cinneesol's repositories
algorithm-trading-webapp
Algorithm Trading web application with Python, Django, PyQt5 and Javascript
awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
Bayesian-Modelling-in-Python
A python tutorial on bayesian modeling techniques (PyMC3)
cookbook-code
Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python
cs231n.github.io
Public facing notes page
Data-Analysis-and-Machine-Learning-Projects
Repository of teaching materials, code, and data for my data analysis and machine learning projects.
data-science-ipython-notebooks
Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
DataSciencePython
common data analysis and machine learning tasks using python
fanova
Functional ANOVA
george
Fast Gaussian Processes for regression
introduction_to_ml_with_python
Notebooks and code for the book "Introduction to Machine Learning with Python"
machine_learning_examples
A collection of machine learning examples and tutorials.
ML_for_Hackers
Code accompanying the book "Machine Learning for Hackers"
opencpu-server
Installation packages (deb, rpm) for OpenCPU cloud server
pylearningcurvepredictor
predicting learning curves in python
pyston
An open-source Python implementation using JIT techniques.
scikit-learn
scikit-learn: machine learning in Python
scipy-2016-sklearn
Scikit-learn tutorial at SciPy2016
thesisdown
An updated R Markdown thesis template using the bookdown package
ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
training-neural-networks-notebook
Notebook to accompany blog post
useR-machine-learning-tutorial
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
yellowbrick
A suite of visual analysis and diagnostic tools to facilitate feature selection, model selection, and parameter tuning for machine learning.