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Grokking Artificial Intelligence Algorithms video edition

Video Introducing this tutorial
01-Preface - Our obsession with technology and automation
02-Preface - Ethics, legal matters, and our responsibility
03-Chapter 1 Intuition of artificial intelligence
04-Chapter 1 A brief history of artificial intelligence
05-Chapter 1 Super intelligence - The great unknown
06-Chapter 1 Banking - Fraud detection
07-Chapter 2 Search fundamentals
08-Chapter 2 Representing state - Creating a framework to represent problem spaces and solutions
09-Chapter 2 Breadth-first search - Looking wide before looking deep
10-Chapter 2 Depth-first search - Looking deep before looking wide
11-Chapter 3 Intelligent search
12-Chapter 3 A_ search
13-Chapter 3 Use cases for informed search algorithms
14-Chapter 3 Exercise - What values would propagate in the following Min-max tree
15-Chapter 3 Alpha-beta pruning - Optimize by exploring the sensible paths only
16-Chapter 4 Evolutionary algorithms
17-Chapter 4 Problems applicable to evolutionary algorithms
18-Chapter 4 Encoding the solution spaces
19-Chapter 4 Selecting parents based on their fitness
20-Chapter 4 Two-point crossover - Inheriting more parts from each parent
21-Chapter 4 Configuring the parameters of a genetic algorithm
22-Chapter 5 Advanced evolutionary approaches
23-Chapter 5 Arithmetic crossover - Reproduce with math
24-Chapter 5 Change node mutation - Changing the value of a node
25-Chapter 6 Swarm intelligence - Ants
26-Chapter 6 Representing state - What do paths and ants look like
27-Chapter 6 Set up the population of ants
28-Chapter 6 Updating pheromones based on ant tours
29-Chapter 7 Swarm intelligence - Particles
30-Chapter 7 Problems applicable to particle swarm optimization
31-Chapter 7 Calculate the fitness of each particle
32-Chapter 7 Position update
33-Chapter 8 Machine learning
34-Chapter 8 Collecting and understanding data - Know your context
35-Chapter 8 Ambiguous values
36-Chapter 8 Finding the mean of the features
37-Chapter 8 Testing the model - Determine the accuracy of the model
38-Chapter 8 Classification with decision trees
39-Chapter 8 Decision-tree learning life cycle
40-Chapter 8 Classifying examples with decision trees
41-Chapter 9 Artificial neural networks
42-Chapter 9 Exercise - Calculate the output of the following input for the Perceptron
43-Chapter 9 Forward propagation - Using a trained ANN
44-Chapter 9 Backpropagation - Training an ANN
45-Chapter 9 Options for activation functions
46-Chapter 9 Bias
47-Chapter 10 Reinforcement learning with Q-learning
48-Chapter 10 Problems applicable to reinforcement learning
49-Chapter 10 Training with the simulation using Q-learning
50-Chapter 10 Exercise - Calculate the change in values for the Q-table
51-Chapter 10 Deep learning approaches to reinforcement learning