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Certificate in Fundamental of Artificial Intelligence

5( 4322 REVIEWS )
4322 STUDENTS

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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This course was designed for representatives from the Financial Services and Insurance sector. Representatives from the below departments would benefit most Retail Banking, Marketing and IT

 

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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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PRIVATE COURSE
  • Number of Units58
  • 6 Months

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4322 STUDENTS ENROLLED

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