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Artificial Intelligence and Machine Learning Course
Rating: 4.2 out of 5(22 ratings)
4,498 students

Artificial Intelligence and Machine Learning Course

Basic ideas and techniques in the design of intelligent computer systems.
Created byEDUCBA Bridging the Gap
Last updated 1/2024
English

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.

Course content

1 section • 93 lectures • 11h 45m total length

  • Introduction to Artificial Intelligence8:15
  • Definition of Artificial Intelligence7:13
  • Intelligent Agents6:36
  • Information on State Space Search7:07
  • Graph theory on state space search9:28
  • Solution for State Space Search8:13

    Explore state space search by modeling problems with nodes, arcs, and start and goal states, illustrated by the eight puzzle and traveling salesman problem.

  • FSM8:46
  • BFS on Graph7:26
  • DFS algo10:07
  • DFS with iterative deepening8:47
  • Backtracking algo11:17
  • Trace backtracking on graph part_16:57
  • Trace backtracking on graph part_29:39
  • Summary_state space search4:39
  • Heuristic search overview8:03
  • Heuristic calculation technique part _16:28
  • Heuristic calculation technique part _26:27
  • Simple hill climbing7:43
  • Best first search algo7:23
  • Tracing best first search-111:41
  • Best first search continue5:35
  • Admissibility-112:18

    Explore best-first search using f(n)=g(n)+h(n), where h(n) counts tiles out of place, and select the lowest f to expand, yielding the admissible, optimal a star path.

  • Mini-max12:11
  • Two ply min max7:50
  • Alpha beta pruning9:48
  • Machine learning_overview8:44
  • Perceptron learning14:16
  • Perceptron with linearly separable7:07
  • Backpropagation with multilayer neuron7:44
  • W for hidden node and backpropagation algo9:56
  • Backpropagation algorithm explained12:07
  • Backpropagation calculation_part017:12
  • Backpropagation calculation_part027:09
  • Updation of weight and cluster7:43
  • K-Means cluster‚NNalgo and appliaction of machine learning6:21
  • Logics_reasoning_overview_propositional calculas part 17:06
  • Logics_reasoning_overview_propositional calculas part 25:05
  • Propotional calculus7:51
  • Predicate calculus6:17
  • First order predicate calculus7:33
  • modus ponus,tollens8:11
  • Unification and deduction process7:50
  • Resolution refutation11:05
  • Resolution refutation in detail8:58
  • Resolution refutation example-2 convert into clause7:40
  • Resoultion refutation example-2 apply refutation7:03
  • Unification substitution andskolemization7:20
  • Prolog overview_some part of reasoning12:06

    Explore Prolog as a logic programming language and its predicate calculus, including horn clauses and resolution. Examine deductive, inductive, abductive, analogical, and non-monotonic reasoning.

  • Model based and CBR reasoning5:00
  • Production system7:42
  • Trace of production system7:17

    Trace a simple production system by applying the rules ba -> ab, ca -> ac, and cb -> bc to a starting memory, iterating substitutions and resolving conflicts.

  • Knight tour prob in chessboard9:12
  • Goal driven_data driven production system part _ 15:33
  • Goal driven_data driven production system part _ 27:19
  • Goal driven Vs data driven and inserting and removing facts7:05
  • Defining rules and commands8:47
  • CLIPS installation and clipstutorial 17:57
  • CLIPS tutorial 27:02
  • CLIPS tutorial 37:25

    Learn to assert facts in CLIPS with case sensitive entries and spaces, then retract, watch memory with watch commands, and reset to initial facts.

  • CLIPS tutorial 46:39

    Explore clips basics by building if-then rules from facts, using diff rule syntax, and asserting actions to fire rules and produce a sound like quack when the animal is duck.

  • CLIPS tutorial 5_part015:28
  • CLIPS tutorial 5_part023:27
  • Tutorial 62:33
  • CLIPS tutorial 76:18
  • CLIPS tutorial 86:03
  • Variable in pattern tutorial 95:12
  • Tutorial 105:11
  • More on wildcardmatching_part017:44
  • More on wildcardmatching_part025:32
  • More on variables8:12
  • Deffacts and deftemplates_part015:31
  • Deffacts and deftemplates_part027:10
  • Template indetail part17:03
  • Not operator6:16

    Apply the not operator in the Clips programming language to negate a predicate, using personal data (name and weight) to check birthday and print when not today.

  • Forall and exists_part015:41
  • Forall and exists_part025:18
  • Truth and control6:45
  • Tutorial 124:36
  • Intelligent agent6:36
  • Simple reflex agent6:45
  • Simple reflex agent with internal state6:11
  • Goal based agent4:12
  • Utility based agent8:09
  • Basics of utility theory8:04
  • Maximum expected utility7:02
  • Decision theory and decision network9:01
  • Reinforcement learning7:17
  • MDPand DDN11:10
  • Basics of set theory part _ 15:53
  • Basics of set theory part _ 26:25
  • Probability distribution8:54

    Explore random variables and probability distributions, including boolean, discrete, and continuous types; apply binomial, Poisson, and normal models, and use joint, conditional, and independence rules.

  • Baysian rule for conditional probability11:27
  • Examples of Bayes Theorm5:22

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 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 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.

Who this course is for:

  • 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.

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