Machine Learning & IoT Training
Connected sensors now generate continuous streams of data from machines, buildings, farms and vehicles, but that data only pays off when models turn it into timely decisions. You begin with the IoT stack, from devices and gateways to cloud platforms, and where machine learning fits at the edge or in the cloud. Sessions on sensors, communication protocols such as MQTT and data acquisition show how readings are captured and transmitted reliably. Time-series data engineering covers cleaning, resampling, windowing and feature design for signals like vibration, temperature and energy consumption.
Modelling days build and evaluate regression and classification models, then apply deep learning for sequences, including recurrent networks, and fuse data from multiple sensors. Later sessions address anomaly detection, remaining useful life estimation and predictive maintenance, along with deploying models to constrained devices. Guided labs use sample sensor data sets throughout. Data scientists, IoT engineers, embedded developers, maintenance and reliability engineers and digital transformation teams in manufacturing, utilities, agriculture and logistics will find it relevant. You finish able to design an end-to-end pipeline that detects faults before they cause downtime.
About this course
Machine Learning & IoT Training addresses the real challenges professionals face in this domain through practical techniques and frameworks.
Participants leave with tools, templates, and real-world strategies they can apply immediately in their work.
The Machine Learning & IoT Training is designed for professionals who need practical, current command of machine learning & iot and the ability to translate it into measurable organizational results. Because organizations now operate under tighter resource, compliance and reporting pressures, specialist capability in this area has moved from desirable to essential. This certified programme combines proven methodology with hands-on application, covering edge AI and tinyml deployment, from pilot to scaled capability and the analytical techniques that underpin sound professional judgement.
Throughout the programme, participants work through realistic scenarios drawn from technology organizations, digital transformation programmes, data-driven enterprises, and public sector IT environments, examining how machine learning & iot is applied in practice and where it most often breaks down. Delegates will develop practical skill in designing appropriate frameworks, selecting and applying the right tools for edge AI and tinyml deployment, interpreting findings, and communicating conclusions clearly to colleagues, management and external stakeholders. Sessions move deliberately from principles to application, so that each technique introduced is practised against a realistic problem before the programme moves on — strengthening both individual effectiveness and the wider capability of the organizations delegates return to.
Training Objectives
By the end of this course, participants will be able to:
- Develop robust machine learning & iot frameworks that support successful implementation and measurable outcomes.
- Analyze performance using recognized evaluation methods drawn from edge AI and tinyml deployment.
- Design practical systems that strengthen accountability, transparency and evidence generation.
- Evaluate outcomes and assess longer-term impact across a range of operating contexts.
- Apply relevant tools, frameworks and analytical methodologies confidently and appropriately.
- Integrate from pilot to scaled capability into existing organizational processes and reporting structures.
- Strengthen decision-making and operational effectiveness through evidence-based professional practice.
What you'll learn
Key Takeaways
By the end of the Machine Learning & IoT Training, participants will leave with:
- A comprehensive working understanding of machine learning & iot principles and performance-measurement methodology.
- Enhanced ability to design practical systems and evaluate outcomes reliably.
- Improved competency in edge AI and tinyml deployment, reporting structures and analytical interpretation.
- Practical techniques for translating findings into strategic insight and clear recommendations.
- Greater confidence in applying machine learning & iot and assessing measurable results.
- Stronger capability to support organizational learning and continuous performance improvement.
- Improved approaches to strengthening sustainability, accountability and operational effectiveness.
Training Methodology
This training course adopts a practical, participant-centered learning approach that builds machine learning & iot expertise, strengthens analytical capability and develops confident professional judgement. The methodology combines specialist facilitation, applied hands-on experience and globally recognized professional practice, so that delegates return able to apply what they have learned immediately rather than in theory only.
Methodology includes:
- Expert-led sessions facilitated by experienced machine learning & iot practitioners and subject-matter specialists.
- Practical application of the frameworks, tools and techniques used in edge AI and tinyml deployment.
- Real-world case studies drawn from technology organizations, digital transformation programmes, data-driven enterprises, and public sector IT environments.
- Tool-based demonstrations of the systems, templates and reporting applications used in current practice.
- Interactive discussion and scenario-based learning focused on realistic challenges and decision points.
- Structured group exercises with facilitator feedback, reinforcing learning through practice rather than lecture alone.
