Course Description

Hands-On Edge AI with Linux Devices is a beginner-friendly, practical course designed to introduce learners to the world of Artificial Intelligence (AI) and Machine Learning (ML) through hands-on projects on Linux-based devices. This course emphasizes applied learning over theory, enabling students, hobbyists, and aspiring engineers to explore, build, and deploy AI systems with confidence.
Through structured modules, learners gain exposure to AI fundamentals, neural networks, machine learning libraries, and real-world applications, starting from basic concepts and progressing to advanced AI projects. The program includes interactive coding exercises, mini-projects, and end-to-end capstone projects that reinforce understanding while building practical skills in computer vision, natural language processing, and speech technologies.
By the end of the course, learners will be able to build machine learning applications from scratch, apply Python and AI libraries, and implement real-world projects, preparing them for careers and innovation in the field of intelligent systems.

What you will learn

• Introduction to AI and ML fundamentals.
• Building perceptrons and neural networks from scratch.
• Working with major frameworks: Scikit-learn, TensorFlow, Keras, OpenCV.
  
• Image classification, object detection, and computer vision projects.
• Natural Language Processing (NLP) and chatbots.
• Generative AI concepts and projects.
• Practical edge AI real-time applications.

Course Objectives

• Provide a hands-on introduction to AI and ML for beginners.
• Teach learners to build neural networks, train AI models, and implement real-world applications.
  
• Introduce popular AI frameworks and tools in a structured, progressive manner.
• Develop practical skills to deploy AI on Linux-based edge devices.
• Encourage self-driven exploration beyond the classroom through applied projects.
  

Key Highlights

• Fully hands-on, project-based training.
• Beginner-friendly, no advanced math or prior AI experience required.
  
• Work with Linux-based devices for real-world AI applications.
• Learn Python programming and essential AI libraries.
• Build multiple mini projects and end-to-end capstone projects.
• Instructor-led guidance with assignments.
• Focus on practical understanding over theory-heavy lectures.
  

Learning Outcomes

By the end of this course, learners will be able to:

• Build neural networks from scratch and understand how they learn.
• Use Python and key AI libraries with confidence.
• Apply AI techniques to real-world problems in computer vision, NLP, and speech technologies.
• Work with frameworks such as TensorFlow, Keras, and OpenCV
• Deploy AI models on edge devices and integrate them with hardware peripherals
• Execute end-to-end AI projects, including object detection and chatbots.
  

Who Should Enroll

• Beginners in engineering, programming, or AI.
• Hobbyists and technology enthusiasts who want hands-on AI experience.
  

Who This Course Is For

This program is built for learners who are serious about gaining real, practical skills in edge AI and machine learning—not just completing a syllabus. The focus is on learning-by-doing, building confidence, and developing the ability to design and implement working machine learning applications.
This is not a passive classroom experience. Instead, students are expected to engage actively, solve problems, build prototypes, and apply the concepts taught. The instructor provides guidance, mentorship, and structured direction—but the actual learning happens through individual effort, experimentation, and persistence.
While a certificate is awarded upon completion, the true value of this course lies in the skills, mindset, and hands-on experience gained. Those who commit to the process will walk away with the confidence to build systems, troubleshoot issues, and contribute to real-world product development.

Prerequisites

To participate effectively in this course, learners must have a smartphone with at least 2.5GB of data available per day.
Additionally, learners should have a laptop with an Intel i3 or i5 processor, running either Windows or Linux, to write and run code, perform simulations, and complete assignments. If it has a GPU, the better. Finally, learners will need a USB storage ranging from 32GB to 1TB. The storage can be an SD card, SATA SSD or NVME with a suitable USB reader.

• Programming knowledge (basic to intermediate) (preferably Python) and familiarity with Linux or Colab.
  

Pricing and Enrollment

To begin your enrollment process, simply visit our Contact Page and submit your details. Please include a preferred time for us to reach you. Once received, our team will get in touch with you to share the course pricing, available batch schedules, and the next steps for enrollment.