This repo contains various IPython notebooks I've created to experiment with libraries and work through exercises, and explore subjects that I find interesting. I've included notebook viewer links below. Click the link to see a live rendering of the notebook.
These notebooks contain introductory content such as an overview of the language and a review of IPython's functionality.
Introduction To Python
IPython Magic Commands
Demonstrations and use cases for many of the most widely used "data science" Python libraries.
NumPy
SciPy
Matplotlib
Pandas
Statsmodels
Scikit-learn
Seaborn
NetworkX
PyMC
NLTK
DEAP
Gensim
Implementations of the exercises presented in Andrew Ng's "Machine Learning" class on Coursera.
Exercise 1 - Linear Regression
Exercise 2 - Logistic Regression
Exercise 3 - Multi-Class Classification
Exercise 4 - Neural Networks
Exercise 6 - Support Vector Machines
Exercise 7 - K-Means Clustering & PCA
Exercise 8 - Anomaly Detection & Recommendation Systems
Lab exercises for the Spark class on edX. I think it's okay to post these now that the class has ended. If you're running through the class content, you're probably better off solving these on your own first.
Lab 0 - Learning Apache Spark
Lab 1 - Building A Word Count Application
Lab 2 - Web Server Log Analysis
Lab 3 - Text Analysis & Entity Resolution
Lab 4 - Introduction To Machine Learning
ML Lab 3 - Linear Regression
ML Lab 4 - Click-Through Rate Prediction
ML Lab 5 - Principal Component Analysis
Notebooks covering various interesting topics!
Comparison Of Various Code Optimization Methods
A Simple Time Series Analysis of the S&P 500 Index
An Intro To Probablistic Programming
Language Exploration Using Vector Space Models
Solving Problems With Dynamic Programming