2.5-day workshop · No coding or model building
Machine Learning for Non-Technical Teams
Learn enough machine-learning language to sponsor the right problem, challenge a metric and work productively with data-science teams—without pretending a workshop turns managers into model builders.
- Duration
- 2.5 days · 20 hours
- Audience
- Product, business and functional leaders
- Focus
- Models, metrics and project decisions
- Coding
- None—the course does not build models
What is the Machine Learning for Non-Technical Teams course?
Machine Learning for Non-Technical Teams is a 20-hour programme for product managers, business analysts, functional leaders and project sponsors. It explains classification, regression, clustering and recommendation systems through decisions and examples, then teaches participants to interpret metrics, recognise leakage and drift, write an ML project brief and evaluate whether a proposed system is useful in context. It does not teach coding or model development.
Is this the right course for you or your team?
Who it is for
- Product managers working with data-science teams
- Business analysts moving into analytics or AI project roles
- Functional leaders sponsoring predictive systems
- Procurement, risk or consulting teams reviewing ML proposals
Situations it fits
- A project begins with an algorithm rather than a business decision
- Stakeholders ask only for accuracy
- Teams cannot explain false-positive and false-negative costs
- A vendor proposal offers a model without a monitoring or fallback plan
Prerequisites
- Comfort reading tables and charts
- No programming or advanced statistics required
- Bring a real or representative ML proposal to discuss if available
When a different course fits better
This course teaches understanding and sponsorship, not implementation.
- Data Science & ML Trainingfor participants who need to build models in Python
- Generative AI Fundamentalsfor broader AI literacy focused on LLMs and generative tools
The business problems this course addresses
A non-technical sponsor still makes technical consequences through framing, metric and rollout decisions.
| Task | The usual problem | What participants practise |
|---|---|---|
| Selecting the problem | The target is vague or cannot be measured reliably. | Write the decision, prediction target, user and consequence before discussing models. |
| Reading performance | A high aggregate accuracy hides expensive failure cases. | Compare precision, recall and error costs against a baseline and threshold. |
| Reviewing data | The dataset does not represent the population or future setting. | Question source, labels, leakage, missing groups and how conditions may change. |
| Planning deployment | Ownership ends when the model is delivered. | Define monitoring, feedback, override, escalation and retirement responsibilities. |
What participants will be able to do
Practise the skills below through course exercises, then check the results and explain how you would apply them to your work.
Distinguish common ML problems
Recognise classification, regression, clustering and recommendation patterns without equations.
Read model evaluation
Interpret confusion matrices, precision, recall, thresholds, baselines and business error costs.
Challenge the data
Ask about representation, labels, leakage, drift and whether the target matches the decision.
Write a usable project brief
Specify user, decision, target, data, constraints, metric and acceptance boundary.
Review build-versus-buy
Compare vendor and internal options using evidence, integration, ownership and monitoring questions.
Plan human oversight
Define review, override, escalation and feedback for consequential model outputs.
Applied to each team’s real work
Product managers
Problem framing, success measures, user workflow and model-feature decisions.
Functional sponsors
Business fit, error costs, adoption and operating ownership.
Business analysts
Data requirements, evaluation interpretation and project translation.
Risk and procurement
Vendor claims, documentation, monitoring and accountability questions.
The ML decisions non-technical teams still own
The programme uses visual examples and model reports rather than coding notebooks.
Problem and target
Core sponsor decision
Define the real decision, prediction or grouping task before selecting technology.
Covers
- User and decision
- Target definition
- Baseline and constraints
What to verify
Would a project team know exactly what useful output means?
Metrics and thresholds
Core evaluation skill
Connect model errors to operational or customer consequences.
Covers
- Confusion matrix
- Precision and recall
- Threshold trade-offs
What to verify
Does the chosen metric reflect the more costly failure?
Lifecycle ownership
Core operating skill
Plan review, monitoring and response after launch.
Covers
- Drift and feedback
- Human override
- Incident and retirement path
What to verify
Who acts when performance or context changes?
2.5 days · 20 live hours
Detailed curriculum
4 modules, each pairing short instruction with guided practice. Sequence and examples can be adjusted after the training-needs discussion; the core outcomes stay the same.
Module 15 hours ML concepts without the mathematics
- Supervised and unsupervised learning
- Classification, regression and clustering
- Recommendations and when ML is unnecessary
Practical activityMatch several business problems to a suitable approach—or reject ML altogether.
