Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a sub field of computer. It enables computers to do things which are normally done by human beings. This course is a comprehensive understanding of AI concepts and its application using Python and iPython.

The training will include the following;

• What is Artificial Intelligence?

• Intelligence

• Applications of AI

• Problem solving

• AI search algorithms

• Informed (Heuristic) Search Strategies

• Local Search Algorithms

• Learning System

• Common Sense

• Genetic algorithms

• Expert Systems

• Scikit-learn module

What is Artificial Intelligence?

The first idea of artificial intelligence was given by scientist Mr. Alan Turing around the time of the second world war. He suggested building a machine that can mimic the understanding of human intelligence and act like a human.

Artificial Intelligence today is used in all fields of work specifically banking, insurance, manufacturing, retail, logistics and so on. Its application in medical diagnosis, robots, remote sensing, etc. is a high state of the art.

AI as a subject includes the use of computer science, mathematics, statistics and domain expertise.

AI has great advantages and so of them are mentioned below:

• It provides greater precision and accuracy on detection and prediction

• Robots trained on AI can be used to do the works which are difficult for us

• AI has created newer technological breakthroughs in our life

• Fraudulent activities such as credit card transactions have become easier with AI technologies

• AI can be used in time-consuming tasks and it can save a lot of time by becoming more efficient.

You will be able to build the following as a practical project: –

• Classifiers of various types

• Logic Programming based optimizers

• Heuristic Search performed on NP-complete problems

• Natural Language Processing on text data

• Machine Learning in general for several kinds of data

• Logic and reasoning for model evaluation and interpretation

• Rule-based Programming for business use cases

• Decision Making based on AI and ML

• Stochastic methods such as time series and HMM