Artificial Intelligence Training Mandaluyong
“Artificial Intelligence Training Mandaluyong” | “Artificial Intelligence Master Program Mandaluyong” | Artificial intelligence (AI) is the simulation of human intelligence through machines & mostly through computer systems. Artificial Intelligence Course is a subfield of the computer. It allows computers to do things which are normally done by human beings. Any program can be said to be Artificial intelligence if it is able to do something that the humans do it using their intelligence through AI Programming Language. AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future. Learn Artificial Intelligence Course Mandaluyong by the industry experts, the program is conducted by Mildaintrainings. Enroll & Get Certified now!
✔ Course Duration : 32 hrs
✔ Training Options : Live Online / Self-Paced / Classroom
✔ Certification Pass : Guaranteed
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Artificial Intelligence Training Mandaluyong
Artificial intelligence (AI) is the simulation of human intelligence through machines & mostly through computer systems. Artificial Intelligence is a sub field of computer. It allows computers to do things which are normally done by human beings. Any program can be said to be Artificial intelligence if it is able to do something that the humans do it using their intelligence. In other words, Artificial Intelligence means the power of a machine to copy the human intelligent behavior. It is all about designing machines that can think Obviously, there is a lot more to it. AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future.
What you will Learn
After completion of AI course you will be able to:
Identify potential areas of applications of AI
Basic ideas & techniques in the design of intelligent computer systems
How to build agents that exhibit reasoning & learning
Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
The topics included in this topic will be related to probability theorem and linear algebra. So a basic knowledge in statistics and mathematics is an added advantage to take up this course. Technical background is a must.
Introduction to Artificial Intelligence
History of artificial intelligence
Detailed explanation of Artificial intelligence with a definition and meaning.
Why artificial intelligence is important in today’s world?
What is involved in artificial intelligence?
The academic disciplines which are related to artificial intelligence.
What is intelligent agents?
Agents and environment
Concept of rationality
Types of agents – Generic agent, Autonomous agent, Reflex agent, Goal Based Agent, Utility based agent
Information on State Space Search
Introduction to State Space Search in artificial intelligence, representation.
Components of search systems.
The areas where state space search is used.
Graph theory on state space search
What is a graph theory?
How may graph theory be used to model problem solving as a search through a graph of problem states?
The And-Or graph is explained with its uses.
Introduction on components of the graph theory.
Problem-Solving through state space search
General Problem, Variants, types of problem-solving approach is explained.
Depth First Search searches deeper into the problem space.
Advantages, disadvantages and algorithm of depth first search.
DFS with iterative deepening (DFID)
What iterative deepening search?
Combination of breadth first search and depth first search.
Its properties & algorithm along with examples.
What is backtracking?
Implementation of Artificial Intelligence.
Description of the methods
When backtracking can be used?
For what applications backtracking algorithm can be used.?
Heuristic search overview
Heuristic search: Rule of thumb
Heuristic search: Search strategies.
function of the nodes and the goals.
The general meaning and the technical meaning of Heuristic search.
Heuristic search: Function of the nodes and the goals.
Heuristic search techniques: Pure Heuristic Search
Simple Hill Climbing technique in Heuristic search.
Function optimization of hill climbing.
Problems with simple hill climbing and its example.
Best-first search algorithm
Combined advantages of breadth first and depth first searches.
What is admissibility?
Heuristic, its formulation, construction
Admissible heuristic using a puzzle problem.
How to estimate the cost to reach the goal state?
Introduction to the Min-Max algorithm.
Explanation of the two players MIN and MAX.
Use of Min-Max Algorithm in two-player games such as Chess and others.
Introduction to search trees.
Speeding the algorithm
Adding alpha beta cut-offs
The Alpha value of the node.
The beta value of the node.
Improvements over minimax algorithm.
Pseudo code and a detailed game example.
Machine learning overview
Introduction about the Machine learning.
History of machine learning,
Types of problems and tasks in machine learning and its algorithms.
Perceptron learning and Neural networks
What is a learning rule?
How to develop the perceptron learning rule?
Advantages and disadvantages of the perceptron rule.
The model of perceptron learning with theory and examples.
The types of neural networks
single layer perceptron network and multi-layer neuron network
The perceptron network architecture
Steps for constructing learning rules
Linear separable problem
Back propagation algorithm and learning rule in multi-layer perceptron
How to calculate back propagation algorithm
Updation of weight
The weight matrix of perceptron.
Learning of processing elements related to weight.
Modeling approaches: Centroid-based.
Modeling approaches: Hierarchical.
Class of problem.
Class of methods.
Cluster algorithm: k-Means
Cluster algorithm: k-Medians
Cluster algorithm: Expectation Maximisation
Cluster algorithm: Hierarchical clustering
Logic reasoning overview
Facts about logics in artificial intelligence.
Why it is useful?
The arguments and its logical meanings.
Theorems, semantics, models and arguments.
First Order Predicate calculus (FOPC)
Predicate calculus: Variables and Constants.
Formula for FOPC
Modus ponens and Modus tollens
Conditional statement and the affirmation of the antecedent of the conditional statement.
Unification and deduction process
Expressions and transactions.
Resolution rules - meaning, propositional and example
Power of false and other examples
what is Skolemization?
How Skolemization works?
Uses of Skolemization
What is production system?
Components of AI production system.
Four classes of production system.
Advantages and disadvantages of production system.
Rules and commands of production system.
Data driven search.
Goal driven search.
CLIPS installation and CLISP Training/Tutorial (ai programing language)
What is CLIPS?
What are expert systems?
History of CLIPS
Facts and Rules
Components of CLIPS
Variables and Pattern matching
Defining classes and instances
Truth and control tutorial
Goal based agent.
Utility based agent.
Maximize expected utility.
Basis of utility theory.
Six axioms of utility theory.
Introduction to decision theory.
Perspectives and disciplines of decision science.
A few different decision theory also explained
Graphical representation of a decision problem.
why reinforcement learning?
How does it work?
What are the motivations?
What technology is used?
Who uses it?
Where can the reinforcement learning be applied?
The limitations of reinforcement learning.
Markov Decision Processes (MDP)
Dynamic Decision Networks (DDN)
DDN is a feature based extension of MDP.
Basics of set theory
Importance of set theory.
What is a set.
Well defined sets.
Cardinality of a set.
Subsets and proper subsets and finally power sets
Joint probability distribution.
Bayesian rule for conditional probability
What is Bayes’ theorem
How to calculate conditional probability using Bayes’ theorem?
You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
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Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.
Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.
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We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy
Yes, the access to the course material will be available for lifetime once you have enrolled into the course.
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Instructor-led SessionsOnline Live Instructor-Led Classes. Classroom Classes at our/your premises.
Real-life Case StudiesLive project based on any of the selected use cases, involving implementation of the various Course concepts.
AssignmentsEach class will be followed by practical assignments.
Lifetime AccessYou get lifetime access to presentations, quizzes, installation guide & class recordings.
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CertificationSucessfully complete your final course project and Mildaintrainings will give you Course completion certificate.