Course: AI Principles with Edge Computing
Modules:
About the Specialization, About the Course, AI & Edge Computing - Course Description, Relational Model of AI and Edge Computing, Artificial Intelligence (AI) Principles and Products - PART I, Artificial Intelligence (AI) Principles and Products - PART II, Machine Learning(ML) Fundamentals and Principles - PART I, Machine Learning(ML) Fundamentals and Principles - PART II, Application of ML in Manufacturing and Production Industries - PART I, Application of ML in Manufacturing and Production Industries - PART II, Diesel Generators with IoT Framework - A Model IoT Architecture - PART I, Diesel Generators with IoT Framework - A Model IoT Architecture - PART II, Quick Tour on Edge Devices in IoT - PART I, Quick Tour on Edge Devices in IoT - PART II, Quick Tour on Edge Devices in IoT - PART III, Edge AI and Cloud AI - An Overview - PART I, Edge AI and Cloud AI - An Overview - PART II, ‘TinyML’ – A Cutting Edge Field - PART I, ‘TinyML’ – A Cutting Edge Field - PART II, Case Study on 'Edge AI Practices in Industrial Applications'
Machine Learning Algorithms  Architecture - PART I, Machine Learning Algorithms  Architecture - PART II, Machine Learning Types & Algorithm Selection Strategy, Bias and Variance - Trade-off - PART I, Bias and Variance - Trade-off - PART II, Machine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART I, Machine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART II, Preparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART I, Preparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART II, Supervised Machine Learning Algorithm- Principle and types - PART I, Supervised Machine Learning Algorithm- Principle and types - PART II, Supervised Machine Learning Algorithm- Principle and types - PART III, Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART I, Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART II, Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART III, Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART I, Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART II, Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART III, Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART IV, Implementation framework of ML algorithms – Lung Cancer Prediction - PART I, Implementation framework of ML algorithms – Lung Cancer Prediction - PART II, Future of COBOT – An application of ML in Oil & Gas industry - PART I, Future of COBOT – An application of ML in Oil & Gas industry - PART II
Principles of Unsupervised Machine Learning Algorithm - PART I, Principles of Unsupervised Machine Learning Algorithm - PART II, Clustering  Algorithm - Principles with Hands-on approach using K-Means - Part I, Clustering  Algorithm - Principles with Hands-on approach using K-Means - Part II, DBSCAN clustering algorithm - A hands on approach - PART I, DBSCAN clustering algorithm - A hands on approach - PART II, Dimensionality Reduction Algorithm – Principle & Implementation of PCA - PART I, Dimensionality Reduction Algorithm – Principle & Implementation of PCA - PART II, Linear Discriminant Analysis - A Quantitative Approach, Autonomous vehicle embedded with  Dimensionality Reduction Algorithm - PART I, Autonomous vehicle embedded with  Dimensionality Reduction Algorithm - PART II, Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART I, Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART II, Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART III, Model Based RL Algorithms – Principle and Example with DYNA Q Algorithm - PART I, Model Based RL Algorithms – Principle and Example with DYNA Q Algorithm - PART II, Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART I, Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART II, Model Free Reinforcement learning – exploring policy based methods - PART I, Model Free Reinforcement learning – exploring policy based methods - PART II, Deployment of Deep Q-Learning in Pick and Place COBOT - An Industrial  application of ML - PART I, Deployment of Deep Q-Learning in Pick and Place COBOT - An Industrial  application of ML - PART II
Fundamentals of Neural Network - PART I, Fundamentals of Neural Network - PART II, Fundamentals of Neural Network - PART III, Digit Recognition using MLP Model – Hands-on Practice - PART I, Digit Recognition using MLP Model – Hands-on Practice - PART II, Digit Recognition using MLP Model – Hands-on Practice - PART III, Gradient Descent Algorithm - Working Principle - PART I, Gradient Descent Algorithm- Working Principle - PART II, Backpropagation Algorithm – Working Principle - PART I, Backpropagation Algorithm – Working Principle - PART II, Backpropagation Algorithm – Working Principle - PART III, Cross-Entropy cost function and its implementation using MLP - PART I, Cross-Entropy cost function and its implementation using MLP - PART II, Overfitting and Regularization principles with a hands-on approach - PART I, Overfitting and Regularization principles with a hands-on approach - PART II, Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART I, Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART II, Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART III, Stochastic Gradient Descent Algorithm Principle and Analysis using IRIS Dataset - PART I, Stochastic Gradient Descent Algorithm Principle and Analysis using IRIS Dataset - PART II, Simulation of Neural Networks - Weka tool based exercise - PART I, Simulation of Neural Networks - Weka tool based exercise - PART II, Simulation of Neural Networks - Weka tool based exercise - PART III, Strategic deployment of shallow neural network for enhancing agriculture - a review exercise - PART I, Strategic deployment of shallow neural network for enhancing agriculture - a review exercise - PART II
Vanishing Gradient Principles And Its Measurement In Sigmoid Activation Function-PART I, Vanishing Gradient Principles And Its Measurement In Sigmoid Activation Function-PART II, Unstable Gradient in Complex networks - PART I, Unstable Gradient in Complex networks - PART II, Unstable Gradient in Complex networks - PART III, A case study on application of DL for banana leaf disease prediction - PART I, A case study on application of DL for banana leaf disease prediction - PART II, Introduction to convolutional neural networks - PART I, Introduction to convolutional neural networks - PART II, Introduction to convolutional neural networks - PART III, Image Recognition principles with a Case study approach in Retail Industry - PART I, Image Recognition principles with a Case study approach in Retail Industry - PART II, Applications of CNN, Generative Network Principles - PART I, Generative Network Principles - PART II, Introduction to RNN, Properties and Construction of RNN - PART I, Properties and Construction of RNN - PART II, Implementation of RNN - PART I, Implementation of RNN - PART II
IoT Architecture with AI - PART I, IoT Architecture with AI - PART II, IoT Architecture with AI - PART III, High Computing Machine based Edge Architecture, Distributed Training - PART I, Distributed Training - PART II, Compression technique, Software tools and their scope for AI and ML - PART I, Software tools and their scope for AI and ML - PART II, Tensor Flow Library - Principles, Keras Library - Principles - PART I, Keras Library - Principles - PART II, Arduino IDE for Edge Computing - PART I, Arduino IDE for Edge Computing - PART II, Basics of Arduino Nano BLE Board, Programming with Arduino Nano BLE(ANB), Sinewave prediction model analysis