(All content in this repository belong to their respective authors and none to me. This is simply a personal consolidation.)
A dump of all the data science pdf's that I have accumulated over the years, organized in the following directories:
Also, I have included a list of typical resource recommendations for picking up machine learning in this README. I plan to continually update this repo from time to time. Some of these are the classics, while others are my personal recommendations. The last section on "References" are not learning resources, but references on specific problems that I encountered. If you are here for the resources, please ignore this section. Content is categorized by medium (most hardcore to least hardcore).
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Pattern Recognition and Machine Learning by Christopher Bishop
- Deep Learning by Ian Goodfellow
- Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
- Learning Python by Mark Lutz
- Forecasting: Principles and Practice by Rod Hyndman, George Athanasopoulos
- DataCamp
- Andrew Ng's Machine Learning course on Coursera
- Georgia Tech Online Master of Science in Computer Science
- Fast.ai Practical Deep Learning for Coders Part 1
- Coursera course on Competitive Data Science
- Waterloo Machine Learning Course by Shai Ben-David
- Rules of Machine Learning by Google
- Deeplearningbook.org
- DeepLearning.TV on YouTube
- Getting Started with TensorFlow on TensorFlow.org
- Word2Vec Tutorial - The Skip-Gram Model
- Complete guide to Parameter Tuning in Xgboost on Analytics Vidhya
- Running R on AWS
- Random Forests in R
- Apache Spark installation on Windows 10
- CRAN Task View on Time Series Analysis
- UCI Machine Learning Repository
- DataRobot Artificial Intelligence Wiki
- TensorFlow Playground
- Classifier comparison
- Gradient Boosting Explained
- R packages by Hadley Wickham
- CRAN Packages By Name