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
Examples using a variety of popular "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
Implementations of the assignments from Google's Udacity course on deep learning.
Assignment 1 - Intro & Data Prep
Assignment 2 - Regression & Neural Nets
Assignment 3 - Regularization
Assignment 4 - Convolutions
Assignment 5 - Word Embeddings
Assignment 6 - Recurrent Nets
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 using Keras to implement deep learning models.
Part 1 - Structured And Time Series Data
Part 2 - Convolutional Networks
Part 3 - Recommender Systems
Part 4 - Recurrent Networks
Part 5 - Anomaly Detection
Part 6 - Generative Adversarial Networks
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
