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AI Principles with Edge Computing · LearnSpace
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courseraIT и технологии

AI Principles with Edge Computing

Курс от L&T EduTech
Средний≈ 20.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

With the paradigm shift of Digital Transformation in industries, there exists a huge volume of digital data in cloud storage about the Men, Materials and Machines of the organization. This data incurs a lot of information which could be used for process planning, predictive failures and business optimization. This course aims to equip the learners with various strategic principles of Artificial Intelligence theory which helps to extract such information from the pool of available data. The reach of AI in every area is consistently growing along with the features of programming. The course introduces appropriate programming skills blended in the modules and the learners will be able to learn by doing lots of practice problems. The long-term vision of AI, with Edge operations are explained in the course along with the principles required in implementing Edge AI. The learner can distinguish and will be able to segment the cloud and edge-based operations appropriately for the real-world problems. The different exercise problems with relevant software and hardware architecture support the learning of Edge AI with suitable metrics. On the whole the learners will get an exciting journey of understanding and applying AI algorithms, processing the algorithms for edge and implementing sample edge AI solutions. Edge AI products available in the market are introduced to the learners and this provides the learners with an ability to map their AI skills with suitable upcoming career options.

Навыки, которые вы освоите

Machine Learning AlgorithmsConvolutional Neural NetworksInternet Of ThingsArtificial Neural NetworksMachine LearningUnsupervised LearningReinforcement LearningArtificial IntelligenceEmerging TechnologiesAI IntegrationsModel EvaluationData PreprocessingNatural Language ProcessingImage AnalysisPython ProgrammingHealthcare 5.0Deep Learning

Программа курса

6 модулей · 136 учебных материалов

01Artificial Intelligence (AI) and its Next Wave - Edge Computing24 материалов

Welcome to the Course

About the SpecializationВидеоAbout the CourseВидеоCourse ReadingЧтениеCourse GlossaryЧтение

Artificial Intelligence (AI) and its Next Wave - Edge Computing

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L&T Edutech

AI Principles with Edge Computing
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 20.2 ч

6 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
AI & Edge Computing - Course DescriptionВидео
Relational Model of AI and Edge ComputingВидео
Artificial Intelligence (AI) Principles and Products - PART IВидео
Artificial Intelligence (AI) Principles and Products - PART IIВидео
Machine Learning(ML) Fundamentals and Principles - PART IВидео
Machine Learning(ML) Fundamentals and Principles - PART IIВидео
Application of ML in Manufacturing and Production Industries - PART IВидео
Application of ML in Manufacturing and Production Industries - PART IIВидео
Diesel Generators with IoT Framework - A Model IoT Architecture - PART IВидео
Diesel Generators with IoT Framework - A Model IoT Architecture - PART IIВидео
Quick Tour on Edge Devices in IoT - PART IВидео
Quick Tour on Edge Devices in IoT - PART IIВидео
Quick Tour on Edge Devices in IoT - PART IIIВидео
Edge AI and Cloud AI - An Overview - PART IВидео
Edge AI and Cloud AI - An Overview - PART IIВидео
‘TinyML’ – A Cutting Edge Field - PART IВидео
‘TinyML’ – A Cutting Edge Field - PART IIВидео
Case Study on 'Edge AI Practices in Industrial Applications'Видео
Statement: With the upcoming Edge AI Solutions in Healthcare and Automotive Industries, there exists a propulsion towards the demand for special Microcontrollers in Embedded System. This may lead to lot of research focus in Opinion 01: Power optimized, tiny microcontrollers; Opinion 02: High computing, High speed Microcontrollers; Prompt your opinion, either for Opinion 01 or 02 with defendable data evidence foreseeing volume of the Digital Transformations happening in the industries. Обсуждение

Assessment on Artificial Intelligence (AI) and its Next Wave - Edge Computing

Assessment on Artificial Intelligence (AI) and its Next Wave - Edge ComputingЗадание
02Python Demos and Case-studies on Machine Learning(ML) Algorithm Fundamentals24 материалов

