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.
Examples using a variety of popular "data science" Python libraries.
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
Implementations of the assignments from Google's Udacity course on deep learning.
Lab exercises for the original Spark classes on edX.
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 Time Series Forecasting With Prophet