Module 27 hours Reading results and failure modes
- Baselines, confusion matrices and thresholds
- Precision, recall and business cost
- Overfitting, leakage, drift and explainability
Practical activityCompare model reports and recommend a threshold with explicit trade-offs.
Module 35 hours Leading an ML project
- Project briefs and data requirements
- Build, buy and vendor evaluation
- Deployment, monitoring and stakeholder expectations
Practical activityReview a proposal and identify missing ownership or evidence.
Module 43 hours Project brief capstone
- Decision and target
- Metric and acceptance criteria
- Oversight, monitoring and fallback
Practical activityPresent an ML project brief and defend the metric and operating plan.
Need a learning plan around your goals?
Share your experience, stack and goals. The proposal sets out the suitable modules.
Exercises and practical outputs
Participants learn through visual model reports, business cases and sponsor decisions.
How every module runs
Frame
Define the user, decision and target.
Interpret
Read model evidence and error trade-offs.
Challenge
Locate weak data, unsupported claims and missing ownership.
Brief
Write a project brief a technical team can evaluate.
Exercises use representative, non-sensitive examples. Outputs are produced during the workshop for practice and review; anything participants keep is their own working material.
Useful AI requires ownership, evidence and boundaries
Participants learn to treat risk and review as part of the workflow, not as a disclaimer added after a pilot is already live.
NIST AI RMF is used as a voluntary risk-management reference, not as legal advice or a substitute for the organisation’s legal, privacy, security and compliance review.
Define the use and owner
State the intended task, affected users, accountable owner and decisions the system may or may not influence.
Protect information
Use only approved data and accounts. Apply the organisation’s confidentiality, retention and access rules before experimentation.
Measure in context
Choose quality, risk and adoption measures that reflect the real task; document what is not or cannot yet be measured.
Review and improve
Monitor failures, user feedback and changed conditions, then update controls or stop the use case when necessary.
How the course is tailored and delivered
Private delivery is shaped around the organisation’s maturity, approved tools, decision rights, workforce roles and current initiatives.
From first conversation to evaluation
Clarify the decision
Agree what participants must decide, design or implement after the programme.
Map the current state
Review roles, tools, policies, data constraints and existing AI or automation activity.
Select the cases
Choose realistic examples that are useful without exposing restricted information.
Define the handover
Confirm the artefact, owner, review path and next checkpoint expected after training.
Delivery formats
Public live-online cohort
Best for: Individuals following a published schedule with a mixed professional cohort
Live Zoom sessions with notes, assignments, lab instructions and three-month recording access through the Technovids LMS.
Private corporate programme
Best for: Teams that want organisation-specific cases and a shared working output
Delivered live online or onsite. Scope, cohort size, materials, support and commercials are confirmed in writing.
Delivery modes
Live online
Facilitated sessions with breakout work, shared artefacts and live review.
Onsite
Private corporate delivery at the client location, with the same structured exercises and review.
Discussion, practice and decisions—not a lecture marathon
Technovids training sessions in different group settings. The photographs do not attribute a particular client, course or outcome.
Photo 1 of 10: Hands-on session 
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Photo 5 of 10: Classroom session 
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Photo 7 of 10: Computer-lab session 
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Photo 9 of 10: Classroom session 
Photo 10 of 10: Group photo
Course details at a glance
The published course parameters on one page. Final scope, dates and fees are confirmed in the written proposal.
Discuss your training goals| Course | Machine Learning for Non-Technical Teams |
|---|---|
| Duration | 20 live hours across 2.5 days or an agreed equivalent schedule |
| Audience | Product managers, business analysts, functional leaders and project sponsors |
| Prerequisite | Comfort with charts and business data; no coding or advanced statistics |
| Output | Reviewed model evaluation and an ML project brief |
| Delivery | Public live online; private live online or onsite |
Who may deliver this programme
Technovids assigns a trainer whose subject depth and facilitation experience fit the audience and agreed scope.
Pankaj Rana
Co-founder & Lead Instructor, Technovids Consulting
Pankaj is a co-founder and the lead instructor at Technovids Consulting. His programme work spans practical AI adoption, AI engineering, evaluation, responsible production AI, data analytics and corporate learning design, with an emphasis on hands-on exercises connected to workplace tasks.
Expertise:
- Practical AI adoption
- Prompting and evaluation
- Responsible AI
- Corporate learning design
Junaid Khateeb
AI & Machine Learning Trainer, Strategist and Consultant
Junaid is an AI and machine-learning trainer, strategist and consultant with more than 22 years of training experience. His work combines enterprise AI education, business-centred adoption, responsible AI practices and applied solution design, helping professional audiences connect AI concepts to real organisational needs.
