kingers Posted May 12 Report Share Posted May 12 Artificial Intelligence And Machine Learning Course Published 1/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.50 GB | Duration: 11h 46mBasic 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.AusFilehttps://ausfile.com/pxizfwyj9lhf/yxusj.Udemy_Artificial_Intelligence_and_Machine_Learning_Course.part1.rarhttps://ausfile.com/96fye33hkh2a/yxusj.Udemy_Artificial_Intelligence_and_Machine_Learning_Course.part2.rarhttps://ausfile.com/ywganhzbsads/yxusj.Udemy_Artificial_Intelligence_and_Machine_Learning_Course.part3.rarhttps://ausfile.com/qji1vlhjj4lq/yxusj.Udemy_Artificial_Intelligence_and_Machine_Learning_Course.part4.rarRapidGatorhttps://rapidgator.net/file/24f89af19271cf3411a9f4d46f89977f/yxusj.Udemy_Artificial_Intelligence_and_Machine_Learning_Course.part1.rarhttps://rapidgator.net/file/1d5acd951b01ad56135a0614344b7b37/yxusj.Udemy_Artificial_Intelligence_and_Machine_Learning_Course.part2.rarhttps://rapidgator.net/file/583d103676678c9a933e45f5e7c6c9ad/yxusj.Udemy_Artificial_Intelligence_and_Machine_Learning_Course.part3.rarhttps://rapidgator.net/file/d98f8f0ac9e8cc406baa0176b3caea30/yxusj.Udemy_Artificial_Intelligence_and_Machine_Learning_Course.part4.rar Link to comment Share on other sites More sharing options...
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