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Home » Courses » **Artificial Intelligence & Machine Learning Training**

This online course on Artificial intelligence is to learn about the intelligence exhibited by machines or software. Through this course you would have an overview of artificial intelligence, understand state space search, Heuristic search, Machine Learning, Logics and reasoning, Rule based Programming, Decision Making and Stochastic methods. All this will Identify and solve complex real world problems using AI approaches.

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Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a sub field of 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 behaviour. It is about designing machines that can think.

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. 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

At the end of this course 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 modelling paradigm
- How to build agents that exhibit reasoning and 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.

The target audience for this course includes students and professionals who are interested in learning robotics and biometrics. This course is also meant for people who are very keen about learning 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 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, 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 are also explained in detail. The types of environment are also explained with examples.

**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 in 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 is 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.

**Heuristic search overview**

Heuristic search is an 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.

**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, Beta value of the node, improvements over minimax algorithm, its Pseudo code and an detailed game example.

**Machine learning overview**

Machine learning is an applied statistics or mathematics. It is a sub field 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, perceptron is an algorithm. This chapter starts with an explanation to what a learning rule is and how to develop the perceptron learning rule. The advantages and disadvantages of the perceptron rule is discussed. The model of perceptron learning is explained using the theory and examples.

**The types of neural networks** – single layer perceptron network and multi layer 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 back propagation algorithm and learning rule in multi layer perceptron is discussed here. It also explains how to calculate back propagation algorithm in a step by step procedure.

**Updation of weight**

The weight matrix of perceptron, learning of processing elements with related to weight are included in this chapter.

**Clustering algorithms**

Clustering methods are organized by modelling 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.

**Logic reasoning overview**

Logic is the study of what follows from what. This section explains the facts about logics 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 syntax 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 symbol 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, power of false and other examples are given in brief in this section.

**Skolemization**

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

**Production system**

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

- Rules and commands of production system
- Data driven search
- Goal driven search
- Its differences
- Examples

**CLIPS installation and clips tutorial**

The topics included in this section are listed below

- What is 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

**Intelligent agent**

This section starts with an brief introduction to intelligent agent. The different types of agents are covered in this topic as mentioned in the list below

- Generic agent
- Autonomous agent
- Reflex agent
- Goal based agent
- Utility based agent

All these types of agents are explained with a pictorial representation and example.

**Utility theory**

This section covers the following topics

- Utility functions
- Maximize expected utility
- Basis of utility theory
- Six axioms of utility theory
- Examples

**Decision theory**

This chapter gives a brief introduction to decision theory, its perspectives and disciplines of decision science. The different decision theory is also explained in detail.

**Decision network**

Decision network is a graphical representation of a decision problem. It is discussed in this chapter in detail with examples.

**Reinforcement learning**

This includes a definition, 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 and the limitations of reinforcement learning.

**Markov Decision Processes (MDP)**

This section includes the objectives, functions, models, dynamic programming, linear programming and examples.

**Dynamic Decision Networks (DDN)**

DDN is a feature based extension of MDP. This section explains its features, representations, components along with examples.

**Basics of set theory**

Here you will learn the importance of set theory, what is a set, set notation, well defined sets, number sets, set equality, cardinality of a set, subsets and proper subsets and finally power sets. It also includes the basic concepts in set theory.

**Probability distribution**

The joint probability distribution is explained in this section with an example and pictorial representation.

**Bayesian rule for conditional probability**

This section explains what is Bayes’ theorem and how to calculate conditional probability using Bayes’ theorem. This is explained with few illustrations of college life, medical diagnosis and witness reliability.

**Why Take This Artificial Intelligence & Machine Learning training?**

Artificial Intelligence is a technology that has become a part of our everyday lives. It has been used in a variety of industries and research fields. In this course you’ll learn the basics and applications of AI, including: machine learning, probabilistic reasoning and few theories.

**What computer skills do I need to have to take an online course ?**

You should know how to use word and how to use internet. Apart from this for this course you should have some basic knowledge in maths and statistics.

