AI Principles with Edge Computing

This course is part of Intelligent Digital Factories Specialization

Instructor: Subject Matter Expert

1,722 already enrolled

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments

Taught in English

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This course is part of the Intelligent Digital Factories Specialization

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There are 6 modules in this course

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.

By the end of this module on Artificial Intelligence and its Next Wave -Edge Computing, learners will be able to :Understand the scope of AI and Edge Computing; Able to acquire the skills in industries using the Edge AI technology; Able to interpret the role of edge computing in IoT

What's included

20 videos2 readings1 assignment1 discussion prompt

20 videos Total 115 minutes

About the Specialization 6 minutes
About the Course 4 minutes
AI & Edge Computing - Course Description 4 minutes
Relational Model of AI and Edge Computing 6 minutes
Artificial Intelligence (AI) Principles and Products - PART I 8 minutes
Artificial Intelligence (AI) Principles and Products - PART II 7 minutes
Machine Learning(ML) Fundamentals and Principles - PART I 8 minutes
Machine Learning(ML) Fundamentals and Principles - PART II 4 minutes
Application of ML in Manufacturing and Production Industries - PART I 6 minutes
Application of ML in Manufacturing and Production Industries - PART II 5 minutes
Diesel Generators with IoT Framework - A Model IoT Architecture - PART I 6 minutes
Diesel Generators with IoT Framework - A Model IoT Architecture - PART II 5 minutes
Quick Tour on Edge Devices in IoT - PART I 7 minutes
Quick Tour on Edge Devices in IoT - PART II 5 minutes
Quick Tour on Edge Devices in IoT - PART III 4 minutes
Edge AI and Cloud AI - An Overview - PART I 8 minutes
Edge AI and Cloud AI - An Overview - PART II 6 minutes
‘TinyML’ – A Cutting Edge Field - PART I 6 minutes
‘TinyML’ – A Cutting Edge Field - PART II 4 minutes
Case Study on 'Edge AI Practices in Industrial Applications' 8 minutes

2 readings Total 20 minutes

Course Reading 10 minutes
Course Glossary 10 minutes

1 assignment Total 30 minutes

Assessment on Artificial Intelligence (AI) and its Next Wave - Edge Computing 30 minutes

1 discussion prompt Total 10 minutes

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. 10 minutes

By the end of this module on Python Demos and Case-Studies on Machine Learning (ML) Algorithm Fundamentals, learners will be able to: interpret errors in machine learning, such as bias and variance; Gain insights of implementing ML in real-time domains such as healthcare, banking, and industries; Acquire the capability to perform Exploratory Data Analysis (EDA) processes using the Python programming language; Develop the skills to model ML algorithms for predicting lung cancer disease

What's included

23 videos1 assignment

23 videos Total 204 minutes

Machine Learning Algorithms Architecture - PART I 5 minutes
Machine Learning Algorithms Architecture - PART II 5 minutes
Machine Learning Types & Algorithm Selection Strategy 13 minutes
Bias and Variance - Trade-off - PART I 9 minutes
Bias and Variance - Trade-off - PART II 12 minutes
Machine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART I 6 minutes
Machine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART II 8 minutes
Preparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART I 7 minutes
Preparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART II 15 minutes
Supervised Machine Learning Algorithm- Principle and types - PART I 6 minutes
Supervised Machine Learning Algorithm- Principle and types - PART II 10 minutes
Supervised Machine Learning Algorithm- Principle and types - PART III 15 minutes
Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART I 7 minutes
Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART II 12 minutes
Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART III 2 minutes
Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART I 9 minutes
Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART II 17 minutes
Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART III 6 minutes
Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART IV 12 minutes
Implementation framework of ML algorithms – Lung Cancer Prediction - PART I 6 minutes
Implementation framework of ML algorithms – Lung Cancer Prediction - PART II 15 minutes
Future of COBOT – An application of ML in Oil & Gas industry - PART I 3 minutes
Future of COBOT – An application of ML in Oil & Gas industry - PART II 5 minutes

1 assignment Total 30 minutes

Assessment on Python Demos and Case-studies on Machine Learning(ML) Algorithm Fundamentals 30 minutes

By the end of this module on Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos, learners will be able to: Model a k-means clustering algorithm through a demo; Develop an application demo employing DBSCAN clustering on a dataset; Demonstrate the use of COBOTs in industrial automation.

