IoT with Machine Learning

Workshop Curriculum

Your Learning Journey

Eight modules progressing from IoT fundamentals and hardware setup through data collection, analytics, ML model training, and edge AI deployment on real devices.

Welcome & Workshop IntroductionAn orientation to the workshop structure, learning objectives, and an overview of what participants will achieve — including hardware labs, ML model training, and end-to-end edge deployment projects.
Basics of IoTDefinition and scope of IoT, IoT architecture and its key components, and a broad survey of IoT applications across industries — from smart homes and industrial automation to healthcare and precision agriculture.
Hardware Platforms for IoTOverview of popular IoT hardware including Arduino, Raspberry Pi, ESP8266, and ESP32 — their capabilities, trade-offs, and selection criteria for different types of IoT projects and deployment environments.

Getting Started with Arduino / Raspberry PiInstalling necessary software and drivers, setting up development environments, and hands-on exercises — blinking an LED and reading live sensor data — to build a confident foundation with the hardware platform.
Introduction to Sensors and ActuatorsTypes of sensors — temperature, humidity, motion, and more — and actuators including motors and relays. Hands-on exercise: connecting sensors to the hardware board and reading live data from the physical world.

Introduction to Networking ConceptsCore networking concepts relevant to IoT — IP addressing, ports, protocols, and how embedded devices communicate over local and wide-area networks in real production deployment environments.
Connecting IoT Devices to the InternetPractical connectivity strategies — Wi-Fi and low-power networks — and the key IoT communication protocols: MQTT for lightweight pub/sub messaging, HTTP for REST APIs, and CoAP for constrained environments.
Hands-On: Sending Sensor Data to the CloudEnd-to-end lab exercise — participants connect hardware sensors, transmit live data over the network using MQTT, and verify successful ingestion into a cloud platform in real time.

Data Collection Techniques in IoTTechniques for reliable, continuous data collection from IoT devices — polling vs. event-driven approaches, data batching, transmission protocols (MQTT, HTTP, CoAP), and handling connectivity in distributed deployments.
Cloud Storage SolutionsCloud storage options for IoT data on AWS IoT Core, Azure IoT Hub, and Google Cloud IoT — how to route, store, and query large volumes of time-series sensor data at scale efficiently and cost-effectively.
Local Storage SolutionsLocal storage strategies for edge environments — time-series databases, SQLite, and flat-file storage — and when to choose local versus cloud storage based on latency, cost, and connectivity constraints.

Introduction to Data AnalyticsFundamentals of data analytics and why robust analysis is the foundation of any intelligent IoT system — understanding descriptive, diagnostic, predictive, and prescriptive analytics applied to real sensor data streams.
Data PreprocessingHands-on data cleaning and transformation for IoT datasets — handling missing values, noise filtering, normalization, feature extraction, and structuring time-series data for machine learning pipelines.
Basic Visualization TechniquesHands-on visualization of IoT sensor data using Matplotlib, Plotly, and Grafana — building real-time dashboards and time-series charts to surface patterns, anomalies, and trends in live device data.

Overview of Machine LearningA practical introduction to machine learning — supervised, unsupervised, and reinforcement learning paradigms — and the core ML workflow from data ingestion through model training, evaluation, and deployment.
Machine Learning for IoTReal-world ML use cases in IoT — predictive maintenance, anomaly detection, gesture recognition, and energy optimization — and the end-to-end ML workflow specific to IoT: data collection, training, and on-device deployment.

Building a Simple ML ModelHands-on model training — choosing a dataset, splitting into training and testing sets, and training a simple ML model using Linear Regression and Decision Tree classifiers on real IoT sensor data.
Introduction to Edge ComputingWhat edge computing is and why it matters for IoT — processing data close to the source reduces latency, bandwidth costs, and privacy exposure, enabling real-time intelligent decisions on constrained hardware.
Tools for ML Deployment on IoTHands-on with TensorFlow Lite for converting and optimizing trained models for microcontrollers, and Edge Impulse for end-to-end embedded ML — from data collection through model training to device deployment.
Hands-On: Deploying a Trained Model on an IoT DeviceFull end-to-end lab — participants take a trained ML model, convert it with TensorFlow Lite, flash it to a microcontroller, and run live inference on sensor data directly on the device without any cloud dependency.

