#### 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.

### End-to-end machine learning projects

- 322. Two dimensional convolutional neural networks
- 321. One dimensional convolutional neural networks
- 314. Neural network optimization
- 313. Advanced neural network methods
- 312. Build a neural network framework
- 311. Neural network visualization

### Machine learning case studies

- 221. k-nearest neighbors
- 213. Polynomial regression
- 212. Time-series analysis
- 211. Decision trees
- 209. Cottonwood examples
- 201. Python intro: Time tools

### Tutorials

### Tools

- 137. Signal processing
- 135. Python concepts and cookbooks II: Advanced topics
- 134. Python concepts and cookbooks I: Foundations
- 133. Matplotlib
- 131. Data munging in Python

### Career development

### 322. Two Dimensional Convolutional Neural Networks

- Build image classification models for benchmark data sets.
- Two dimensional convolution
- Softmax
- Batch normalization
- MNIST digits case study
- CIFAR-10 images case study

### 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.

### 193. How Neural Networks Work

- How fully connected neural networks work
- How convolutional neural networks work
- How convolutional neural networks work, in depth
- How recurrent neural networks and LSTM work
- How backpropagation works
- What neural networks can learn
- Approaching human intelligence through robotics

### 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

### 137. Signal Processing Techniques

- How to normalize a signal by its minimum and maximum
- How to normalize a signal by mean and variance
- Rate of change
- Exponential smoothing
- How to turn a picture into numbers
- How to convert RGB color images to grayscale
- One dimensional convolution
- Two dimensional convolution

### 135. Python concepts and cookbooks II: Advanced topics

- Threading
- Multiprocessing for Parallelization
- Multiprocessing for Real-Time Applications
- Make your code run faster

### 134. Python concepts and cookbooks I: Fundamentals

### 133. How to Navigate Matplotlib

- Quick start guide
- Three important ideas
- Lines and curves
- Scatterplots and points
- Colors and colormaps
- Text, axis labels, and annotation
- Patches
- Ticks, tick labels, and grids
- Layout, background, and multiple plots

### 131. Data Munging Tips and Tricks

- How to slice and index pandas DataFrames
- Reading and writing data files
- Play and record sounds
- Make your code run faster
- How to use datetime
- Turn images to videos and back
- Make your own personal Python toolbox
- Use Multiprocessing

### 121. Navigating a data science career

- Data science archetypes
- How to choose a project
- How to solve a hard problem
- How to choose your tools
- Oversimplify your communication
- Decide and commit
- Choose your professional path
- How to navigate a data science interview
- Up-level your resume
- How to get hired as a data scientist
- Get to know your new company
- Imposter syndrome
- Build a strong data science team
- Build a strong distributed data science team
- What to do when a leader does something wrong

### 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