What's included

22 videos1 assignment

22 videos Total 193 minutes

Principles of Unsupervised Machine Learning Algorithm - PART I 7 minutes
Principles of Unsupervised Machine Learning Algorithm - PART II 8 minutes
Clustering Algorithm - Principles with Hands-on approach using K-Means - Part I 12 minutes
Clustering Algorithm - Principles with Hands-on approach using K-Means - Part II 11 minutes
DBSCAN clustering algorithm - A hands on approach - PART I 7 minutes
DBSCAN clustering algorithm - A hands on approach - PART II 13 minutes
Dimensionality Reduction Algorithm – Principle & Implementation of PCA - PART I 12 minutes
Dimensionality Reduction Algorithm – Principle & Implementation of PCA - PART II 14 minutes
Linear Discriminant Analysis - A Quantitative Approach 9 minutes
Autonomous vehicle embedded with Dimensionality Reduction Algorithm - PART I 7 minutes
Autonomous vehicle embedded with Dimensionality Reduction Algorithm - PART II 6 minutes
Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART I 9 minutes
Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART II 7 minutes
Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART III 7 minutes
Model Based RL Algorithms – Principle and Example with DYNA Q Algorithm - PART I 7 minutes
Model Based RL Algorithms – Principle and Example with DYNA Q Algorithm - PART II 5 minutes
Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART I 7 minutes
Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART II 12 minutes
Model Free Reinforcement learning – exploring policy based methods - PART I 8 minutes
Model Free Reinforcement learning – exploring policy based methods - PART II 10 minutes
Deployment of Deep Q-Learning in Pick and Place COBOT - An Industrial application of ML - PART I 6 minutes
Deployment of Deep Q-Learning in Pick and Place COBOT - An Industrial application of ML - PART II 6 minutes

1 assignment Total 30 minutes

Assessment on Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos 30 minutes

By the end of this module on Principles and successful demonstrations of Neural Networks (Text Analytics), learners will be able to: demonstrate digit recognition using MLP and CNN; Develop a Python program to identify overfitting and underfitting issues in an ML model; develop an ML network using the WEKA tool.

What's included

25 videos1 assignment

25 videos Total 199 minutes

Fundamentals of Neural Network - PART I 8 minutes
Fundamentals of Neural Network - PART II 6 minutes
Fundamentals of Neural Network - PART III 8 minutes
Digit Recognition using MLP Model – Hands-on Practice - PART I 5 minutes
Digit Recognition using MLP Model – Hands-on Practice - PART II 2 minutes
Digit Recognition using MLP Model – Hands-on Practice - PART III 12 minutes
Gradient Descent Algorithm - Working Principle - PART I 6 minutes
Gradient Descent Algorithm- Working Principle - PART II 7 minutes
Backpropagation Algorithm – Working Principle - PART I 6 minutes
Backpropagation Algorithm – Working Principle - PART II 15 minutes
Backpropagation Algorithm – Working Principle - PART III 10 minutes
Cross-Entropy cost function and its implementation using MLP - PART I 7 minutes
Cross-Entropy cost function and its implementation using MLP - PART II 6 minutes
Overfitting and Regularization principles with a hands-on approach - PART I 7 minutes
Overfitting and Regularization principles with a hands-on approach - PART II 8 minutes
Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART I 12 minutes
Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART II 10 minutes
Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART III 17 minutes
Stochastic Gradient Descent Algorithm Principle and Analysis using IRIS Dataset - PART I 5 minutes
Stochastic Gradient Descent Algorithm Principle and Analysis using IRIS Dataset - PART II 6 minutes
Simulation of Neural Networks - Weka tool based exercise - PART I 7 minutes
Simulation of Neural Networks - Weka tool based exercise - PART II 8 minutes
Simulation of Neural Networks - Weka tool based exercise - PART III 10 minutes
Strategic deployment of shallow neural network for enhancing agriculture - a review exercise - PART I 6 minutes
Strategic deployment of shallow neural network for enhancing agriculture - a review exercise - PART II 7 minutes

1 assignment Total 30 minutes

Assessment on Principles and Successful Demonstrations of Neural Networks (Text Analytics) 30 minutes

By the end of this module on Advanced Applications with Deep Learning Networks, learners will be able to: Identify the vanishing and unstable gradient problems in a deep learning model; Apply DL for banana leaf disease detection; Apply CNN for Pneumonia Detection; Model an advanced CNN-based ML system to recognize images

What's included

20 videos1 assignment

20 videos Total 164 minutes

Vanishing Gradient Principles And Its Measurement In Sigmoid Activation Function-PART I 11 minutes
Vanishing Gradient Principles And Its Measurement In Sigmoid Activation Function-PART II 10 minutes
Unstable Gradient in Complex networks - PART I 4 minutes
Unstable Gradient in Complex networks - PART II 9 minutes
Unstable Gradient in Complex networks - PART III 10 minutes
A case study on application of DL for banana leaf disease prediction - PART I 9 minutes
A case study on application of DL for banana leaf disease prediction - PART II 8 minutes
Introduction to convolutional neural networks - PART I 9 minutes
Introduction to convolutional neural networks - PART II 5 minutes
Introduction to convolutional neural networks - PART III 7 minutes
Image Recognition principles with a Case study approach in Retail Industry - PART I 6 minutes
Image Recognition principles with a Case study approach in Retail Industry - PART II 5 minutes
Applications of CNN 13 minutes
Generative Network Principles - PART I 11 minutes
Generative Network Principles - PART II 7 minutes
Introduction to RNN 9 minutes
Properties and Construction of RNN - PART I 7 minutes
Properties and Construction of RNN - PART II 7 minutes
Implementation of RNN - PART I 3 minutes
Implementation of RNN - PART II 15 minutes

1 assignment Total 30 minutes

Assessment on Advanced Applications with Deep Learning Networks 30 minutes

By the end of this module on IoT with AI and edge computing, learners will be able to: Understand the working principles of the TinyML system; Identify the need for compression techniques; Interpret High Computing Machine based Edge Architecture; Learn the functionalities of the Arduino IDE and programming on the Arduino Nano BLE development board