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.

WHO CAN DO?

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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LIVE ONLINE
TRAINING
Live online lectures where you interact with the instructor via webinar.

ONE TO ONE
TRAINING
Individually focused and highly interactive training with you and exclusive trainer for you.

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