My Apache Zeppelin and Jupyter notebooks(and more!) for a series of useful data analysis and machine learning related stuff in general
My curated list of data science resourses, including books, papers, softwares, libraries, notebooks, etc. The most of the libraries are for Python though the rest of the materials here are generaly suited for working with data.
- Foundations of Machine Learning
- Python Machine Learning
- Python Data Science Handbook
- Whirlwind Tour Of Python
- Mining Massive Datasets
- Python Machine Learning (2nd edition)
- Deep Learning Book(MIT Press)
- Networks, Crowds, and Markets: Reasoning About a Highly Connected World
- Probability and Statistics Cookbook
- An ML Cheat Sheet
- Hand-book on STATISTICAL DISTRIBUTIONS for experimentalists
- Arxiv.org/ML
- Kaggle
- Reddit MachineLearning Community
- CrowdAI
- Quora
- Github.com
- Apache Projects
- Stanford Machine Learning Course(Have a look at the project section!)
- NIPS Website
- Scipy Lectures
- Nice website about Data Mining
- ML Resources on Github
- A list of researches on a few interesting topics
- Pandas
- Scipy
- Numpy
- Scikit Learn
- Bokeh
- Matplotlib
- Graph tool
- NetworkX
- TensorFlow
- Keras
- NLTK
- Pattern
- BeautifulSoup
- IPython
- Orange
- Theano
- Catboost
- Xgboost
- Mlxtend
- NetworKit
- Eli5
- Pandasql
- Dask
- MLBox
- Gensim
- Imbalanced-Learn
- Patsy
- Statsmodels
- Seaborn
- Pandas-profiling
- Blaze
- Altair
- Numba
- Usefull Metrics
- Xgboost Benchmarks
- Franchise Notebook
- Orange
- Weka
- ELKI
- Julia Programming Language
- SQL Notebook
- IPython
- Incanter
- Torch
- BPython
- RAnalyticFlow
- SPMF
- SageMath
- H2O AI Platform
- Various ML Cheat Sheets
- OpenRefine
- Deep Learning Papers
- Mxnet
- Material for the book 'Python for Data Analysis'
- Encog Machine Learning Framework
- Apache Spark MLib
- Awesome-Python
- GATE
- Microsoft CNTK