Machine-Learning Python Misc Scripts / iPython Notebooks / Codebases

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1. MiniGrad

2. Map/Reduce implementations of common ML algorithms

3. CAEs for Data Assimilation

Convolutional autoencoders for 3D image/field compression applied to reduced order [Data Assimilation](https://en.wikipedia.org/wiki/Data_assimilation).

4. SVM Explorer

Interactive SVM Explorer, using Dash and scikit-learn

5. pattern_classification

6. thinking stats 2

7. hyperopt

8. numpic

9. 2012-paper-diginorm

10. A gallery of interesting IPython notebooks

11. ipython-notebooks

12. 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.

13. decision-weights

14. Sarah Palin LDA

Topic Modeling the Sarah Palin emails.

15. Diffusion Segmentation

A collection of image segmentation algorithms based on diffusion methods.

16. Scipy Tutorials

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 This is outdated, check out scipy-lecture-notes.

17. Crab

A recommendation engine library for Python.

18. BayesPy

Bayesian Inference Tools in Python.

19. scikit-learn tutorials

Series of notebooks for learning scikit-learn.

20. sentiment-analyzer

Tweets Sentiment Analyzer

21. sentiment_classifier

Sentiment classifier using word sense disambiguation.

22. group-lasso

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

23. jProcessing

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.

24. mne-python-notebooks

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

25. Neon Course

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

26. pandas cookbook

Recipes for using Python's pandas library.

27. climin

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

28. Allen Downey’s Data Science Course

Code for Data Science at Olin College, Spring 2014.

29. Allen Downey’s Think Bayes Code

Code repository for Think Bayes.

30. Allen Downey’s Think Complexity Code

Code for Allen Downey's book Think Complexity.

31. Allen Downey’s Think OS Code

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

32. Python Programming for the Humanities

Course for Python programming for the Humanities, assuming no prior knowledge. Heavy focus on text processing / NLP.

33. GreatCircle

Library for calculating great circle distance.

34. Optunity examples

Examples demonstrating how to use Optunity in synergy with machine learning libraries.

35. Dive into Machine Learning with Python Jupyter notebook and scikit-learn

"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."

36. TDB

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

37. Suiron

Machine Learning for RC Cars.

38. Introduction to machine learning with scikit-learn

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

39. Practical XGBoost in Python

comprehensive online course about using XGBoost in Python.

40. Introduction to Machine Learning with Python

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

41. Pydata book

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

42. Homemade Machine Learning

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

43. Prodmodel

Build tool for data science pipelines.

44. the-elements-of-statistical-learning

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

45. Hyperparameter-Optimization-of-Machine-Learning-Algorithms

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

46. Heart_Disease-Prediction

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

47. Flight Fare Prediction

This basically to gauge the understanding of Machine Learning Workflow and Regression technique in specific.

48. Keras Tuner

An easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.