Introduction to Embedded Machine Learning Training

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Introduction to Embedded Machine Learning Training is designed to give you an overall knowledge of embedded machine learning and technological advancements related to this. You can fulfil the requirements of your company with a plethora of tasks after you have earned this Introduction to Embedded Machine Learning Training. We, at Multisoft Systems, are backed by a team of specialized subject matter experts and deliver a hands-on course for Embedded Machine Learning Training.

Go for this Introduction to Embedded Machine Learning Training if you are curious about embedded machine learning. The course materials designed by our subject-matter experts is based on the fundamentals of embedded systems, basics of machine learning, and introduction to Tiny ML. It also includes the methods of using Embedded Machine Learning effectively. You will be able to pick your own classifications, audio and deploy a machine learning model yourself after you have completed this course.

Introduction to Embedded Machine Learning Objectives:
  • How to work with the Internet of Things?
  • What is Embedded Systems?
  • What is Machine Learning?
  • An introduction to TinyML
  • How to use Embedded Machine Learning?
Introduction to Embedded Machine Learning Online Training
  • Recorded Videos After Training
  • Digital Learning Material
  • Course Completion Certificate
  • 24x7 After Training Support
Target Audience
  • Students who are curious about embedded machine learning are eligible for this Introduction to Embedded Machine Learning Training.
Course Prerequisites
  • To pursue this Introduction to Embedded Machine Learning Training, you need to have access to Thunderboard™ Sense 2: IoT Development Kit.
Certification
  • Multisoft Systems will provide you with a training completion certificate after completing this Introduction to Embedded Machine Learning .

Module 1: Introduction 

Module 2: Fundamentals of Embedded Systems 

  • Microcontrollers
  • Microcontrollers
  • Debugging
  • Debugging
  • Threads

Module 3: Fundamentals of machine Learning 

  • ML Basics
  • ML Basics
  • Supervised Learning

Module 4: Fundamentals of Tiny ML

  • Tiny ML

Module 5: Embedded ML Project 

  • Edge Impulse
  • Create Account
  • Connect Device
  • Connect Device - Phone View
  • Collect Data - Phone View
  • Observe Data
  • Generate Machine Learning Model
  • Retrain Model
  • Test Model - Phone View
  • Next Steps

Module 6: Conclusion 

  • How to become an Embedded ML Engineer
     

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