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AI Principles with Edge Computing

This course is part of Intelligent Digital Factories Specialization

Instructor: Subject Matter Expert

1,854 already enrolled

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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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Intelligent Digital Factories Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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 videosTotal 115 minutes

About the Specialization6 minutes
About the Course4 minutes
AI & Edge Computing - Course Description4 minutes
Relational Model of AI and Edge Computing6 minutes
Artificial Intelligence (AI) Principles and Products - PART I8 minutes
Artificial Intelligence (AI) Principles and Products - PART II7 minutes
Machine Learning(ML) Fundamentals and Principles - PART I8 minutes
Machine Learning(ML) Fundamentals and Principles - PART II4 minutes
Application of ML in Manufacturing and Production Industries - PART I6 minutes
Application of ML in Manufacturing and Production Industries - PART II5 minutes
Diesel Generators with IoT Framework - A Model IoT Architecture - PART I6 minutes
Diesel Generators with IoT Framework - A Model IoT Architecture - PART II5 minutes
Quick Tour on Edge Devices in IoT - PART I7 minutes
Quick Tour on Edge Devices in IoT - PART II5 minutes
Quick Tour on Edge Devices in IoT - PART III4 minutes
Edge AI and Cloud AI - An Overview - PART I8 minutes
Edge AI and Cloud AI - An Overview - PART II6 minutes
‘TinyML’ – A Cutting Edge Field - PART I6 minutes
‘TinyML’ – A Cutting Edge Field - PART II4 minutes
Case Study on 'Edge AI Practices in Industrial Applications'8 minutes

2 readingsTotal 20 minutes

Course Reading10 minutes
Course Glossary10 minutes

1 assignmentTotal 30 minutes

Assessment on Artificial Intelligence (AI) and its Next Wave - Edge Computing30 minutes

1 discussion promptTotal 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 videosTotal 204 minutes

Machine Learning Algorithms Architecture - PART I5 minutes
Machine Learning Algorithms Architecture - PART II5 minutes
Machine Learning Types & Algorithm Selection Strategy13 minutes
Bias and Variance - Trade-off - PART I9 minutes
Bias and Variance - Trade-off - PART II12 minutes
Machine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART I6 minutes
Machine Learning Strategies for Business Improvement – An Overview (Healthcare, Banks, Industries) - PART II8 minutes
Preparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART I7 minutes
Preparing Data for Optimization in Production Manhours - Demo with EDA procedures - PART II15 minutes
Supervised Machine Learning Algorithm- Principle and types - PART I6 minutes
Supervised Machine Learning Algorithm- Principle and types - PART II10 minutes
Supervised Machine Learning Algorithm- Principle and types - PART III15 minutes
Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART I7 minutes
Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART II12 minutes
Regression Algorithm - Principle & Practicing exercise on Salary Prediction - PART III2 minutes
Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART I9 minutes
Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART II17 minutes
Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART III6 minutes
Classification algorithm-Decision tree algorithm for EV vehicle purchase - PART IV12 minutes
Implementation framework of ML algorithms – Lung Cancer Prediction - PART I6 minutes
Implementation framework of ML algorithms – Lung Cancer Prediction - PART II15 minutes
Future of COBOT – An application of ML in Oil & Gas industry - PART I3 minutes
Future of COBOT – An application of ML in Oil & Gas industry - PART II5 minutes

1 assignmentTotal 30 minutes

Assessment on Python Demos and Case-studies on Machine Learning(ML) Algorithm Fundamentals30 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 videosTotal 193 minutes

