End-to-End Machine Learning Library

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

If you're wondering where to get started, here are some recommended course sequences. Whether you are new to data science or want to dive into building your own neural networks, there is a set of courses lined up for you.

300 series. Project courses with coding.

September 1, 2020	316. Recurrent Neural Networks
June 1, 2020	315. Convolutional Neural Networks
	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 What is a Computation Graph?
	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.

200 series. Application-centered case studies.

	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. Use Pandas DataFrames to model transit time distributions. Build the model into a command line application.

100 series. Foundational concepts and skills.

Everything below this line is free.

	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 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 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
	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