Foundations · Build your engineering starting point

Foundations of AI

Data, Machine Learning & Responsible Evaluation

Learn how AI models learn—and how to tell when their results deserve confidence. Build the mathematics, data and evaluation habits behind practical AI. Train a small model, compare it with a simple baseline and explain its mistakes using a reproducible project.

10 practical modules Three guided projects One independently assessed foundation capstone A readiness review for your next learning step
RoboEdify track record
10,000+
alumni transformed
1,000+
hiring partners
4.8/5
average class rating
87%
placed in 6 months
10+
years of training
Where our AI alumni work
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer

What will this foundation help you understand?

AI foundations connect a task, usable data, a model and evidence of performance. This course introduces machine learning and small neural networks, with a bounded generative-AI evaluation exercise. The emphasis is on reasoning about results rather than collecting tool names.

Learning approach

Build understanding through small tasks you can explain.

Each module connects a concept to a practical exercise. You predict a result, run the task, inspect what happened and correct a mistake. The final review checks your own reasoning and working project rather than attendance alone.

Learning format

Learn through guided practice and individual feedback.

The program combines mentor-led explanations, exercises, project work and support sessions. An advisor can explain the current on-campus, online or working-professional format and the learning setup before enrollment.

On-campusLive onlineWorking-professional format
Who should join

Start at the level your current skills support.

No prior machine-learning experience is required. You should be able to write basic Python functions, work with lists and read a small data file. Complete Foundations of Engineering Computing first, or demonstrate equivalent readiness. School-level algebra is expected; the course develops the specific probability and linear-algebra ideas it uses.

This course is suitable for students, graduates and working professionals who want to strengthen the relevant foundations. A diagnostic helps avoid repeating skills you can already demonstrate.

Learning outcomes

Leave with foundations you can demonstrate.

Frame a learning problemDefine inputs, targets and a meaningful success measure.
Inspect dataCheck labels, missing values and class balance.
Build a baselineTrain and compare a simple model.
Evaluate honestlySeparate development from final testing and inspect class-level errors.
Understand neural networksTrace a small training and prediction workflow.
Explain limitationsPackage reproducible results and distinguish unsupported claims.
Entry readiness

What you should be able to do before you start.

ComputingWrite and run a small Python function and inspect an error.
DataRead a table and identify missing values and units.
MathematicsUse arithmetic, simple equations, percentages and graphs.
Course curriculum

Ten modules, from first principles to a working foundation project.

01

AI tasks and model reasoning

Foundation
+
Topics
•Classification, regression and clustering
•Learning versus rules
•Predictive and generative outputs
•Task boundaries
Hands-on lab
Describe a small sensor-classification problem and a simple rule-based comparison.
Failure scenario you solve
A prediction problem has no clear target or success criterion.
You build
Problem brief and baseline plan.
Assessed outcome: Explain what the model should predict and what would count as failure.
02

Mathematics for learning

Foundation
+
Topics
•Vectors and dot products
•Mean and variation
•Probability
•Gradients as local change
•Loss intuition
Hands-on lab
Calculate a small linear prediction and inspect how a parameter changes its error.
Failure scenario you solve
A learner applies a formula without noticing inconsistent input scales.
You build
Worked calculations and numerical notebook.
Assessed outcome: Explain a prediction and the purpose of a training update.
03

Data quality and labels

Foundation
+
Topics
•Schemas
•Missing values
•Duplicates
•Units
•Label meaning
•Class balance
•Dataset provenance
Hands-on lab
Audit a supplied dataset and produce a transparent cleaning and rejection report.
Failure scenario you solve
The rare target class is underrepresented and missing rows disappear without explanation.
You build
Dataset card and data-quality report.
Assessed outcome: Report what was retained, rejected and uncertain.
04

Splits, preprocessing and leakage

Foundation
+
Topics
•Train/validation/test roles
•Grouped and time-aware splits
•Preprocessing fit scope
•Pipelines
•Untouched final evaluation
Hands-on lab
Compare a random split with a grouping-aware split and fit preprocessing using training data only.
Failure scenario you solve
Records from the same session appear in both training and test sets.
You build
Split manifest and preprocessing pipeline.
Assessed outcome: Explain which split matches the intended generalization claim.
05

Baseline machine learning

Foundation
+
Topics
•Simple classifiers/regressors
•Feature choices
•Fit/predict
•Overfitting
•Confusion matrix
•Precision/recall
•Baseline comparison
Hands-on lab
Train a simple classifier and compare it with a dummy or rule baseline.
Failure scenario you solve
High accuracy hides poor recall on the class the task cares about.
You build
Model comparison and error analysis.
Assessed outcome: Choose a metric appropriate to the task and explain failed cases.
06

Small neural networks

Foundation
+
Topics
•Tensors
•Layers
•Activations
•Loss
•Optimization
•Epochs
•Training/evaluation mode
•Deterministic setup limits
Hands-on lab
Train a small network using a supplied scaffold and compare it with the simpler model.
Failure scenario you solve
A larger model improves training results but performs worse on held-out data.
You build
Training configuration, learning curves and comparison.
Assessed outcome: Explain whether the added model complexity helped.
07

