07-09

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
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