Artificial Intelligence And Machine Learning Course

Basic ideas and techniques in the design of intelligent computer systems.

What you'll learn

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.

Requirements

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

Description

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 belowGreater precision and accuracy can be achieved through AIThese machines do not get affected by the planetary environment or atmosphereRobots can be programmed to do the works which are difficult for the human beings to completeAI will open up doors to new technological breakthroughsAs they are machines they don’t stop for sleep or food or rest. They just need some source of energy to workFraud detection becomes easier with artificial intelligenceUsing AI the time-consuming tasks can be done more efficientlyDangerous tasks can be done using AI machines as it affects only the machines and not the human beingsArtificial Intelligence has become the centrepiece of strategic decision making for organizations. It is disrupting the way industries function - from sales and marketing to finance and HR, companies are betting on AI to give them a competitive edge. This course is a thoughtfully created course designed specifically for business people and does not require any programming. Through this course you will learn about the current state of AI, how it's disrupting businesses globally and in diverse fields, how it might impact your current role and what you can do about it. This course also dives into the various building blocks of AI and why it's necessary for you to have a high-level overview of these topics in today's data-driven world.

Overview

Section 1: Artificial Intelligence And Machine Learning Training Course

Lecture 1 Introduction to Artificial Intelligence

Lecture 2 Definition of Artificial Intelligence

Lecture 3 Intelligent Agents

Lecture 4 Information on State Space Search

Lecture 5 Graph theory on state space search

Lecture 6 Solution for State Space Search

Lecture 7 FSM

Lecture 8 BFS on Graph

Lecture 9 DFS algo

Lecture 10 DFS with iterative deepening

Lecture 11 Backtracking algo

Lecture 12 Trace backtracking on graph part_1

Lecture 13 Trace backtracking on graph part_2

Lecture 14 Summary_state space search

Lecture 15 Heuristic search overview

Lecture 16 Heuristic calculation technique part _1

Lecture 17 Heuristic calculation technique part _2

Lecture 18 Simple hill climbing

Lecture 19 Best first search algo

Lecture 20 Tracing best first search-1

Lecture 21 Best first search continue

Lecture 22 Admissibility-1

Lecture 23 Mini-max

Lecture 24 Two ply min max

Lecture 25 Alpha beta pruning

Lecture 26 Machine learning_overview

Lecture 27 Perceptron learning

Lecture 28 Perceptron with linearly separable

Lecture 29 Backpropagation with multilayer neuron

Lecture 30 W for hidden node and backpropagation algo

Lecture 31 Backpropagation algorithm explained

Lecture 32 Backpropagation calculation_part01

Lecture 33 Backpropagation calculation_part02

Lecture 34 Updation of weight and cluster

Lecture 35 K-Means cluster‚NNalgo and appliaction of machine learning

Lecture 36 Logics_reasoning_overview_propositional calculas part 1

Lecture 37 Logics_reasoning_overview_propositional calculas part 2

Lecture 38 Propotional calculus

Lecture 39 Predicate calculus

Lecture 40 First order predicate calculus

Lecture 41 modus ponus,tollens

Lecture 42 Unification and deduction process

Lecture 43 Resolution refutation

Lecture 44 Resolution refutation in detail

Lecture 45 Resolution refutation example-2 convert into clause

Lecture 46 Resoultion refutation example-2 apply refutation

Lecture 47 Unification substitution andskolemization

Lecture 48 Prolog overview_some part of reasoning

Lecture 49 Model based and CBR reasoning

Lecture 50 Production system

Lecture 51 Trace of production system

Lecture 52 Knight tour prob in chessboard

Lecture 53 Goal driven_data driven production system part _ 1

Lecture 54 Goal driven_data driven production system part _ 2

Lecture 55 Goal driven Vs data driven and inserting and removing facts

Lecture 56 Defining rules and commands

Lecture 57 CLIPS installation and clipstutorial 1

Lecture 58 CLIPS tutorial 2

Lecture 59 CLIPS tutorial 3

Lecture 60 CLIPS tutorial 4

Lecture 61 CLIPS tutorial 5_part01

Lecture 62 CLIPS tutorial 5_part02

Lecture 63 Tutorial 6

Lecture 64 CLIPS tutorial 7

Lecture 65 CLIPS tutorial 8

Lecture 66 Variable in pattern tutorial 9

Lecture 67 Tutorial 10

Lecture 68 More on wildcardmatching_part01

Lecture 69 More on wildcardmatching_part02

Lecture 70 More on variables

Lecture 71 Deffacts and deftemplates_part01

Lecture 72 Deffacts and deftemplates_part02

Lecture 73 Template indetail part1

Lecture 74 Not operator

Lecture 75 Forall and exists_part01

Lecture 76 Forall and exists_part02

Lecture 77 Truth and control

Lecture 78 Tutorial 12

Lecture 79 Intelligent agent

Lecture 80 Simple reflex agent

Lecture 81 Simple reflex agent with internal state

Lecture 82 Goal based agent

Lecture 83 Utility based agent

Lecture 84 Basics of utility theory

Lecture 85 Maximum expected utility

Lecture 86 Decision theory and decision network

Lecture 87 Reinforcement learning

Lecture 88 MDPand DDN

Lecture 89 Basics of set theory part _ 1

Lecture 90 Basics of set theory part _ 2

Lecture 91 Probability distribution

Lecture 92 Baysian rule for conditional probability

Lecture 93 Examples of Bayes Theorm

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.