This course covers foundational AI topics and concepts and provides an understanding of essential AI techniques, the basics of neural networks, and fundamental neural network architectural layers. It addresses fundamental conditions, problems, and challenges for AI also from a philosophical perspective.
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Course Curriculum
What is Artificial Intelligence? | 00:00:00 | ||
AI Philosophy | 00:00:00 | ||
AI Goals | 00:00:00 | ||
What Contributes to AI? | 00:00:00 | ||
Programming Without and With AI | 00:00:00 | ||
What is AI Technique? | 00:00:00 | ||
Applications of AI | 00:00:00 | ||
History of AI | 00:00:00 | ||
AI Intelligent Systems | 00:00:00 | ||
What is Intelligence and Types of Intelligence? | 00:00:00 | ||
What is Intelligence Composed of? | 00:00:00 | ||
Difference between Human and Machine Intelligence | 00:00:00 | ||
Speech and Voice Recognition | 00:00:00 | ||
Real Life Applications of AI Research Areas | 00:00:00 | ||
Task Classification of AI | 00:00:00 | ||
What are Agent and Environment? | 00:00:00 | ||
Agent Terminology and Rationality | 00:00:00 | ||
What is Ideal Rational Agent? | 00:00:00 | ||
Nature of Environments and Properties of Environment | 00:00:00 | ||
AI – Popular Search Algorithms | 00:00:00 | ||
AI – Fuzzy Logic Systems | 00:00:00 | ||
AI – Natural Language Processing | 00:00:00 | ||
AI – Machine Learning Concepts | 00:00:00 | ||
AI – Deep Learning Concepts | 00:00:00 | ||
Artificial Intelligence – Expert Systems | 00:00:00 | ||
Artificial Intelligence – Robotics | 00:00:00 | ||
Artificial Intelligence – Neural Networks | 00:00:00 | ||
Tools and Technologies – Describe various technologies used for Machine Learning R, Python, SAS, SPSS, Elastic Search, Spark ML | 00:00:00 | ||
What is data science and Artificial Intelligence and how are they related? | 00:00:00 | ||
What are the steps followed in a data science process? | 00:00:00 | ||
The process flow of a data science process | 00:00:00 | ||
What is Machine Learning? | 00:00:00 | ||
Supervised, unsupervised, semi supervised learning | 00:00:00 | ||
Types of problems: classification, regression etc | 00:00:00 | ||
How does a machine learning algorithm work? | 00:00:00 | ||
How to identify best features to select for the algorithm? | 00:00:00 | ||
Multi collinearity and its drawbacks | 00:00:00 | ||
Machine Learning algorithms | 00:00:00 | ||
Evaluation techniques used for different algorithms | 00:00:00 | ||
What is overfitting and how to address it? | 00:00:00 | ||
What is normalization, why and where is it used? | 00:00:00 | ||
Drawbacks of skewed datasets? | 00:00:00 | ||
Principal Component Analysis, dimensionality reduction and Singular value decomposition | 00:00:00 | ||
Why the sudden hype with AI now? | 00:00:00 | ||
What are Neural networks? How neural networks work? | 00:00:00 | ||
Different types of activation functions used | 00:00:00 | ||
Introduction of deep learning | 00:00:00 | ||
How does deep learning work? | 00:00:00 | ||
Difference between deep learning and machine learning | 00:00:00 | ||
What are hyper parameters and how can it be tuned? | 00:00:00 | ||
What is Natural Language Processing? How it is used? | 00:00:00 | ||
What is tensorflow? | 00:00:00 | ||
What are Convolutional Neural Networks? | 00:00:00 | ||
What are Recurrent Neural Networks? | 00:00:00 | ||
What is Reinforcement Learning? | 00:00:00 | ||
Tools and Technologies – Describe various technologies used for Machine Learning R, Python, SAS, SPSS, Elastic Search, Spark ML | 00:00:00 | ||
Demo on R, Spark ML and Python for sample programs | 00:00:00 | ||
Hands on – Simple hands on R for creation of data set, usage of algorithm | 00:00:00 |
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