AIoT: Artificial Intelligence of Things
Combine IoT data with machine learning: sensor data preparation, anomaly detection, forecasting basics and edge-inference concepts.
- Level
- Advanced
- Duration
- Confirmed at enquiry
- Mode
- Online across India; confirm batch
- Fee
- Enquire for current fee
About this course
AIoT combines connected sensing with machine learning so that systems can detect unusual behaviour, predict conditions or classify what they sense. The hard parts are rarely the algorithms: they are collecting representative data, cleaning it, choosing the right question and deploying a small model where it is actually useful.
This course builds an honest, practical pipeline: collect sensor data, explore and clean it, apply simple statistical and machine-learning methods and discuss where inference should run, on the cloud, a gateway or the device.
Who is this course for?
- Learners who completed complete IoT or Raspberry Pi courses
- Computer-science and data-oriented students entering IoT
- Engineers adding analytics to existing IoT systems
Eligibility
For learners with Python basics and prior IoT or data exposure. Exact eligibility is confirmed at enrollment.
Prerequisites recommended
Python basics, familiarity with sensor data and basic statistics. Completing the Complete IoT or Raspberry Pi course is recommended.
Syllabus: 6 modules
1AIoT concepts
- Where AI adds value in IoT and where it does not
- Cloud AI versus edge AI
- Data, models and deployment lifecycle
2Data collection and preparation
- Designing data collection for modelling
- Cleaning, resampling and labelling time series
- Train, validation and test splits for temporal data
3Statistical methods
- Thresholds, moving averages and z-scores
- Seasonality and baselines
- Evaluating false alarms and misses
4Machine learning basics
- Classification and regression with scikit-learn
- Anomaly detection (isolation forest, simple autoencoder concepts)
- Model evaluation and common pitfalls
5Edge inference
- Model size, quantisation and latency (concepts)
- Running a small model on Raspberry Pi
- TinyML overview and its limits
6Project
- Sensor anomaly detection pipeline
- Evaluation report with limitations
- Deployment design for edge or cloud
Tools and technologies covered
- Python
- pandas / NumPy
- scikit-learn
- Raspberry Pi
- MQTT
- Jupyter notebooks
- TinyML concepts
Practical exercises and project examples
- Sensor anomaly detection on temperature and vibration data
- Short-term forecast of a measured quantity
- Simple activity classification from accelerometer data
- Edge-versus-cloud inference comparison
These are example exercises and project concepts for this course, not a record of projects completed by past learners.
Outcomes, career directions and limits
Skills you should be able to demonstrate
- Prepare time-series sensor data for modelling
- Apply statistical and basic machine-learning methods to detect anomalies
- Evaluate models honestly, including false alarms
- Explain trade-offs between edge and cloud inference
Relevant career directions
- Data-oriented IoT roles after further practice
- Foundation for predictive-maintenance and analytics specialisation
- Research and advanced project work
What this course does not do
- This is not a general machine-learning course; deep learning theory and large-model training are out of scope.
- Useful models need representative data; results from small demo datasets do not transfer directly to production.
Career outcomes depend on your skills, portfolio, experience and employer requirements. No course can guarantee a job.
Delivery, batches and fees
Delivery and batch
Online training is available across India. Offline training is offered only at confirmed locations. Batch timings are confirmed by the training team.
Course fee
Enquire for Current Course Fee
Special fee benefits may be available on selected courses. Contact us to confirm the current fee, what it includes and any offer conditions.
Check Your Special Course FeeAIoT: Artificial Intelligence of Things: frequently asked questions
Do I need to know machine learning already?
No, but you should be comfortable with Python and basic statistics. The course introduces the machine-learning methods used.
Will I run AI on a microcontroller?
TinyML is introduced at a concept level. Hands-on inference typically runs on a Raspberry Pi or a laptop, with discussion of microcontroller deployment.
Is a GPU required?
No. The datasets and models in this course are small enough for ordinary laptops.
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Learners across India take this course online
City guides explain how online delivery works for learners in each city and which project themes fit.