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Basic ideas and techniques in the design of intelligent computer systems.

What you will learn

Identify potential areas of applications of AI

Basic ideas and techniques in the design of intelligent computer systems

Statistical and decision-theoretic modeling paradigm

How to build agents that exhibit reasoning and learning

Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.

Description

Artificial Intelligence has been used in wide range of fields these days. For example medical diagnosis, robots, remote sensing, etc. Artificial intelligence is around us in many ways but we don’t realize it. For example, the ATM which we are using is an artificial intelligence machine learning training. Few of the advantages of using artificial intelligence is listed below


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  • Greater precision and accuracy can be achieved through AI
  • These machines do not get affected by the planetary environment or atmosphere
  • Robots can be programmed to do the works which are difficult for the human beings to complete
  • AI will open up doors to new technological breakthroughs
  • As they are machines they don’t stop for sleep or food or rest. They just need some source of energy to work
  • Fraud detection becomes easier with artificial intelligence
  • Using AI the time-consuming tasks can be done more efficiently
  • Dangerous tasks can be done using AI machines as it affects only the machines and not the human beings

Artificial Intelligence has become the centrepiece of strategic decision making for organizations. It is disrupting the way industries function – from sales and marketing to finance and HR, companies are betting on AI to give them a competitive edge. This course is a thoughtfully created course designed specifically for business people and does not require any programming. Through this course you will learn about the current state of AI, how it’s disrupting businesses globally and in diverse fields, how it might impact your current role and what you can do about it. This course also dives into the various building blocks of AI and why it’s necessary for you to have a high-level overview of these topics in today’s data-driven world.

English
language

Content

Artificial Intelligence And Machine Learning Training Course

Introduction to Artificial Intelligence
Definition of Artificial Intelligence
Intelligent Agents
Information on State Space Search
Graph theory on state space search
Solution for State Space Search
FSM
BFS on Graph
DFS algo
DFS with iterative deepening
Backtracking algo
Trace backtracking on graph part_1
Trace backtracking on graph part_2
Summary_state space search
Heuristic search overview
Heuristic calculation technique part _1
Heuristic calculation technique part _2
Simple hill climbing
Best first search algo
Tracing best first search-1
Best first search continue
Admissibility-1
Mini-max
Two ply min max
Alpha beta pruning
Machine learning_overview
Perceptron learning
Perceptron with linearly separable
Backpropagation with multilayer neuron
W for hidden node and backpropagation algo
Backpropagation algorithm explained
Backpropagation calculation_part01
Backpropagation calculation_part02
Updation of weight and cluster
K-Means cluster‚NNalgo and appliaction of machine learning
Logics_reasoning_overview_propositional calculas part 1
Logics_reasoning_overview_propositional calculas part 2
Propotional calculus
Predicate calculus
First order predicate calculus
modus ponus,tollens
Unification and deduction process
Resolution refutation
Resolution refutation in detail
Resolution refutation example-2 convert into clause
Resoultion refutation example-2 apply refutation
Unification substitution andskolemization
Prolog overview_some part of reasoning
Model based and CBR reasoning
Production system
Trace of production system
Knight tour prob in chessboard
Goal driven_data driven production system part _ 1
Goal driven_data driven production system part _ 2
Goal driven Vs data driven and inserting and removing facts
Defining rules and commands
CLIPS installation and clipstutorial 1
CLIPS tutorial 2
CLIPS tutorial 3
CLIPS tutorial 4
CLIPS tutorial 5_part01
CLIPS tutorial 5_part02
Tutorial 6
CLIPS tutorial 7
CLIPS tutorial 8
Variable in pattern tutorial 9
Tutorial 10
More on wildcardmatching_part01
More on wildcardmatching_part02
More on variables
Deffacts and deftemplates_part01
Deffacts and deftemplates_part02
Template indetail part1
Not operator
Forall and exists_part01
Forall and exists_part02
Truth and control
Tutorial 12
Intelligent agent
Simple reflex agent
Simple reflex agent with internal state
Goal based agent
Utility based agent
Basics of utility theory
Maximum expected utility
Decision theory and decision network
Reinforcement learning
MDPand DDN
Basics of set theory part _ 1
Basics of set theory part _ 2
Probability distribution
Baysian rule for conditional probability
Examples of Bayes Theorm