Machine Learning Artificial Intelligence and Machine Learning Training Course

Artificial Intelligence and Machine Learning Training Course

Catalog: Machine Learning
Short name: AIMLTC
Course start date: 2024-07-02
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Description

Introduction to Artificial Intelligence

Artificial Intelligence is a branch of science which makes machines to solve the complex problems in a human way. This chapter contains a history of artificial intelligence, detailed explanation of Artificial intelligence with a definition and meaning. It also explains why artificial intelligence is important in today’s world, what is involved in artificial intelligence and the academic disciplines which are related to artificial intelligence.


Intelligent Agents

This section will help you to learn what is intelligent agents, agents, and environment, a concept of rationality, types of agents – Generic agent, Autonomous agent, Reflex agent, Goal-Based Agent, Utility-based agent. The basis of classification of the agents is also explained in detail. The types of environment are also explained with examples.

Course Duration:-12h 27m

Sections

General
0 activities

Introduction to Artificial Intelligence
Definition of Artificial Intelligence
Intelligent Agents
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
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
Course Certificate
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Cost: 5000
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Course Duration:-12h 27m