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?
- 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