**Sebastin**

This Artificial Intelligence & Machine Learning course is absolutely incredible. The content of the course is top-notch. The course is very engaging and interesting. The theories are explained with beautiful examples which made it easy for me to understand the concept. This is definitely one of the best courses.

**Peter**

Artificial Intelligence & Machine Learning training is the best way to start with AI. The concepts are explained in simple language which makes it easy to grasp. None of your time will be wasted while learning this course. It was engaging and felt like learning more. It is highly recommended for beginners as well as for non-beginners. You can learn a lot of information from this course in an simple yet effective way.

Where do our learners come from? |

Professionals from around the world have benefited from eduCBA’s Artificial Intelligence & Machine Learning Training courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many. |

1 | Introduction to Artificial Intelligence |

2 | Definition of Artificial Intelligence |

3 | Intelligent Agents |

4 | Information on State Space Search |

5 | Graph theory on state space search |

6 | Problem Solving through state space search |

7 | Solution for State Space Search |

8 | FSM |

9 | BFS on Graph |

10 | DFS algo |

11 | DFS with iterative deepening |

12 | backtracking algo |

13 | trace backtracking on graph part_1 |

14 | trace backtracking on graph part_2 |

15 | summary_state space search |

16 | Heuristic search overview |

17 | heuristic calculation technique part _1 |

18 | heuristic calculation technique part _2 |

19 | simple hill climbing |

20 | best first search algo |

21 | tracing best first search-1 |

22 | best first search continue |

23 | admissibility-1 |

24 | mini-max |

25 | two ply min max |

26 | alpha beta pruning |

27 | machine learning_overview |

28 | perceptron learning |

29 | perceptron with linearly separable |

30 | backpropagation with multilayer neuron |

31 | W for hidden node and backpropagation algo |

32 | backpropagation algorithm explained |

33 | backpropagation calculation_part01 |

34 | backpropagation calculation_part02 |

35 | updation of weight and cluster |

36 | k-means cluster,NNalgo and appliaction of machine learning |

37 | logics_reasoning_overview_propositional calculas part 1 |

38 | logics_reasoning_overview_propositional calculas part 2 |

39 | propotional calculus |

40 | predicate calculus |

41 | First order predicate calculus |

42 | modus ponus,tollens |

43 | unification and deduction process |

44 | resolution refutation |

45 | resolution refutation in detail |

46 | resolution refutation example-2 convert into clause |

47 | resoultion refutation example-2 apply refutation |

48 | unification substitution andskolemization |

49 | prolog overview_some part of reasoning |

50 | model based and CBR reasoning |

51 | production system |

52 | trace of production system |

53 | knight tour prob in chessboard |

54 | Goal driven_data driven production system part _ 1 |

55 | Goal driven_data driven production system part _ 2 |

56 | goal driven Vs data driven and inserting and removing facts |

57 | defining rules and commands |

58 | CLIPS installation and clipstutorial1 |

59 | CLIPS tutorial 2 |

60 | CLIPS tutorial 3 |

61 | CLIPS tutorial 4 |

62 | CLIPS tutorial 5_part01 |

63 | CLIPS tutorial 5_part02 |

64 | tutorial 6 |

65 | CLIPS tutorial 7 |

66 | CLIPS tutorial 8 |

67 | variable in pattern tutorial 9 |

68 | tutorial 10 |

69 | more on wildcardmatching_part01 |

70 | more on wildcardmatching_part02 |

71 | more on variables |

72 | deffacts and deftemplates_part01 |

73 | deffacts and deftemplates_part02 |

74 | template indetail part1 |

75 | not operator |

76 | forall and exists_part01 |

77 | forall and exists_part02 |

78 | truth and control |

79 | tutorial 12 |

80 | intelligent agent |

81 | simple reflex agent |

82 | simple reflex agent with internal state |

83 | goal based agent |

84 | utility based agent |

85 | basics of utility theory |

86 | maximum expected utility |

87 | decision theory and decision network |

88 | reinforcement learning |

89 | MDPand DDN |

87 | basics of set theory part _1 |

88 | basics of set theory part _2 |

89 | probability distribution |

90 | baysian rule for conditional probability |

91 | examples of bayes theorm |