M.E. in Computer Engineering, specialising in Artificial Intelligence · author of programming books on Python, Java and C++
Expertise:
- Enterprise AI and ML training
- AI strategy and adoption
- Responsible AI
- Applied AI solution design
Pradeep Varadarajan
Technical Trainer & Curriculum Architect
Pradeep is a technical trainer and curriculum architect with more than 20 years of experience across enterprise technology, cloud infrastructure and advanced AI systems. He designs practitioner-focused programmes, labs and assessments covering AI, LLM systems, Python and machine learning, translating complex technical subjects into structured learning experiences.
Bachelor of Engineering in Electrical Engineering · instructor-led, virtual and blended delivery
Expertise:
- AI and LLM systems
- Curriculum architecture
- Python and machine learning
- Hands-on lab design
The trainer for a specific delivery is confirmed with the schedule or written proposal.
Updated
Organisations we have trained, and participant perspectives
Permissioned feedback and published client relationships provide context on delivery. They do not guarantee a specific adoption or business result.
Organisations we have delivered training for
Practical training programmes delivered for learners, professionals and teams across AI, data, cloud, productivity and professional skills.
Participant experiences across workplace roles
What professionals found useful, practical and relevant to their day-to-day work.
- Gauraw DeshmukhData Engineer · Capgemini
Technovids AI training was very practical and well structured. The sessions helped me understand how AI concepts can be applied in real data engineering and business workflows, instead of only learning theory. The trainer explained the topics clearly with relevant examples, which made the learning experience valuable and easy to follow.
- PriyanshaSoftware Developer
The AI training from Technovids helped me gain a better understanding of Generative AI, prompt engineering, and practical AI use cases for software development. The course was clear, interactive, and focused on real-world applications. It gave me confidence to explore AI tools more effectively in my work.
- Sidda RajuData Engineer
Technovids delivered the AI training in a very practical and industry-relevant way. As a data engineer, I found the examples and hands-on discussions useful for understanding how AI can improve data workflows, automation, and decision-making. The sessions were well planned and easy to understand.
- Akshat KabraData Engineer
The AI training program by Technovids was insightful and professionally delivered. It covered important AI concepts in a simple and practical manner, with examples that connected well with data and analytics use cases. The sessions helped me build a stronger foundation in applying AI in real business scenarios.
- MangeshSoftware Engineer · Siemens
Technovids conducted the AI training in a very effective and engaging manner. The sessions were practical, easy to follow, and focused on how AI can be used in real software engineering and business workflows. It was a valuable program for understanding the current direction of AI adoption in the industry.
- ReshuSoftware Developer
Technovids AI training was a very useful learning experience. The course explained AI concepts in a practical way and showed how developers can use AI tools to improve productivity, problem-solving, and application development. The trainer's approach was simple, clear, and focused on industry use cases.
Common questions before planning this course
Answers about the published course. The written proposal records any agreed customisation, delivery details and commercials.
Are public cohorts available for Machine Learning for Non-Technical Teams?
Public cohorts run live online when dates are published. An enquiry records your interest; a place is confirmed only when Technovids provides the cohort schedule and enrolment terms in writing.
What does a public cohort include?
The Technovids LMS provides notes, assignments, lab instructions and recordings for three months after the cohort. Live sessions are hosted through Zoom.
Is a certificate included?
Learners who meet the stated completion requirements receive a Technovids completion certificate with a unique ID that can be verified on the Technovids website. It is not a vendor certification.
What equipment or accounts do participants need?
Participants provide a suitable laptop and the accounts named in the cohort information. Tool subscriptions, consumption charges or paid API use are not included unless stated in writing.
Will I be able to build a machine-learning model afterwards?
No. The objective is to understand, sponsor and evaluate ML work. Choose Data Science & ML Training for hands-on Python model development.
Do I need mathematics or statistics?
No advanced background is required. The course explains metrics visually and in terms of business consequences. Participants should be comfortable reading tables, percentages and charts.
Is this useful for evaluating vendors?
Yes. The programme includes questions about problem fit, data, baselines, metrics, integration, monitoring, ownership and fallback. It does not endorse or rank specific vendors.
Plan your next step with this course
Tell us whether you are enquiring for yourself or a team, which development tasks matter and what access you have. We will confirm suitable delivery options and commercial details in writing.