End-to-End Machine Learning Library

part of The e2eML Course Catalog

Welcome! Pour yourself a mug of something hot and have a look around.

Here are some recommended course sequences to get you started.

321. One Dimensional Convolutional Neural Networks

Build a classification model for detecting unhealthy rhythms in electrocardiography data.
How one dimensional convolution works
Electrocardiogram case study

314. Neural Network Optimization

Build an autoencoder to extract basis elements of images of the Martian surface.
Optimize compression performance by tuning hyperparameters.
Build and use Evolutionary Powell's method, an experimental hyperparameter optimization algorithm.

313. Advanced Neural Network Methods

Add regularization, dropout, computation graphs and optimizer options to the framework we built in Course 312.
Run it on images from Mars.
How Regularization Works

312. Build a Neural Network Framework

Code up a fully connected deep neural network from scratch in Python.
Extend it into a framework through object-oriented design.

311. Neural Network Visualization

Create a custom neural network visualization in python.
Learn Matplotlib tricks for making professional plots.

221. k-nearest neighbors

Code up the k-nearest neighbors algorithm in Python.
Use k-NN for categorization, regression, and interpolation on several data sets.

213. Polynomial Regression

Code up a robust optimizer from scratch in python.
Fit high-order polynomials to real data on dog breeds.
Implement Monte Carlo cross-validation to select the best model.

212. Time-series Prediction

Build a command line weather prediction tool from a century of data.
Perform data-driven deseasonalization to remove annual weather patterns.
Use autocorrelation to extract predicted temperatures.

211. Decision Trees

Code up a decision tree in python from scratch.
Dynamically construct URL queries for live transit data API.
Build the model into a command line application.

209. Cottonwood examples

A collection of case studies solving problems using the Cottonwood machine learning framework. For video walkthroughs of the code, look here and here.

201. Python intro: Time tools

A Python course for the absolute beginner. Zero prior knowledge assumed. Learn Python basics while building projects like a clock, a timer, and a stopwatch.