Day 1: Foundations of Machine Learning and the IoT Stack
- Supervised, unsupervised and reinforcement learning concepts mapped to IoT use cases
- Anatomy of an IoT system: sensors, gateways, connectivity, platform and application layers
- Where intelligence belongs: device, edge, fog or cloud
- Python, NumPy and scikit-learn environment setup for the practical labs
- Reference architectures for predictive maintenance, smart metering and asset tracking
- Roles, responsibilities and stakeholder coordination in foundations of machine learning and the iot stack
- Common challenges, risks and failure points in foundations of machine learning and the iot stack
Day 2: Sensors, Protocols and Data Acquisition
- Sensor types, sampling rates, calibration and measurement error
- MQTT, CoAP, HTTP and LoRaWAN compared for bandwidth and power constraints
- Device provisioning, identity and message payload design
- Building an ingestion pipeline from simulated devices to a broker
- Handling intermittent connectivity, buffering and store-and-forward
- Core principles and terminology underpinning sensors, protocols and data acquisition
- Standards, frameworks and good practice governing sensors, protocols and data acquisition
Day 3: Time-Series Data Engineering and Feature Design
- Cleaning sensor streams: missing values, drift, outliers and resampling
- Windowing, lag features, rolling statistics and spectral features
- Signal processing basics: filtering, FFT and denoising for vibration and audio
- Time-series storage options and retention strategies
- Labelling telemetry for supervised learning from maintenance records
- Data, evidence and information requirements for time-series data engineering and feature design
- Decision points and escalation routes in time-series data engineering and feature design
Day 4: Building and Evaluating Predictive Models
- Regression and classification on sensor datasets with scikit-learn
- Tree ensembles and gradient boosting for tabular telemetry
- Train/validation splits that respect temporal ordering
- Metrics for imbalanced failure data: precision, recall, F1 and PR curves
- Hyperparameter tuning and guarding against leakage in time-series data
- Measuring performance and assuring quality in building and evaluating predictive models
- Worked example: applying building and evaluating predictive models in a live machine learning & iot context
Day 5: Deep Learning for Sequences and Sensor Fusion
- Neural network fundamentals with TensorFlow and Keras
- CNNs for spectrograms and 1D convolutions over raw signals
- LSTM and GRU models for multivariate sequence forecasting
- Fusing accelerometer, temperature and current data into a single model
- Transfer learning to reduce labelled-data requirements
- Practical exercise and facilitated review
- Core principles and terminology underpinning deep learning for sequences and sensor fusion
Day 6: Anomaly Detection and Predictive Maintenance
- Unsupervised detection with isolation forests, one-class SVM and autoencoders
- Threshold setting, alarm fatigue and false-positive cost analysis
- Remaining useful life estimation and degradation modelling
- Condition monitoring workflow from alert to work order
- Case build: rotating equipment failure prediction from vibration data
- Regulatory, ethical and compliance dimensions of anomaly detection and predictive maintenance
- Technology and automation supporting anomaly detection and predictive maintenance
Day 7: Edge AI and TinyML Deployment
- Model compression: quantisation, pruning and knowledge distillation
- TensorFlow Lite and TensorFlow Lite for Microcontrollers workflows
- Latency, memory and power budgeting on constrained devices
- Deploying an inference model to a gateway or microcontroller target
- Trade-offs between on-device inference and cloud round trips
- Documentation, record-keeping and reporting requirements
- Measuring performance and assuring quality in edge AI and tinyml deployment
Day 8: IoT Platforms, MLOps and Fleet Operations
- Cloud IoT platform services for device registry, rules and telemetry routing
- Containerised model serving and streaming inference pipelines
- Over-the-air model and firmware updates with rollback safeguards
- Monitoring data drift, concept drift and model decay across a device fleet
- Retraining triggers, versioning and reproducible experiment tracking
- Practical tools, templates and techniques for iot platforms, mlops and fleet operations
- Roles, responsibilities and stakeholder coordination in iot platforms, mlops and fleet operations
Day 9: Security, Privacy and Governance for Connected Intelligence
- Threat modelling for device, network and model attack surfaces
- Secure boot, key management, certificates and encrypted transport
- Adversarial inputs, model extraction and data poisoning defences
- Edge preprocessing, anonymisation and federated learning for data minimisation
- Governance, audit trails and responsible use of sensor-derived inference
- Resourcing, cost and capacity considerations
- Regulatory, ethical and compliance dimensions of security, privacy and governance for connected intelligence
Day 10: From Pilot to Scaled Capability
- Building the business case: cost per device, downtime avoided and payback
- KPIs and dashboards linking model accuracy to operational outcomes
- Scaling from proof of concept to multi-site deployment
- Team structure, skills and tooling needed to sustain ML and IoT in-house
- Capstone: design, present and critique an end-to-end connected intelligence solution
- Planning and sequencing from pilot to scaled capability within the wider workflow
- Data, evidence and information requirements for from pilot to scaled capability
Machine Learning & IoT Training addresses engineers and data practitioners who connect instrumented devices to predictive models and need both sides to work together in production.
