Introduction to Embedded AI Development
( 25 Modules )

Module #1
Introduction to Embedded Systems
Overview of embedded systems, their components, and applications.

Module #2
Introduction to Artificial Intelligence (AI)
Basics of AI, machine learning, and deep learning.

Module #3
Embedded AI:Definition and Applications
Definition, advantages, and applications of embedded AI.

Module #4
Embedded AI Development Lifecycle
Overview of the development lifecycle of embedded AI systems.

Module #5
Hardware Platforms for Embedded AI
Introduction to popular hardware platforms for embedded AI development (e.g., Raspberry Pi, Arduino, etc.).

Module #6
Software Tools for Embedded AI
Overview of software tools for embedded AI development (e.g., TensorFlow Lite, OpenCV, etc.).

Module #7
Machine Learning Fundamentals
Introduction to machine learning concepts, algorithms, and techniques.

Module #8
Deep Learning Fundamentals
Introduction to deep learning concepts, algorithms, and techniques.

Module #9
Embedded AI Development frameworks
Introduction to popular embedded AI development frameworks (e.g., TensorFlow Lite, PyTorch Mobile, etc.).

Module #10
Model Optimization for Embedded AI
Techniques for optimizing AI models for deployment on embedded systems.

Module #11
Data Preparation for Embedded AI
Importance of data preparation for embedded AI development.

Module #12
Model Training for Embedded AI
Training AI models for deployment on embedded systems.

Module #13
Model Deployment on Embedded Systems
Deploying AI models on embedded systems.

Module #14
Power and Performance Optimization
Optimizing power consumption and performance of embedded AI systems.

Module #15
Memory and Storage Optimization
Optimizing memory and storage usage of embedded AI systems.

Module #16
Security Considerations for Embedded AI
Security considerations for embedded AI systems.

Module #17
Real-time Processing and Inference
Real-time processing and inference techniques for embedded AI systems.

Module #18
Edge AI and Distributed Intelligence
Introduction to edge AI and distributed intelligence concepts.

Module #19
Case Study:Image Classification on Embedded Systems
Hands-on case study on deploying image classification models on embedded systems.

Module #20
Case Study:Object Detection on Embedded Systems
Hands-on case study on deploying object detection models on embedded systems.

Module #21
Case Study:Natural Language Processing on Embedded Systems
Hands-on case study on deploying NLP models on embedded systems.

Module #22
Project Development and Implementation
Guided project development and implementation of embedded AI systems.

Module #23
Debugging and Troubleshooting Embedded AI Systems
Techniques for debugging and troubleshooting embedded AI systems.

Module #24
Best Practices for Embedded AI Development
Best practices for embedded AI development and deployment.

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Embedded AI Development career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Introduction to Embedded AI Development ( 25 Modules )

Introduction to Embedded AI Development
( 25 Modules )