GitHub projects

Given clinical parameters about a patient, can we predict whether or not they have heart disease?

Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.

This repository contains Jupyter notebooks implementing the algorithms found in the book and summary of the textbook.

Build tool for data science pipelines.

Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

Notebooks and code for the book "Introduction to Machine Learning with Python"

IPython notebooks from Data School's video tutorials on scikit-learn.

Machine Learning for RC Cars.

TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow.

"I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself."

Text and supporting code for Think OS: A Brief Introduction to Operating Systems.

Code for Allen Downey's book Think Complexity.

Code for Data Science at Olin College, Spring 2014.

Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others.

Recipes for using Python's pandas library.

IPython notebooks for a complete course around understanding Nervana's Neon.

IPython notebooks for EEG/MEG data processing using mne-python.

Kanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese Text. Run Cabocha(ISO--8859-1 configured) in Python.

Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model.

Sentiment classifier using word sense disambiguation.

Tweets Sentiment Analyzer

Series of notebooks for learning scikit-learn.

Bayesian Inference Tools in Python.

A recommendation engine library for Python.

SciPy tutorials. This is outdated, check out scipy-lecture-notes.

A collection of image segmentation algorithms based on diffusion methods.

Sarah Palin LDA - Topic Modelling the Sarah Palin emails.

data-science-ipython-notebooks - Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.

A gallery of interesting IPython notebooks

numpic

thinking stats 2

Interactive SVM Explorer, using Dash and scikit-learn

Fundamentals of machine learning in python.

Convolutional autoencoders for 3D image/field compression applied to reduced order Data Assimilation.

Biologically-Inspired and Machine Learning Algorithms in Python. [Deprecated]

Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, k-means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.

A minimal, educational, Pythonic implementation of autograd (~100 loc).

A slightly larger, somewhat feature-complete, PyTorch-inspired, NumPy implementation of a tensor reverse-mode automatic differentiation engine.

Ignite your models into blazing-fast machine learning APIs with a modern framework.

A little logger for machine learning research. Output any object to the terminal, CSV, TensorBoard, text logs on disk, and more with just one call to logger.log().

Debugging and visualization tool for machine learning and data science. It extensively leverages Jupyter Notebook to show real-time visualizations of data in running processes such as machine learning training.

A workflow engine for solving machine learning problems by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation via user-defined (Python) functions.

A framework for creating analytical web applications built on top of Plotly.js, React, and Flask

PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters

Lime is about explaining what machine learning classifiers (or models) are doing. It is able to explain any black box classifier, with two or more classes.

A dashboard library for interactive visualizations using flask socketio and react.

A visualization library for quick and easy generation of common plots in data analysis and machine learning.