Generative AI and output evaluation

Foundation
+
Topics
•Prompt versus training
•Model output uncertainty
•Reference answers
•Source checking
•Private data
•Prompt-injection awareness
Hands-on lab
Evaluate a supplied set of generated answers against references and review one misleading response.
Failure scenario you solve
Fluent wording is accepted as evidence even when the answer is unsupported.
You build
Small evaluation set and response-review report.
Assessed outcome: Separate helpful output from verified correctness; no paid API is required for this exercise.
08

Inference and reproducible handoff

Foundation
+
Topics
•Saved model
•Preprocessing consistency
•Input validation
•Environment versions
•Model card
•Limitations
Hands-on lab
Package an inference example and test a new valid input and an invalid one.
Failure scenario you solve
Training and inference use different feature orders.
You build
Inference script, model card and smoke tests.
Assessed outcome: Reproduce a prediction using the documented input contract.
09

Independent AI foundation capstone

Capstone
+
Topics
•Frozen task
•Baseline
•Defensible splits
•Final evaluation
•Errors
•Reproducibility
Hands-on lab
Train and evaluate a small classifier on a supplied dataset and defend its limitations.
Failure scenario you solve
The reported score depends on test data used during tuning.
You build
Project repository, split manifest, metrics and model card.
Assessed outcome: Reproduce results and explain why the evaluation supports only the stated scope.
10

Readiness review and technical explanation

Readiness review
+
Topics
•Code walkthrough
•Leakage diagnosis
•Metric interpretation
•Personal contribution
•Next-path selection
Hands-on lab
Explain your project and diagnose an unfamiliar data or evaluation error.
Failure scenario you solve
A learner quotes the score but cannot explain the denominator or split.
You build
Reviewed project summary and individual feedback.
Assessed outcome: Demonstrate the foundations needed for the next AI course.

Scope note: This is a technical foundation, not a standalone ML-engineer placement program. Production MLOps, advanced model architecture, large-model fine-tuning and autonomous-agent deployment belong to later courses.

Tools and methods

Use a focused toolkit that supports understanding.

Python and NumPyExpress data and numeric operations.
scikit-learnBuild baselines, preprocessing and evaluations.
A qualified PyTorch setupExplore a small neural-network scaffold.
Notebooks, scripts and GitExplain experiments and reproduce the final result.

The exact software setup is qualified before teaching. Supplied examples and small datasets keep the core accessible; no physical robot, FPGA board or paid cloud subscription is required for the stated foundation assessment.

Projects and portfolio

Three guided projects and a foundation capstone.

Guided projects develop across the modules; they are part of the course rather than additional promises of production experience.

Guided project 1

Dataset audit

Document labels, missing values and a defensible split.

Portfolio evidence
Dataset cardQuality reportSplit manifest
Guided project 2

Baseline versus neural network

Compare two small models on matched data and metrics.

Portfolio evidence
Learning curvesMetricsError analysis
Guided project 3

Inference handoff

Package preprocessing and prediction with input checks.

Portfolio evidence
Inference scriptTestsModel card
Capstone

A defensible small-model classification project

Define a bounded task, audit the supplied data, train a baseline and a small comparison model, then report held-out performance and limitations. The model with the most complexity need not be the best choice.

What you submit
  • •A clear problem statement and assumptions.
  • •Your code, calculations or simulation files.
  • •Tests covering the specified normal and failure cases.
  • •A result report with units, counts or metrics as applicable.
  • •A readable reproduction guide and individual explanation.
What the assessor checks
  • •Keep final test data untouched during development and fit preprocessing only on training data.
  • •Report split groups, class counts, baseline results and task-relevant errors.
  • •Retain failed experiments and explain the selected model without inventing an accuracy target.
  • •A second learner can reproduce inference from the handoff; invalid inputs receive a defined response.
Assessment and completion

Demonstrate understanding before moving forward.

RoboEdify · Certificate of Completion
Foundations of AI
Presented to
Learner name
Awarded for completing the foundation exercises and independently explaining an assessed project within the course scope.
Manikanta Kona
Founder · RoboEdify
ROBO
EDIFY
CERT
30%
Module exercises
20%
Practical checkpoints
35%
Foundation capstone
15%
Individual explanation and debugging
The proposed completion standard is 70% overall, at least 60% separately in the capstone and individual review, and completion of all mandatory project requirements. Feedback identifies specific gaps for remediation and reassessment.
The credential records foundation completion. It does not replace specialist training, employer assessment or external certification.
Your next learning step

Use the foundation to choose a focused engineering pathway.

Supports progression to applied AI, generative AI and edge-ML learning. IoT/Edge AI additionally requires firmware readiness; AI Chip Design additionally requires RTL and architecture competence.

Your next-course recommendation is based on demonstrated readiness. Recognized foundation work can satisfy matching preparation outcomes, but each advanced course still checks its specific prerequisites. You do not need to take all four foundations unless your chosen pathway requires them.