Principles of Unsupervised Machine Learning Algorithm - PART I7 minutes
Principles of Unsupervised Machine Learning Algorithm - PART II8 minutes
Clustering Algorithm - Principles with Hands-on approach using K-Means - Part I12 minutes
Clustering Algorithm - Principles with Hands-on approach using K-Means - Part II11 minutes
DBSCAN clustering algorithm - A hands on approach - PART I7 minutes
DBSCAN clustering algorithm - A hands on approach - PART II13 minutes
Dimensionality Reduction Algorithm – Principle & Implementation of PCA - PART I12 minutes
Dimensionality Reduction Algorithm – Principle & Implementation of PCA - PART II14 minutes
Linear Discriminant Analysis - A Quantitative Approach9 minutes
Autonomous vehicle embedded with Dimensionality Reduction Algorithm - PART I7 minutes
Autonomous vehicle embedded with Dimensionality Reduction Algorithm - PART II6 minutes
Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART I9 minutes
Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART II7 minutes
Reinforcement Machine Learning Algorithm – with a Practice Approach in HVAC System - PART III7 minutes
Model Based RL Algorithms – Principle and Example with DYNA Q Algorithm - PART I7 minutes
Model Based RL Algorithms – Principle and Example with DYNA Q Algorithm - PART II5 minutes
Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART I7 minutes
Paradigm shift in health care diagnosis with reinforcement learning - a review exercise - PART II12 minutes
Model Free Reinforcement learning – exploring policy based methods - PART I8 minutes
Model Free Reinforcement learning – exploring policy based methods - PART II10 minutes
Deployment of Deep Q-Learning in Pick and Place COBOT - An Industrial application of ML - PART I6 minutes
Deployment of Deep Q-Learning in Pick and Place COBOT - An Industrial application of ML - PART II6 minutes

1 assignmentTotal 30 minutes

Assessment on Demonstrating Unsupervised & Reinforcement Machine Learning Algorithms with Python demos30 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 videosTotal 199 minutes

Fundamentals of Neural Network - PART I8 minutes
Fundamentals of Neural Network - PART II6 minutes
Fundamentals of Neural Network - PART III8 minutes
Digit Recognition using MLP Model – Hands-on Practice - PART I5 minutes
Digit Recognition using MLP Model – Hands-on Practice - PART II2 minutes
Digit Recognition using MLP Model – Hands-on Practice - PART III12 minutes
Gradient Descent Algorithm - Working Principle - PART I6 minutes
Gradient Descent Algorithm- Working Principle - PART II7 minutes
Backpropagation Algorithm – Working Principle - PART I6 minutes
Backpropagation Algorithm – Working Principle - PART II15 minutes
Backpropagation Algorithm – Working Principle - PART III10 minutes
Cross-Entropy cost function and its implementation using MLP - PART I7 minutes
Cross-Entropy cost function and its implementation using MLP - PART II6 minutes
Overfitting and Regularization principles with a hands-on approach - PART I7 minutes
Overfitting and Regularization principles with a hands-on approach - PART II8 minutes
Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART I12 minutes
Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART II10 minutes
Digit Recognition System for Visually Impaired – CNN based ML Algorithm - PART III17 minutes
Stochastic Gradient Descent Algorithm Principle and Analysis using IRIS Dataset - PART I5 minutes
Stochastic Gradient Descent Algorithm Principle and Analysis using IRIS Dataset - PART II6 minutes
Simulation of Neural Networks - Weka tool based exercise - PART I7 minutes
Simulation of Neural Networks - Weka tool based exercise - PART II8 minutes
Simulation of Neural Networks - Weka tool based exercise - PART III10 minutes
Strategic deployment of shallow neural network for enhancing agriculture - a review exercise - PART I6 minutes
Strategic deployment of shallow neural network for enhancing agriculture - a review exercise - PART II7 minutes

1 assignmentTotal 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 videosTotal 164 minutes

Vanishing Gradient Principles And Its Measurement In Sigmoid Activation Function-PART I11 minutes
Vanishing Gradient Principles And Its Measurement In Sigmoid Activation Function-PART II10 minutes
Unstable Gradient in Complex networks - PART I4 minutes
Unstable Gradient in Complex networks - PART II9 minutes
Unstable Gradient in Complex networks - PART III10 minutes
A case study on application of DL for banana leaf disease prediction - PART I9 minutes
A case study on application of DL for banana leaf disease prediction - PART II8 minutes
Introduction to convolutional neural networks - PART I9 minutes
Introduction to convolutional neural networks - PART II5 minutes
Introduction to convolutional neural networks - PART III7 minutes
Image Recognition principles with a Case study approach in Retail Industry - PART I6 minutes
Image Recognition principles with a Case study approach in Retail Industry - PART II5 minutes
Applications of CNN13 minutes
Generative Network Principles - PART I11 minutes
Generative Network Principles - PART II7 minutes
Introduction to RNN9 minutes
Properties and Construction of RNN - PART I7 minutes
Properties and Construction of RNN - PART II7 minutes
Implementation of RNN - PART I3 minutes
Implementation of RNN - PART II15 minutes

1 assignmentTotal 30 minutes

Assessment on Advanced Applications with Deep Learning Networks30 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