeff Ullman
- Pattern Recognition and Machine Learning - Cmbishop (2006)
- Probabilistic Programming and Bayesian Methods for Hackers (Free)
- A Programmer's Guide to Data Mining - Ron Zacharski (PDF)
- R in Action
- ReinForcement Learning: An Introduction - Richard S. Sutton, Andrew G. Barto
- The Lion Way Machine Learning Plus Intelligent Optimization (PDF)
- Understanding Machine Learning: from Theory to Algorithms
- Analysis of large data sets - Mining Massive DatASESTS translation
- Mathematical methods of training on precedents (the theory of teaching machines) - K. V. Vorontsov (PDF)
- Machine training - Peter Flah (PDF)
- Methods of ensembleing of students of algorithms - dissertation A. Gushchina (PDF)
Online courses (MOOC)
- List of the best courses on almost any areas of mathematics
- A ton of various programming courses, algorithms, including 29 ML courses
- Couursera:
- CS229: Machine Learning (Andrew Ng, Stanford University) is the most popular machine training course (carefully, instead of standard python or R - Matlab/Octave)
- Specialization machine training and data analysis (Yandex + MIPT/MIPT)
- My repository in this specialization
- Machine Learning Foundations: A Case Study Approach (University of Washington)
- Data Mining Specialization
- Data Science At Scale Specialization (University of Washington)
- Calculus: Single Variable Part 1 (University of Pennsylvania)
- Modern combinatorics (A.M. Raigorodsky, MIPT/MIPT)
- Probability theory for beginners (A.M. Raigorodsky, MIPT/MIPT)
- Linear algebra (HSE/HSE) - a line of linear algebra for non -demeterics faculties, is suitable "for a quick start"
- Economics (HSE/HSE) (Econometrics)
- Business Analytics Specialization (University of Pennsylvania) - specialization on the practical application of statistics and data analysis. For people who were disappointed in DS and do not understand, why is it all
- Social Network Analysis (University of Michigan)
- Social and Economic Networks: Models and Analysis (Stanford University)
- Recommerender Systems Specialization (University of Minnesota)
- Build Intelligent Applications Specialization (University of Washington)
- Programming on Python (MFTI/MIPT)
- Udacy:
- Machine Learning Engineer Nanodegree (Co-Creed by Kaggle)
- Data Analyst Nanodegree (Co-Creed by Facebook & Mongodb)
- Artificial Intelligence Nanodegree (Co-Creed by IBM Watson & Amazon Alexa)
- Predictive Analytics for Business Nanodegree (Co-Creed by Tableau & Alteryx)
- EDX:
- Data Science and Engineering with Spark Xseries (Berkeley)
- 6.002x: Introduction to Computation Thinking and Data Science (MIT)
- 6.041x: Introduction to Probability - The Science of Uncertain (MIT)
- The Analytics Edge (MIT)
- Learning from Data (CALTECH) - Introduction to machine learning (basic theory, algorithms and areas of practical application)
- Video of lectures of the School of Data Analysis (Shad)
- Video classes of the course "Machine Training" (K.V. Vorontsov, Shad)
- Data Mining in Action Course Materials (MIPT/MIPT)
- Open Opendatascience Course on machine learning
- Intro to Python for Data Science - the basics of Python and a little about Numpy
- Fundamentals of statistics - high -quality introduction into statistics, entirely in Russian
- Data Science and Machine Learning Essentials (Microsoft)
- CS231N: Convolutional Neural Networks for Visual Recognition (Stanford University) - an excellent ten -stage course in neuralities and computer vision
- Mining Massive Datasets (Stanford University) - a course based on the book Mining of Massive DatASES to the authors of Jure Leskovec, Anand Rajaraman, and Jeff Ullman (they are also instructors of this course)
- CS109: Data Science (Harvard University)
- Foundations of Machine Learning - A Part of Bloomberg's Machine Learning Edu Initiative
Social
Discussion of machine learning in messengers (groups, channels, chats, communities).
- Open Data Science
- Dedicated to Moscow ML training group on Facebook
- and VKontakte group about machine training training
- Tomsk Machine Training Group
- Slack Tomsk ML Group
- Publics/VKontakte groups:
- Data Science
- Deep Learning
- Data Mining Labs
- Deeplearning (deep neural networks)
- Memes about machine learning for young ladies
- In Telegram:
- Deeplearning community channel
- The first news channel about Data Science
- Chat according to big data, processing and machine learning - Big Data & Machine Learning
- Chat on the topic Data Science - Data Science Chat
- Channel Py_Digest
- Chat RU_PYTHON
- Spark in Me: Internet, Statistics, Data Science, Philosophy
- SPARK in ME channel chat
- Channel with hot posts from Reddit on DS theme
- Sabreddites on machine learning and adjacent topics (I recommend to see at least the top for all the time + Sidebar):
- /R/Analyzit
- /r/bigdata
- /R/BigDatajobs
- /R/Computervision
- /R/Datacleaning
- /R/Datagangsta
- /R/Dataisbeautiful
- /R/Dataisugly
- /R/Datascience
- /R/Datasets
- /R/Dataviz
- /r/jupyternotebooks
- /R/Languagetechnology
- /R/Learnmachinelearning
- /r/learnpython
- /R/Machinelearning
- /R/Opendata
- /r/rstats
- /R/ProbabilityTheory
- /R/Pystats
- /r/samplesize
- /r/semanticweb
- /r/statistics
- /R/TextDatamining
- PEOPLE TWEITING ABOUT ML and AI
- Blogs in Datasens Tematics + List:
- Distill.pub
- Inference.vc
- karpathy.github.io
- Deliprao.com
- Fastml.com
- Timvieira.github.io
- Blogs.princeton.edu
- OffConvex.org
- ruder.io
- argmin.net
- nlpers.blogspot.ru
- BLOG.Shakirm.com
- Blog.paralleldots.com
- alexanderdyakonov.wordpress.com