Python Demos and Case-studies on Machine Learning(ML) Algorithm Fundamentals

Machine Learning Algorithms Architecture - PART IВидеоMachine Learning Algorithms Architecture - PART IIВидеоMachine Learning Types & Algorithm Selection StrategyВидеоBias and Variance - Trade-off - PART IВидеоBias and Variance - Trade-off - PART IIВидеоMachine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART IВидеоMachine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART IIВидеоPreparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART IВидеоPreparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART IIВидеоSupervised Machine Learning Algorithm- Principle and types - PART IВидеоSupervised Machine Learning Algorithm- Principle and types - PART IIВидеоSupervised Machine Learning Algorithm- Principle and types - PART IIIВидеоRegression Algorithm - Principle & Practicing exercise on Salary Prediction - PART IВидеоRegression Algorithm - Principle & Practicing exercise on Salary Prediction - PART IIВидеоRegression Algorithm - Principle & Practicing exercise on Salary Prediction - PART IIIВидеоClassification algorithm-Decision tree algorithm for EV vehicle purchase - PART IВидеоClassification algorithm-Decision tree algorithm for EV vehicle purchase - PART IIВидеоClassification algorithm-Decision tree algorithm for EV vehicle purchase - PART IIIВидеоClassification algorithm-Decision tree algorithm for EV vehicle purchase - PART IVВидеоImplementation framework of ML algorithms – Lung Cancer Prediction - PART IВидеоImplementation framework of ML algorithms – Lung Cancer Prediction - PART IIВидеоFuture of COBOT – An application of ML in Oil & Gas industry - PART IВидеоFuture of COBOT – An application of ML in Oil & Gas industry - PART IIВидео

Assessment on Python Demos and Case-studies on Machine Learning(ML) Algorithm Fundamentals

Assessment on Python Demos and Case-studies on Machine Learning(ML) Algorithm FundamentalsЗадание
03Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos23 материалов

Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos

Principles of Unsupervised Machine Learning Algorithm - PART IВидеоPrinciples of Unsupervised Machine Learning Algorithm - PART IIВидеоClustering Algorithm - Principles with Hands-on approach using K-Means - Part IВидеоClustering Algorithm - Principles with Hands-on approach using K-Means - Part IIВидеоDBSCAN clustering algorithm - A hands on approach - PART IВидеоDBSCAN clustering algorithm - A hands on approach - PART IIВидеоDimensionality Reduction Algorithm – Principle & Implementation of PCA - PART IВидеоDimensionality Reduction Algorithm – Principle & Implementation of PCA - PART IIВидеоLinear Discriminant Analysis - A Quantitative ApproachВидеоAutonomous vehicle embedded with Dimensionality Reduction Algorithm - PART IВидеоAutonomous vehicle embedded with Dimensionality Reduction Algorithm - PART IIВидеоReinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART IВидеоReinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART IIВидеоReinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART IIIВидеоModel Based RL Algorithms – Principle and Example with DYNA Q Algorithm - PART IВидеоModel Based RL Algorithms – Principle and Example with DYNA Q Algorithm - PART IIВидеоParadigm shift in health care diagnosis with reinforcement learning - a review exercise - PART IВидеоParadigm shift in health care diagnosis with reinforcement learning - a review exercise - PART IIВидеоModel Free Reinforcement learning – exploring policy based methods - PART IВидеоModel Free Reinforcement learning – exploring policy based methods - PART IIВидеоDeployment of Deep Q-Learning in Pick and Place COBOT - An Industrial application of ML - PART IВидеоDeployment of Deep Q-Learning in Pick and Place COBOT - An Industrial application of ML - PART IIВидео

Assessment on Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos

Assessment on Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demosЗадание
04Principles and Successful Demonstrations of Neural Networks (Text Analytics)26 материалов

Principles and Successful Demonstrations of Neural Networks (Text Analytics)

