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.
Machine Learning Exercises
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
Tensorflow Deep Learning Exercises
Implementations of the assignments from Google's Udacity course on deep learning.
Spark Big Data Labs
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 from Jeremy Howard's fast.ai class.
Lesson 1 - Image Classification
Lesson 2 - Multi-label Classification
Lesson 3 - Structured And Time Series Data
Lesson 4 - Sentiment Classification
Lesson 5 - Recommendation Using Deep Learning
Lesson 6 - Language Modeling With RNNs
Lesson 7 - Convolutional Networks In Detail
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
Markov Chains From Scratch
A Sampling Of Monte Carlo Methods