07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2:
00:00:00 __ 001 Setting up the Notebook and Understanding Delimiters in a Dataset
00:10:22 __ 002 Create a Full Matrix
00:28:34 __ 003 Count the Tokens to Train the Naive Bayes Model
00:44:35 __ 004 Sum the Tokens across the Spam and Ham Subsets
00:52:13 __ 005 Calculate the Token Probabilities and Save the Trained Model
01:00:17 __ 006 Coding Challenge Prepare the Test Data
08 - Test and Evaluate a Naive Bayes Classifier Part 3:
01:05:02 __ 001 Set up the Testing Notebook
01:08:56 __ 002 Joint Conditional Probability (Part 1) Dot Product
01:20:35 __ 003 Joint Conditional Probablity (Part 2) Priors
01:30:15 __ 004 Making Predictions Comparing Joint Probabilities
01:38:33 __ 005 The Accuracy Metric
01:45:27 __ 006 Visualising the Decision Boundary
02:16:21 __ 007 False Positive vs False Negatives
02:27:57 __ 008 The Recall Metric
02:33:41 __ 009 The Precision Metric
02:41:45 __ 010 The F-score or F1 Metric
02:46:15 __ 011 A Naive Bayes Implementation using SciKit Learn
09 - Introduction to Neural Networks and How to Use Pre-Trained Models:
03:15:57 __ 001 The Human Brain and the Inspiration for Artificial Neural Networks
03:24:08 __ 002 Layers Feature Generation and Learning
03:45:19 __ 003 Costs and Disadvantages of Neural Networks
03:59:07 __ 004 Preprocessing Image Data and How RGB Works
04:12:30 __ 005 Importing Keras Models and the Tensorflow Graph
04:21:50 __ 006 Making Predictions using InceptionResNet
04:38:33 __ 007 Coding Challenge Solution Using other Keras Models