End-to-End Machine Learning Library

Welcome! Pour yourself a mug of something hot and have a look around. Here are some recommended e2eML course sequences to get you started.
— Brandon

310 series

Build a neural network-powered image compressor from the ground up.

	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.
	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 What is a Computation Graph?
	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.
	311. Neural Network Visualization Create a custom neural network visualization in python. Learn Matplotlib tricks for making professional plots.
In progress Completion June 1, 2020	321. Convolutional Neural Networks How one dimensional convolution works
September 1, 2020	331. Recurrent Neural Networks

200 series

Application-centered case studies.

	211. Decision Trees Code up a decision tree in python from scratch. Dynamically construct URL queries for live transit data API. Use Pandas DataFrames to model transit time distributions. Build the model into a command line application.
	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.
	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.

100 series

Foundational concepts and skills.

	193. How Neural Networks Work How fully connected neural networks work What neural networks can learn How backpropagation works How convolutional neural networks work How recurrent neural networks and LSTM work How deep learning works Getting closer to human intelligence through robotics How convolutional neural networks work, in depth
	191. How Selected Models and Methods Work How decision trees work How Bayesian inference works How convolution works How autocorrelation works How support vector machines work
	173. How Optimization for Machine Learning Works Optimization methods Optimizing a central tendency model Optimizing a linear model Optimizing complex models
	171. How to Choose a Model Choosing between models Separating signal from noise Choosing a loss function Splitting the data Navigating assumptions
	133. How to Navigate Matplotlib Make your first plots Figures and Axes Lines and curves Scatterplots and points Text, axis labels, and annotation Ticks, tick labels, and grids Layout, background, and multiple plots Make your own diagrams Resources
	131. Data Munging Tips and Tricks How to slice and index pandas DataFrames Reading and writing data files Create a personal Python toolbox Make your code run faster Run your code on several cores at once How to use datetime How to turn a picture into numbers How to convert RGB color images to grayscale How to convert images to video and back
	121. Navigating a data science career Data science archetypes Planning your professional path How to navigate a data science interview How to become a data scientist How to get hired as a data scientist Up-level your resume Get people to listen to you on the Internet How to build a data science team How to make a great remote data science team How to choose your tools Oversimplify Imposter syndrome Responding to leaders' misbehavior My career path (Super Data Science podcast)
	101. Data science concepts How data science works Data science for beginners There is more to data science than machine learning What is data How to get good quality data What questions can machine learning answer
	Foundational Skills 091. Mental Focus 071. Git 041. Statistics 032. Linear Algebra 031. Calculus 021. SQL 016. C++ 012. NumPy 011. Python