Live online learning

Your teaching team

Build understanding through live sessions, code review, hardware practice and project discussions.

Our teaching team

Learn with people who build

IoT, automotive embedded systems and AI expertise, brought together through live online teaching, hardware projects and practical feedback.

Rohit Mundke

Founder & IoT Mentor

An industry-focused IoT expert who connects embedded hardware, firmware, communication protocols, and cloud applications to real-world connected solutions.

Internet of Things (IoT)
sensor and actuator integration
microcontroller-based connected systems
ESP32 and Arduino ecosystems
MQTT and HTTP communication
device-to-cloud integration

Teaching approach & profile

Rohit Mundke brings a practical, engineering-led approach to IoT learning, helping students understand how connected products work beyond the block diagram. His sessions connect sensors and microcontrollers with device communication, data flow, cloud integration, and application-level outcomes. Learners build a stronger understanding of the complete IoT lifecycle—from interfacing and firmware logic to connectivity, debugging, and working prototypes. His mentoring emphasizes sound fundamentals, systematic troubleshooting, and project execution so learners can approach IoT challenges with confidence and an engineer’s mindset.

Teaching approach

Understand the system end to end, build it in stages, test assumptions, and debug methodically. The goal is not just to make a demo work, but to understand why it works.

Who this supports

Engineering students, aspiring IoT developers, embedded learners, career switchers, and working professionals building connected-device skills.

Rohit on LinkedIn

Avi Singh

IoT & Connected Systems Mentor

An industry-focused IoT expert who helps learners turn connected-device concepts into practical prototypes through structured engineering and hands-on development.

IoT system architecture
embedded connectivity
development boards
sensor interfacing
MQTT and HTTP
device-to-cloud data flow

Teaching approach & profile

Avi Singh focuses on the engineering that brings connected devices to life: selecting and interfacing hardware, structuring device logic, establishing reliable communication, moving data between devices and applications, and validating the complete solution. His learning approach encourages students to think in terms of system architecture rather than isolated components. Through guided implementation and practical troubleshooting, learners develop a clearer understanding of connectivity, protocol choices, data handling, and prototype validation. The emphasis is on building useful, testable IoT solutions while developing the discipline required to diagnose issues across hardware, firmware, and communication layers.

Teaching approach

Build with purpose: understand the architecture, validate each layer, and use testing and debugging to turn a prototype into a dependable solution.

Who this supports

Learners interested in IoT prototyping, connected products, embedded connectivity, practical engineering projects, and IoT-oriented technical roles.

Shubham Pande

Automotive Embedded Systems Mentor

4+ years of industry experience

An industry-focused automotive expert who helps learners understand the embedded software, in-vehicle communication, diagnostics, and validation concepts behind modern vehicle electronics.

Automotive embedded systems
ECU fundamentals
CAN and CAN FD
UDS diagnostics fundamentals
embedded C concepts
automotive communication

Teaching approach & profile

Shubham Pande brings an automotive engineering perspective to embedded systems, helping learners connect core firmware knowledge with the workflows used in vehicle electronics. His sessions explore electronic control unit (ECU) fundamentals, in-vehicle communication, diagnostic concepts, and systematic testing and validation. Learners are introduced to the purpose of protocols such as CAN and CAN FD, the fundamentals of UDS diagnostics, and the role of structured engineering practices in automotive development. The focus is on building technical clarity, protocol-level understanding, and a disciplined approach to debugging and validation—foundations that support further growth in automotive embedded engineering.

Teaching approach

Learn the underlying system and communication flow first, then apply structured testing and debugging to understand behavior and validate results.

Who this supports

Electronics and electrical engineering students, embedded developers, automotive software aspirants, and professionals exploring vehicle electronics.

Rajveer Sharma

AI & Machine Learning Mentor

4+ years of industry experience

An industry-focused AI expert who makes AI and machine learning practical through clear concepts, disciplined experimentation, and application-led project work.

Artificial intelligence fundamentals
Python for AI
data preparation
machine learning
model training and evaluation
applied AI projects

Teaching approach & profile

Rajveer Sharma helps learners move from understanding AI terminology to developing a structured approach to solving problems with data and models. His teaching connects Python foundations with data preparation, machine-learning workflows, model evaluation, and practical implementation. Learners are encouraged to understand not only how to train a model, but also how to assess its performance, recognize limitations, and communicate results responsibly. Where relevant, the learning path introduces Edge AI and TinyML concepts, showing how intelligence can be brought closer to devices and embedded applications. The emphasis is on strong fundamentals, hands-on experimentation, and the ability to explain the reasoning behind technical decisions.

Teaching approach

Start with the problem, understand the data, test the model carefully, and evaluate results honestly. Practical AI begins with sound reasoning—not just running code.

Who this supports

Engineering students, Python learners, aspiring AI/ML practitioners, career switchers, and professionals exploring AI applications.

Learn by building

Connect concepts to firmware, boards and working projects.

Debug together

Investigate failures, explain the cause and improve the design.

Receive feedback

Review code and project evidence at agreed course milestones.

Next step

Find the right course and cohort

Speak with admissions about your goals, trainer assignment and online timetable.

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