Artificial intelligence

by PRAKASH UPRETI SOCIAL MEDIA MANAGER/ CONTENT

Defining Artificial Intelligence course:

            Artificial intelligence (AI) also Machine Intelligence (MI) is the simulation of a human intelligence through machines & mainly through computer systems. Artificial intelligence (AI) is a sub field of a computer. Artificial Intelligence (AI) allows computers to do things which are supposed to do 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.

What is the use of Artificial Intelligence course?

  These days Artificial Intelligence (AI) has been used in wide range like we can use Artificial Intelligence in robots, remote sensing & medical diagnosis etc. Smart cars can be an example of Artificial Intelligence what it does? These cares are working as self-driving cars, in self-driving a car you can read newspaper while heading towards your office and also does some other important works.it will help you in saving your precious time.

 There are some advantages of Artificial Intelligence below.

·       The Artificial Intelligence Machines do not get affected by the planetary atmosphere.

·       The work which is difficult for human being that can be completed by robot with the help of Artificial Intelligence.

·       Machines do not need sleep, food, do not need rest as well.

·       One of the most important thing of Artificial Intelligence is it can detect fraud easily.

What are the objectives of Artificial Intelligence (AI) and Machine learning course?

   After completing Artificial Intelligence course with Mildaintraining what you will learn is:

·       Applications of Artificial Intelligence (AI) Natural Language Processing (NLP), Robotics/Vision

·       Introduction to Artificial Intelligence & Intelligent agents, history of Artificial Intelligence (AI).

·       To apply classification, clustering, retrieval, and recommender systems & deep learning.

   The Projects are included in Online Artificial Intelligence (AI) training.

There will be Practical hands on experience on applications followed by generating one application based on personal experience and notable AI projects.

The Trends of learning Artificial Intelligence (AI):

    Artificial Intelligence is nowadays applied in self-driving cars, personal assistants, surveillance systems, robotic arms in manufacturing, financial services, cyber security, searching web, video games, image analysis, machine vision, code analysis & product recommendations, such applications are use in AI process & practice to interpret, information from a variety of sources & adapts them into intelligent, social and goal-directed behavior.   

The Best AI Companies to Work for in 2018: According to Glass door website Microsoft, Facebook, Adobe, Uber, and NVIDIA are the best Artificial Intelligence companies to work for in 2018. The above companies together have around 99 open Artificial Intelligence positions today.

 Around 512 Artificial Intelligence (AI) jobs on Glassdoor today, with AI Software Engineer, AI Data Scientist, AI Software Development Engineer and AI Research Scientist having a combined total of 118 open positions.

Around 67% of all AI jobs listed on Glassdoor are located in San Jose San Francisco, Seattle, Los Angeles & New York City.

Expected Salaries of AI professionals earned:

AI Developer-$104900 PA

Senior AI expert-$111000 PA

AI Professional-96000 PA

Disclaimer-The Information realated to salary trends of AI expert cannot be same it will depend on different locations it can vary up to some extent.

Pre requisites:

However there is not any specific requirement for starting Artificial Intelligence (AI) course but if you have knowledge of discrete mathematics, Graph, Trees and basic knowledge of statistics it will be having extra edge.

FaQ:

Why should I take Artificial Intelligence Training from mildaintrainings.com?

    You should try Artificial Intelligence (AI) Training from Mildaintrainings as our trainers have 15 plus years of industry practical experience and will provides 6 months technical support.

Do I get the training certificate?

    Yes At Mildaintrainings we will provide participation certificate after completing Artificial Intelligence (AI) course from MildainTraining.

When the classes will be held?

Classes will be held weekend as well as weekdays as per schedule.

What if I miss the AI class?

If you miss the class in that case backup class can be adjusted in next live session.

What is Artificial Intelligence course duration?

Artificial Intelligence course at MildainTraining will be of 32 hrs. Or 4 days.

What is the outcome of Artificial Intelligence (AI) course at Mildaintraining.com?

     Mildaintraining is one of the best training company which provides ‘’Artificial Intelligence’’ Training through our industry experience instructor, having more than 10 years of industry practical experience.

Software & hardware requirement for learning Artificial

Intelligence (AI) course.

Hardware Requirements:

CPU: core 2 Duo/Athlon x2 or better

Ram: 1.5 GB

Video Card: NVIDIA 7800 Series, ATI Radeon 1800 series or better

Graphic Card: 512 MB of Graphic Memory

Storage: 12GB (Approx.)

Sound Card: DirectX9.0c Compatible.

Software Requirements:

A fast CPU

Large amounts of RAM needed

Large hard drive, a good Graphics Card and also need input output devices.

Artificial Intelligence (AI) such as LISP OR PROLOG will require.

Curriculum of AI

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 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 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 in used.

·         Graph theory on state space search

Under this chapter you will learn what a graph theory is and how it may be used to model problem solving as a search through a graph of problem states. The 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 iterative deepening search is, it is properties & algorithm along with examples.

·         Backtracking algorithm

Backtracking is an implementation of Artificial Intelligence. This section explains what is backtracking and 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.

·         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 a detailed game example.

Section 4: Machine Learning

·         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 are 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.

Section 5: Logics and Reasoning

·         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 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, 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.

Section 6: Rule Based Programming

·         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