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.
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
At the end of this Machine learning training, you will be able to
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.
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 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.
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.
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.
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.
The topics included in this section includes General Problem, Variants, types of problem-solving approach is explained with examples.
Depth First Search searches deeper into the problem space. This section also includes the advantages, disadvantages, and algorithm of depth-first search.
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 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 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
This chapter explains the Simple Hill Climbing technique in Heuristic search, function optimization of hill climbing, problems with simple hill climbing and its example.
This algorithm combines the advantages of breadth-first and depth-first searches. This algorithm finds the most promising path. It is explained with examples.
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.
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 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.
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.
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.
The weight matrix of the perception, learning of processing elements with related to weight is included in this chapter.
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.
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.
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 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.
The unification algorithm, its expressions, and transactions are given in this chapter
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.
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
The topics included in this section are listed below
This section starts with a brief introduction to the intelligent agent. The different types of agents are covered in this topic as mentioned in the list below
All these types of agents are explained by a pictorial representation and example.
This section covers the following topics
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 is a graphical representation of a decision problem. It is discussed in this chapter in detail with examples.
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.
This section includes the objectives, functions, models, dynamic programming, linear programming, and examples.
DDN is a feature based extension of MDP. This section explains its features, representations, components along with examples.
Here you will learn the importance of set theory, what is a set, set notation, well-defined sets, number sets, set equality, a cardinality of a set, subsets and proper subsets and finally power sets. It also includes the basic concepts in set theory.
The joint probability distribution is explained in this section with an example and pictorial representation.
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.
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 Machine learning course, you’ll learn the basics and applications of AI, including machine learning, probabilistic reasoning, and few theories.
You should know how to use a word and how the to use the internet. Apart from this for this, machine learning training, you should have some basic knowledge of maths and statistics.
This artificial intelligence & machine learning training 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 machine learning courses.
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.|