Advanced IoT ArchitecturesFog computing and edge computing architectures — how distributing intelligence across the device-to-cloud continuum enables scalable, resilient, and low-latency IoT systems for demanding industrial use cases.
Emerging Trends in IoT and MLA forward-looking view of AIoT (Artificial Intelligence of Things) and TinyML — running sophisticated ML models on ultra-low-power microcontrollers — and how these technologies will define the next decade of connected intelligent devices.
Q&A and Wrap-UpOpen Q&A with instructors — addressing specific project questions, career guidance for IoT and ML roles, certification pathways, and a structured review of all key concepts covered across the full workshop.

Hands-On Learning

Learn by Doing, Not Just Listening

Every concept is reinforced through live hardware demonstrations, guided labs, and real end-to-end project exercises in a fully equipped environment.

Hardware Lab

Wire up sensors, read live temperature and motion data, and transmit it to the cloud over MQTT

ML Training Lab

Train a Decision Tree classifier on real IoT sensor data — from raw data to an evaluated model

Edge Deployment

Convert a trained model with TensorFlow Lite and run live inference directly on a microcontroller

Real Hardware LabsWork with actual Arduino and Raspberry Pi boards — wiring sensors, writing embedded code, and transmitting data to cloud platforms in real time throughout the workshop.

End-to-End ML PipelineBuild a complete machine learning pipeline from scratch — data collection, preprocessing, model training, evaluation, and optimization — entirely using IoT-sourced real sensor data.

Live Edge DeploymentDeploy trained ML models directly onto microcontrollers using TensorFlow Lite and Edge Impulse — running intelligent inference on embedded hardware with no cloud dependency required.

Expert Q&A SessionsOpen discussions with IoT and ML practitioners throughout the workshop — addressing your questions and providing guidance on career paths, tools, and real-world project challenges.

Application Domains

Where IoT + ML Changes Everything

Intelligent connected devices are transforming every major sector. This workshop equips you to build solutions across all of them.

Industrial IoT & Predictive Maintenance

ML models running on edge devices detect equipment anomalies before failures occur — reducing downtime, cutting maintenance costs, and extending asset lifetimes in manufacturing and utilities environments.

Healthcare & Wearables

Smart wearables with on-device ML models continuously monitor vital signs, detect irregular patterns, and alert clinicians in real time — enabling proactive care outside of traditional clinical settings.

Smart Agriculture

Sensor networks combined with ML models optimize irrigation, predict crop yields, and detect plant disease early — transforming food production efficiency and resource management at scale.

Smart Cities & Infrastructure

Intelligent traffic management, energy optimization, waste monitoring, and environmental sensing — IoT and ML work together to make urban infrastructure more efficient, sustainable, and responsive.

Smart Home & Consumer IoT

Voice recognition, occupancy prediction, energy management, and personalized automation — consumer IoT devices rely heavily on on-device ML to deliver intelligent, responsive user experiences.

Autonomous Vehicles & Robotics

Edge AI in vehicles and robotic systems processes sensor data in real time for navigation, obstacle avoidance, and decision-making — where latency tolerances are measured in milliseconds.

Who Should Attend

This Workshop Is For You If…

Whether you're a curious beginner, an embedded developer, or a data scientist exploring hardware — this workshop delivers practical value at every level.

Students & Freshers

Build a strong IoT and ML foundation with hands-on hardware experience, real projects for your portfolio, and a recognized certificate before entering the rapidly growing embedded AI job market.

Software Developers

Extend your skills into the physical world — learn hardware interfacing, embedded programming, and on-device ML deployment to build the next generation of intelligent connected applications.

Electronics Engineers

Add machine learning and data engineering to your embedded systems toolkit — learn to make your hardware projects intelligent using ML models trained directly on your own sensor data.

Data Scientists

Bridge the gap between data science and embedded hardware — learn to deploy your models on microcontrollers, understand hardware constraints, and build true end-to-end edge AI systems.

Workshop Benefits

Why Attend This Workshop?

An immersive, project-driven program that gives you real hardware experience, ML skills, and the portfolio projects to stand out in the IoT and edge AI job market.

Complete Coverage

Eight modules spanning the full IoT and ML stack — from wiring your first sensor through training ML models and deploying them on microcontrollers with TensorFlow Lite and Edge Impulse.

Real Hardware Experience

Hands-on labs with actual Arduino, Raspberry Pi, and ESP32 hardware — building physical systems that sense, communicate, and make intelligent decisions directly on the device itself.

Expert Guidance

Practitioners with real IoT and ML deployment experience guide every session — sharing practical insights from production projects and helping you solve challenges on actual hardware in real time.

Portfolio-Ready Projects

Leave with a real end-to-end project — sensor data collection, ML model training, and edge deployment — that demonstrates concrete skills to employers and clients in IoT and AI roles.

Why IoT & ML?

What You'll Learn