Fundamentals of Neural Network - PART IВидеоFundamentals of Neural Network - PART IIВидеоFundamentals of Neural Network - PART IIIВидеоDigit Recognition using MLP Model – Hands-on Practice - PART IВидеоDigit Recognition using MLP Model – Hands-on Practice - PART IIВидеоDigit Recognition using MLP Model – Hands-on Practice - PART IIIВидеоGradient Descent Algorithm - Working Principle - PART IВидеоGradient Descent Algorithm- Working Principle - PART IIВидеоBackpropagation Algorithm – Working Principle - PART IВидеоBackpropagation Algorithm – Working Principle - PART IIВидеоBackpropagation Algorithm – Working Principle - PART IIIВидеоCross-Entropy cost function and its implementation using MLP - PART IВидеоCross-Entropy cost function and its implementation using MLP - PART IIВидеоOverfitting and Regularization principles with a hands-on approach - PART IВидеоOverfitting and Regularization principles with a hands-on approach - PART IIВидеоDigit Recognition System for Visually Impaired – CNN based ML Algorithm - PART IВидеоDigit Recognition System for Visually Impaired – CNN based ML Algorithm - PART IIВидеоDigit Recognition System for Visually Impaired – CNN based ML Algorithm - PART IIIВидеоStochastic Gradient Descent Algorithm Principle and Analysis using IRIS Dataset - PART IВидеоStochastic Gradient Descent Algorithm Principle and Analysis using IRIS Dataset - PART IIВидеоSimulation of Neural Networks - Weka tool based exercise - PART IВидеоSimulation of Neural Networks - Weka tool based exercise - PART IIВидеоSimulation of Neural Networks - Weka tool based exercise - PART IIIВидеоStrategic deployment of shallow neural network for enhancing agriculture - a review exercise - PART IВидеоStrategic deployment of shallow neural network for enhancing agriculture - a review exercise - PART IIВидео

Assessment on Principles and Successful Demonstrations of Neural Networks (Text Analytics)

Assessment on Principles and Successful Demonstrations of Neural Networks (Text Analytics)Задание
05Advanced Applications with Deep Learning Networks21 материалов

Advanced Applications with Deep Learning Networks

Vanishing Gradient Principles And Its Measurement In Sigmoid Activation Function-PART IВидеоVanishing Gradient Principles And Its Measurement In Sigmoid Activation Function-PART IIВидеоUnstable Gradient in Complex networks - PART IВидеоUnstable Gradient in Complex networks - PART IIВидеоUnstable Gradient in Complex networks - PART IIIВидеоA case study on application of DL for banana leaf disease prediction - PART IВидеоA case study on application of DL for banana leaf disease prediction - PART IIВидеоIntroduction to convolutional neural networks - PART IВидеоIntroduction to convolutional neural networks - PART IIВидеоIntroduction to convolutional neural networks - PART IIIВидеоImage Recognition principles with a Case study approach in Retail Industry - PART IВидеоImage Recognition principles with a Case study approach in Retail Industry - PART IIВидеоApplications of CNNВидеоGenerative Network Principles - PART IВидеоGenerative Network Principles - PART IIВидеоIntroduction to RNNВидеоProperties and Construction of RNN - PART IВидеоProperties and Construction of RNN - PART IIВидеоImplementation of RNN - PART IВидеоImplementation of RNN - PART IIВидео

Assessment on Advanced Applications with Deep Learning Networks

Assessment on Advanced Applications with Deep Learning NetworksЗадание
06IoT with AI and edge computing18 материалов

IoT with AI and edge computing

IoT Architecture with AI - PART IВидеоIoT Architecture with AI - PART IIВидеоIoT Architecture with AI - PART IIIВидеоHigh Computing Machine based Edge ArchitectureВидеоDistributed Training - PART IВидеоDistributed Training - PART IIВидеоCompression techniqueВидеоSoftware tools and their scope for AI and ML - PART IВидеоSoftware tools and their scope for AI and ML - PART IIВидеоTensor Flow Library - PrinciplesВидеоKeras Library - Principles - PART IВидеоKeras Library - Principles - PART IIВидеоArduino IDE for Edge Computing - PART IВидеоArduino IDE for Edge Computing - PART IIВидеоBasics of Arduino Nano BLE BoardВидеоProgramming with Arduino Nano BLE(ANB)ВидеоSinewave prediction model analysisВидео

Assessment on IoT with AI and edge computing

Assessment on IoT with AI and edge computingЗадание