A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
[Download a PDF version] of this flowchart.
Predictive modeling, supervised machine learning, and pattern classification - the big picture [Markdown]
Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]
An Introduction to simple linear supervised classification using
scikit-learn[IPython nb]
Scaling and Normalization
Feature Selection
Dimensionality Reduction
Representing Text
Parametric Techniques
Non-Parametric Techniques
Regression Analysis
Naive Bayes and Text Classification I - Introduction and Theory [PDF]
Out-of-core Learning and Model Persistence using scikit-learn [IPython nb]
Artificial Neurons and Single-Layer Neural Networks - How Machine Learning Algorithms Work Part 1 [IPython nb]
Activation Function Cheatsheet [IPython nb]
Collecting Fantasy Soccer Data with Python and Beautiful Soup [IPython nb]
Download Your Twitter Timeline and Turn into a Word Cloud Using Python [IPython nb]
Reading MNIST into NumPy arrays [IPython nb]
[IPython nb] [PDF]
Supervised Learning
Parametric Techniques
Non-Parametric Techniques
This project is about building a music recommendation system for users who want to listen to happy songs. Such a system can not only be used to brighten up one's mood on a rainy weekend; especially in hospitals, other medical clinics, or public locations such as restaurants, the MusicMood classifier could be used to spread positive mood among people.
Copy-and-paste ready LaTex equations [Markdown]
Open-source datasets [Markdown]
Free Machine Learning eBooks [Markdown]
Terms in data science defined in less than 50 words [Markdown]
Useful libraries for data science in Python [Markdown]
General Tips and Advices [Markdown]
A matrix cheatsheat for Python, R, Julia, and MATLAB [HTML]