Artificial Intelligence and Predictive Analysis


Artificial Intelligence and Predictive Analysis, This course is a comprehensive understanding of AI concepts and its application using Python and iPython.


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
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