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Become Expert In Artificial Intelligence And Machine Learning

Learn the Basic ideas and techniques in the design of intelligent computer systems, & How to build agents that exhibit reasoning and learning.

Price : $10.00 $99.00
Discount: $89.00
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12

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At the end of this Machine learning training, you will be able to, 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

The target audience for this course includes students and professionals who are interested in learning robotics and biometrics. This Machine learning training is also meant for people who are very keen on learning Artificial Intelligence.


 

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  • Overview of Artificial Intelligence
    • Introduction to Artificial Intelligence
    • Definition of Artificial Intelligence
    • Intelligent Agents
  • Representation and Search State Space Search
    • Information on State Space Search
    • Graph theory on state space search
    • Problem Solving through 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
  • Representation and search Heuristic 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
    • 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 and reasoning
    • 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
  • Rule based Programming
    • 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
  • Decision Making
    • 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
  • Stochastic methods
    • basics of set theory part _ 1
    • basics of set theory part _ 2
    • probability distribution
    • baysian rule for conditional probability
    • examples of bayes theorm