Artificial Intelligence and Machine Learning Training Course

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  • Specialism Intelligence
  • Certifications Other
  • Location United Kingdom
  • Study type Online
  • Course Type Short (less than 3 months)
  • Duration online self paced LIFETIME ACCESS
  • Price Price on Request

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About the course

Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a subfield of a computer. It enables 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 simple words, Artificial Intelligence means the power of a machine to copy the human intelligent behavior. It is about designing machines that can think.

Why is Artificial Intelligence used?

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

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 & Machine Learning Training Objectives

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

Prerequisites for taking this Machine learning course

The topics included in this topic will be related to probability theorem and linear algebra. So a basic knowledge of statistics and mathematics is an added advantage to take up this Machine learning course.

Target Audience for this Machine Learning course

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.

Artificial Intelligence & Machine Learning Course Description

Section 1: Overview of Artificial Intelligence
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.

Section 2: Representation and Search: State Space Search
Information on State Space Search

This chapter gives a brief introduction to State Space Search in artificial intelligence, its representation, components of search systems and the areas where state space search is used.

Graph theory on state space search

Under this chapter, you will learn what is a graph theory and how it may be used to model problem solving as a search through a graph of problem states. The And/or graph is explained with its uses. The components of the graph theory are also given a brief introduction.

Problem-Solving through state space search

The topics included in this section includes General Problem, Variants, types of problem-solving approach is explained with examples.

DFS algorithm

Depth First Search searches deeper into the problem space. This section also includes the advantages, disadvantages, and algorithm of depth-first search.

DFS with iterative deepening (DFID)

This is a combination of breadth-first search and depth-first search. In this section, you will learn what is iterative deepening search, its properties, and algorithm along with examples.

Backtracking algorithm

Backtracking is an implementation of Artificial Intelligence. This section explains what is backtracking, description of the method when backtracking can be used and for what applications backtracking algorithm can be used. It is explained with few examples and graphs.


Section 3: Representation and Search: Heuristic Search
Heuristic search overview

Heuristic search is a search technique that employs a rule of thumb for its moves. It plays a major role in search strategies. In this chapter, the general meaning and the technical meaning of Heuristic search is explained. It contains more information about the Heuristic search along with the function of the nodes and the goals. The section also contains the following topics which are its type of techniques

Pure Heuristic Search
A* Algorithm
Iterative- Deepening A*
Depth First Branch and Bound
Heuristic Path Algorithm
Recursive Best-First Search

Simple hill climbing

This chapter explains the Simple Hill Climbing technique in Heuristic search, function optimization of hill climbing, problems with simple hill climbing and its example.

The best first search algorithm

This algorithm combines the advantages of breadth-first and depth-first searches. This algorithm finds the most promising path. It is explained with examples.

Admissibility heuristic

This algorithm is used to estimate the cost to reach the goal state. In this chapter, you will learn what is admissibility heuristic, its formulation, construction and examples of admissible heuristic using a puzzle problem.

Min-Max algorithm

This algorithm is used in two-player games such as Chess and others. This section involves a brief introduction to search trees, introduction to the algorithm, explanation of the two players MIN and MAX, optimization, speeding the algorithm, adding alpha beta cut-offs and an example using a game is given for your easy understanding.

Alpha-beta pruning

Alpha-beta pruning is a method to reduce the number of nodes in minimax algorithm in its search tree. This chapter explains the Alpha value of the node, a Beta value of the node, improvements over minimax algorithm, its Pseudo code and a detailed game example.

Section 4: Machine Learning
Machine learning overview

Machine learning is an applied statistics or mathematics. It is a subfield of computer science. This chapter gives a brief 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

In machine learning, a perceptron is an algorithm. This chapter starts with an explanation of what a learning rule is and how to develop the perceptron learning rule. The advantages and disadvantages of the perceptron rule are discussed. The model of perceptron learning is explained using the theory and examples.

The types of neural networks – single layer perceptron network and multilayer neuron network is explained in detail. The perceptron network architecture is explained with few pictures

The steps for constructing learning rules are also given in this chapter.

The linear separable problem is included in this section with examples.

The backpropagation algorithm and learning rule in multilayer perceptron are discussed here. It also explains how to calculate backpropagation algorithm in a step by step procedure.

Updation of weight

The weight matrix of the perception, learning of processing elements with related to weight is included in this chapter.

Clustering algorithms

Clustering methods are organized by modeling approaches like centroid-based and hierarchical. It describes the class of problem and the class of methods. This chapter includes the details of cluster algorithm and its popular algorithms k-Means, k-Medians, Expectation Maximisation and hierarchical clustering with few examples.

Section 5: Logic and Reasoning
Logic reasoning overview

Logic is the study of what follows from what. This section explains the facts about logic in artificial intelligence, why it is useful, the arguments and its logical meanings are explained in detail. Proof theory is used to check the validity of the arguments.

In propositional logic, lexicon and grammar are the syntaxes used and it is explained in detail under this topic along with the symbols used. The theorems, semantics, models, and arguments are also mentioned in this chapter.

First Order Predicate calculus (FOPC)

FOPC includes a wide range of entities. The predicate calculus includes variables and constants. The formula for FOPC is defined and each of its symbols is explained in detail with examples.

Modus ponens and Modus tollens

Modus Ponens and Modus tollens are forms of valid inferences. Modus Ponens involves two premises – conditional statement and the affirmation of the antecedent of the conditional statement. Both the terms are explained with examples.

Unification and deduction process

The unification algorithm, its expressions, and transactions are given in this chapter

Resolution refutation

Resolution rules, its meaning, propositional resolution example, a power of false and other examples are given in brief in this section.


This chapter explains what is Skolemization, how it works, uses of Skolemization and Skolem theories in detail.

Section 6: Rule-Based Programming
Production system

This section contains what is the production system, components of AI production system, four classes of a production system, advantages and disadvantages of a production system. It also contains the following topics

Rules and commands of the production system
Data-driven search
Goal driven search
It’s Differences
CLIPS installation and clips tutorial
The topics included in this section are listed below

What are CLIPS?
What are expert systems?
History of CLIPS
Facts and Rules
Components of CLIPS
Variables and Pattern matching
Defining classes and instances
Wildcard matching
Field constraints
Mathematical operators
Truth and control tutorial


Start date Location / delivery
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