About Course
A series of lessons on artificial intelligence and cognitive computing. Collectively, the lessons teach not only concepts and theory, but also how to implement those concepts using python. This series of videos assumes that viewers have no prior knowledge or experience working with artificial intelligence or cognitive computing systems. Topics covered include introductory concepts, Jupyter Notebooks, python fundamentals, Thompson sampling, reinforcement learning, deep neural networks, and convolutional neural networks, among many others.
Course Content
Artificial intelligence (AI) and Cognitive Computing Concepts
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Introduction to Artificial Intelligence (AI) and Cognitive Computing
08:40 -
AI Toolkit – Working with Jupyter Notebooks
17:55 -
Python Fundamentals – Part 01
12:31 -
Python Fundamentals – Part 02
16:01 -
Python Fundamentals Part -3
14:46 -
Foundation of Reinforcement Learning
11:22 -
Reinforcement Learning: Thompson Sampling and the famous Multi-Armed Bandit Problem – Part 1
16:37 -
Reinforcement Learning: Thompson Sampling and the famous Multi-Armed Bandit Problem – Part 2
10:23 -
Profit-maximizing artificial intelligence system in Python
16:49 -
Foundations of Q-learning
16:59 -
Q-learning-based AI system written in Python.
18:08 -
Foundations of Artificial Neural Networks and Deep Q-learning.
17:26 -
Convolutional Neural Networks and Deep Convolutional Q-learning
12:40 -
Greedy Cross Validation to quickly identify optimal machine learning (ML) models
14:51