L&T EduTech
AI Principles with Edge Computing
L&T EduTech

AI Principles with Edge Computing

This course is part of Intelligent Digital Factories Specialization

Taught in English

Some content may not be translated

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

20 hours (approximately)
Flexible schedule
Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

March 2024

Assessments

6 assignments

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

20 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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
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There are 6 modules in this course

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

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

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

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

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

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

What's included

17 videos1 assignment

Instructor

Subject Matter Expert
L&T EduTech
67 Courses25,478 learners

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L&T EduTech

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