- Machine Learning Engineers and Data Scientists moving models onto edge and embedded targets
- IoT Solution Architects designing sensor, gateway and cloud ingestion layers
- Embedded Systems and Firmware Engineers adding inference to constrained hardware
- Data Engineers building streaming pipelines for telemetry and time-series data
- Industrial Automation and SCADA Engineers applying predictive maintenance to plant assets
- Cloud and Platform Engineers operating device fleets and MLOps tooling
- Technology Managers accountable for connected-product strategy and performance measurement
Participants who complete all ten days receive a Certificate of Completion from Vision Reach Global Consultancy, confirming their ability to design machine learning models for IoT telemetry, deploy inference at the edge, and measure the performance and business impact of connected solutions.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Our training team can help with group pricing, invoicing, or picking the right schedule.
Choose Where You Want to Learn
Machine Learning & IoT Training Venues
KenyaMombasaNext Nov 2· 13 dates$3,200- KenyaNaivashaNext Nov 9· 13 dates$3,000
- KenyaNairobiNext Nov 16· 13 dates$3,000
- KenyaKisumuNext Nov 2· 13 dates$3,000
- KenyaMachakosNext Nov 9· 13 dates$3,200
UgandaKampalaNext Nov 9· 13 dates$3,800
RwandaKigaliNext Nov 2· 13 dates$3,800
South AfricaCape TownNext Nov 2· 13 dates$7,600
TanzaniaZanzibarNext Nov 23· 13 dates$5,200
UAEDubaiNext Nov 2· 13 dates$7,600- South AfricaPretoriaNext Nov 9· 13 dates$6,400
- South AfricaJohannesburgNext Nov 9· 13 dates$6,800
- TanzaniaDodomaNext Nov 2· 13 dates$3,600
- TanzaniaDar es SalaamNext Nov 2· 13 dates$4,000
- TanzaniaArushaNext Nov 16· 13 dates$3,800
NigeriaAbujaNext Nov 16· 13 dates$5,600- NigeriaLagosNext Nov 23· 13 dates$5,600
GhanaAccraNext Nov 23· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Machine Learning & IoT Training
Everything you need to know about this course before you register.
By the end of the Machine Learning & IoT Training programme, you'll be able to understand fundamental concepts and frameworks, apply best practices and methodologies, solve real-world challenges, and build capability in the organization. The full breakdown of topics is covered session by session in the Course Outline tab above.
Machine Learning & IoT Training addresses engineers and data practitioners who connect instrumented devices to predictive models and need both sides to work together in production. Typical delegates include Machine Learning Engineers and Data Scientists moving models onto edge and embedded targets, IoT Solution Architects designing sensor, gateway and cloud ingestion layers, Embedded Systems and Firmware Engineers adding inference to constrained hardware, Data Engineers building streaming pipelines for telemetry and time-series data, Industrial Automation and SCADA Engineers applying predictive maintenance to plant assets, Cloud and Platform Engineers operating device fleets and MLOps tooling, and Technology Managers accountable for connected-product strategy and performance measurement.
Machine Learning & IoT Training typically runs as 10 Days. It's available as in-person classroom training — every course can also be delivered on-site for your team on dates that suit you.
Machine Learning & IoT Training is scheduled in-classroom in Mombasa, Kenya, Naivasha, Kenya, Nairobi, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Machine Learning & IoT Training starts November 23, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Machine Learning & IoT Training from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Machine Learning & IoT Training is pitched at intermediate to advanced professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Machine Learning & IoT Training vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Machine Learning & IoT Training can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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Training by Country
Machine Learning & IoT Training by Country
- Machine Learning & IoT Training in GhanaGHClassroom in Accra
- Machine Learning & IoT Training in KenyaKEClassroom in Mombasa, Naivasha, Nairobi, Kisumu, Machakos
- Machine Learning & IoT Training in NigeriaNGClassroom in Abuja, Lagos
- Machine Learning & IoT Training in RwandaRWClassroom in Kigali
- Machine Learning & IoT Training in South AfricaZAClassroom in Cape Town, Pretoria, Johannesburg
- Machine Learning & IoT Training in TanzaniaTZClassroom in Zanzibar, Dodoma, Dar es Salaam, Arusha
- Machine Learning & IoT Training in UgandaUGClassroom in Kampala
- Machine Learning & IoT Training in the UAEAEClassroom in Dubai
- Live Online Machine Learning & IoT TrainingInstructor-led, joined from anywhere
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