Here is a portal fo Daily Data Sciences Notes
- Tech At Bloomberg， https://www.techatbloomberg.com
- Guido Van Rossum – Python, C++ (R would have enjoyed monopoly had he not created Python)
- Tianqi Chen – Python, C++ (Creator of XGBoost Package)
- Sebastian Raschka – Python (Data Scientist & Author of Python Machine Learning)
- Mike Bostock – D3, Javascript (Creator of D3.js)
- Hadley Wickham – R (Chief Scientist at RStudio)
- Andreas Muller – Python (Machine Learning Scientist, Core developer of Scikit Learn, Author)
- Oliver Grisel – Python, Java (Contributor to Scikit Learn)
- Randy Olson – Python, HTML (Senior Data Scientist at University of Pennsylvania)
- Wes Mckinney – Python, C++ (Author of Python for Data Analysis)
- Jake Vanderplas – Python (Data Scientist, Mentor, Professor)
- Ian Goodfellow – Python (Research Scientist at Open AI, Author of Deep Learning Book)
- Andrej Karparthy – Python, Lua (Research Scientist at OpenAI)
- John Myles White – R, Julia (Author, Research Scientist at Facebook)
- Soumith Chintala – Lua, Python (Facebook AI Research)
- Allen Downey – Python (Author of ThinkStats, Professor)
- Yihuai Xie – R (Software Engineer at RStudio)
- Denny Britz – Python, Javascript (Google Brain, High School Dropout)
- Cameron Davidson – Python (Author, Product Analyst at Shopify)
- Skipper Seabold – Python (Data Scientist at Civic Analytics)
- David Robinson – R (Data Scientist at Stack Overflow)
- Jennifer Bryan – R (Professor at University of Columbia)
- Coursera
- EdX
- Datacamp
- Udemy
- Udacity
- Khan Academy
- Kaggle
- R-bloggers
- Analytics Vidya
- KDNuggets
- Probability & Statistics
- Linear Algebra
- Python
- R
- SQL/Presto
- Tableau/PowerBI
- AWS/Azure
- Spark
- Excel
- DevOps
- Linear Regression
- Logistics Regression
- K-means Clustering
- PCA
- Support Vector Machine
- Decision Tree
- Random Forrest
- Gradient Boosting Machine
- XGboost
- Artificial Neural Networks
- Technology
- Finance
- Retail
- Telecom
- Healthcare & Pharma
- Manufacturing
- Automotive
- Cybersecurity
- Energy
- Utilities
- Free Data Science Books – This repository comprises of downloadable books on subjects like statistics, machine learning, data mining etc. If you like reading books, and prefer to gain knowledge from books than any other method, you have a lot to take home from this repository.
- Exercises from ML for Hackers – This book is written by John Myles White. If you have read this book, wonderful! In case you haven’t. nothing to worry. These exercises are simple and effective enough to make you understand the implementation of a particular method. It’s good for people who learn better by doing than reading.
- Exercises from Probabilistic Programming – This book is written by Cam Davidson Pilon. This repository consists of exercises from described in his book Probabilistic Programming and Bayesian Method for Hackers. If you understand probability in depths, you must do these exercises and see how is it being used by machine learning.
- Machine Learning Books – This repository has 10 books on machine learning available for download.
- ML in Python – This repository consists of coding exercises from the book Introduction to Machine Learning in Python written by Andreas C Mueller and Sarah Guido. This is good for people who want to start with ML in python as the coding exercises are quite easy.
- Python Projects – Keen to do interesting python projects but don’t know where to start ? Check out some interesting projects done in python, understand them and may be they could inspire you to start one on your own. In other words, these projects are nothing but recipes taken from the IPython Cookbook written by Dr. Cyrille Rossant.
-
TensorFlow Examples – TensorFlow ( library made for numerical computation) has rapidly gained popularity among machine learning practitioners in Python. This repository will help you get started with tensorflow and its features. This repository is best suited for beginners keen to learn tensorflow and looking for practical examples with concise explanation.
-
useR 2016 Machine Learning – This repository consists of machine learning tutorials delivered at The R User Conference 2016. Mainly, it explains 6 popular supervised machine learning methods in R. Along with, several best practices which one should follow while model building.
-
Machine Learning University Courses – This repository enlists all the ML programs undertaken at top universities around the world. Some of these universities also share course content online, which will also find here. It consists of the top courses undertaken at various universities. This repository should help you understand their course curriculum and depth of topics covered.
-
Notebooks on Statistics & ML – This notebook demonstrates statistical concepts in python. The notebook shared above are focused on only machine learning methods. But, this repository contains notebooks which shows how statistical analysis can be done in python. For best results, you must have prior knowledge of statistics and related concepts.
- Awesome Machine Learning in Python – As evident by its name, this repository enlists all the useful tutorials on doing machine learning, computer vision, natural language processing (NLP) in python. Considering the rapidly increasing usage of python in data science, it’s a good resource if you too are trying to enhance your python skills .
- IPython Notebooks – What could be better than learning by doing? Yes, this repository contains ipython notebooks on ML algorithms (scikit learn) by solving various problems including titanic kaggle. Moreover, it also contains tensor flow notebooks to build scalable ML models in python. The focus of this repository is kept on exploring broad aspect of python in machine learning.
- Tutorials in Notebooks – More notebooks for you to practice and amplify your breadth of knowledge in machine learning.
- Interesting ipython notebooks – Even more notebooks.
- Data Science in Python – This repository consists of ML algorithms wise (neural network, decision trees, linear regression etc) list of tutorials to give you a clear view of how an algorithm works. Also, it introduces you to most common tasks in data manipulations and how to do them in python.
- Machine Learning Packages – This repository comprises of an exhaustive list R packages for machine learning. Many a times, we find ourselves stuck at caret or e1071 packages. But, turns out there are many other ML packages which are equally powerful and can reduce our modeling time.
- Awesome R – Here you’ll find all the useful resources to learn R in a comprehensive manner. Not just predictive modeling, this repository contains tutorials on building web apps, visualization, programming, database management etc. R is a multi-purpose language. Most of us confine ourselves to predictive modeling, using this repository you can explore its various sides.
- Data Science in R – This repository takes you deeper into specifics of model building in R. It comprises several hot questions on topics like data exploration, data manipulation, time series analysis etc. Along side, you’ll also find additional tutorials missed in the above two repositories.
- Practice H2o – If H2o has helped you in reducing computational time, you might be interested in mastering this powerful package. This repository contains practical examples (airlines delay, bad loans, Citibike demand) using which you can explore various h2o features in model building.