00:00:00 - Introduction
00:01:59 - Linear model and neural net from scratch
00:07:30 - Cleaning the data
00:26:46 - Setting up a linear model
00:38:48 - Creating functions
00:39:39 - Doing a gradient descent step
00:42:15 - Training the linear model
00:46:05 - Measuring accuracy
00:48:10 - Using sigmoid
00:56:09 - Submitting to Kaggle
00:58:25 - Using matrix product
01:03:31 - A neural network
01:09:20 - Deep learning
01:12:10 - Linear model final thoughts
01:15:30 - Why you should use a framework
01:16:33 - Prep the data
01:19:38 - Train the model
01:21:34 - Submit to Kaggle
01:23:22 - Ensembling
01:25:08 - Framework final thoughts
01:26:44 - How random forests really work
01:28:57 - Data preprocessing
01:30:56 - Binary splits
01:41:34 - Final Roundup

Timestamps thanks to RogerS49 on forums.fast.ai.
Transcript thanks to azaidi06, fmussari, wyquek, heylara on forums.fast.ai.