Introduction to Coding

We'll learn coding fundamentals with Python. Along the way, we'll pull data from the web, and create interactive graphs of our data.

Click on the free interactive lessons to begin. And checkout the companion videos to learn to code with a special guest.

0. Introduction
1. Variables
2. Lists
3. Dictionaries
4. Coding Tips
5. Loops
6. Nested Data
7. Make it Easy
8. Loop Over Data
9. Loops to Lists
10. Live Data
11. Functions
12. Arguments
13. Code to Codebase

Want More Practice?

1. Lists Lab
2. Dictionaries Lab
3. Nested Data Lab
4. Live Data Lab
5. For Loops Lab
6. Scraping to Plots Lab

Introduction to Pandas

We'll learn how to select, explore and plot data in Pandas. We'll do so using flood data from the FEMA API, and movie revenue data from 538.

Click on the free interactive lessons to begin.

0. Dataframes
1. Select Columns
2. Select Columns Lab
3. Selecting Rows
4. Selecting Rows Lab
5. Exploring Data
6. Plotting Data
7. Plotting Data Lab

Introduction to Machine Learning

We'll learn the fundamentals of machine learning with decision trees. This includes learning about the common components of all machine learning algorithms, training a model in sklearn, and the test-train split. We'll also learn how to work with the pandas library to collect, explore, and modify our data.

Training Decision Trees

1. Machine Learning Problem
2. The Process
3. Hypothesis Function
4. Training
5. Cost Function
6. Reviewing
7. Sklearn
8. Sklearn Lab
9. Test Train Split
10. Bias Variance Tradeoff

Using Pandas

11. Pandas Dataframes
12. Pandas Columns
13. Pandas Columns Lab
14. Rows and Columns Lab
15. Feature Engineering
16. Engineering to Model Lab

Build a Neural Network

We'll build a neural network from scratch. Along the way, we'll learn the calculus and matrix algebra fundamentals for understanding machine learning algorithms.

A single neuron

0. Introduction
1. The Process
2. Intro to Neurons
3. Plotting a Neuron
4. Sigmoid Neuron
5. Sigmoid Neuron Lab
6. Training a Neuron
7. Gradient Descent
8. Derivatives
9. Derivative Rules
10. Multiparameter Descent
11. Partial Derivatives
12. Applying Gradient Descent
13. Gradient Descent Lab
14. Taking Stock
15. Chain Rule
16. Chain Rule Lab
17. Backpropagation
18. Chain Rule With Neurons
19. Backpropagation Lab

A neural network

20. Neural Networks
21. Dot Products
22. Multiple Neurons
23. Building a Layer
24. Building a Layer Lab
25. Building Layers
26. Output Layers
27. MNIST Layers Lab
28. Hypothesis Function
29. Backprop Layers
30. Training a Network
31. Predicting with MNIST