Career support

Build your first technical portfolio and plan your next step.

Across RoboEdify’s career programs, support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. This foundation course focuses on project feedback and progression readiness. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.
01 / PORTFOLIO

Evidence from your own work.

For this foundation course, your portfolio starts with the assessed project, a clear explanation of your work and the corrections you made after feedback.

02 / PROFILE

Profile and resume preparation.

A reviewed technical project summary, a readable repository and resume statements grounded in your contribution.

03 / INTERVIEWS

Interview practice and introductions.

Technical interview practice, with role-fit introductions where available.

RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.

Institute leadership

Meet the team behind RoboEdify.

MK
Manikanta Kona
Founder, RoboEdify · Enterprise AI Architect
Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI
15 yrs
ENTERPRISE AI
2,400+
LEARNERS
4.9 /5
RATING

Manikanta brings 15 years of enterprise platform architecture experience from AT&T, Salesforce, Cox Communications and Broadcom. His background includes enterprise platform and AI rollouts for Fortune-500 banks, telcos and insurers, and production agentic-AI deployments for governed case handling.

Education: M.S. in Engineering, Purdue University.

RK
Ravi Krishna
Chief Technologist, RoboEdify · Implementation & Delivery Lead
Enterprise automation · Deployment · Evaluation evidence · Delivery governance
10 yrs
IMPLEMENTATION & DELIVERY
1,800+
LEARNERS
4.8 /5
RATING

Ravi leads RoboEdify's implementation and delivery practice. His background spans enterprise automation programs, deployment, evaluation evidence and delivery governance.

Industry voices from RoboEdify’s AI programs

What employers say about RoboEdify’s AI and robotics graduates.

The following testimonials retain their original program context. They describe AI, robotics and enterprise-program experience, rather than outcomes from this foundation course.

Microsoft logo

RoboEdify grads ramp 40% faster on applied AI projects than typical hires. Best AI engineering pipeline in India.

Aakash Mehta

Aakash Mehta, Partner Programme Lead, Microsoft

Deloitte logo

We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for ML plus AI agent practices.

Anita Sharma

Anita Sharma, Senior Manager, Deloitte

Mphasis logo

The programme is comprehensive — predictive ML, LLM systems, plus agentic and robotics work. Grads come pre-trained for enterprise.

Rahul Bhatt

Rahul Bhatt, Solutions Lead, Mphasis

TCS logo

Their agent + robotics track produces engineers who ship production-grade perception and control code on day one. Genuinely rare.

Deepak Pillai

Deepak Pillai, Senior Architect, TCS

Accenture logo

What sets RoboEdify apart is the simulation-to-hardware layer baked into the AI track. Our clients ask for exactly this profile.

Suresh Menon

Suresh Menon, Practice Lead, Accenture

Infosys logo

Their fundamentals prep is rigorous, and the capstone with a real deployed system and safety case is what closes interviews for us.

Vikram Iyer

Vikram Iyer, Director, Infosys

Wipro logo

RoboEdify's AI grads get models into production twice as fast in the first 90 days. Our internal metrics back this up clearly.

Lakshmi Nair

Lakshmi Nair, VP Engineering, Wipro

Cognizant logo

Best AI + robotics pipeline we've sourced from in India. Their projects are production work, not toy code.

Karthik Subramanian

Karthik Subramanian, Engineering Director, Cognizant

Capgemini logo

Strong ML and edge-deployment foundation. Their grads need almost zero ramp time on enterprise engagements with us.

Arun Joshi

Arun Joshi, Practice Director, Capgemini

IBM logo

We've placed 40+ RoboEdify alumni across our AI and automation teams. Strong fundamentals, sharp on the agent stack.

Sanjay Verma

Sanjay Verma, Talent Director, IBM

LTIMindtree logo

ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.

Anjali Desai

Anjali Desai, Practice Head, LTIMindtree

Tech Mahindra logo

Their AI track delivers engineers who navigate data, models and integrations on customer engagements unsupervised.

Ramesh Iyer

Ramesh Iyer, Senior Manager, Tech Mahindra

Cyient logo

Hired 25+ RoboEdify graduates for our AI practice. Strong coding, strong ML depth, sharp on the agent layer.

Geetha Pillai

Geetha Pillai, Talent Acquisition Lead, Cyient

Microsoft logo

RoboEdify grads who blend robotics with Azure OpenAI land production-ready on day one. Rare combination, well-trained.

Priya Reddy

Priya Reddy, Talent Lead, Microsoft

AI alumni across RoboEdify

Meet alumni featured in our AI programs.

SB
Spandana Bala
ML Engineer
Hyderabad · India
Now at · Infosys
NV
Naveen Vedala
AI Agent Engineer
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
Simulation Engineer
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
Robotics Software Engineer
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Edge AI Engineer
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Physical AI Engineer
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Autonomous Systems Engineer
New York · United States
Now at · NTT Data
RD
Rahul Dhamma
Robot Learning Engineer
Hyderabad · India
Now at · Cognizant
Our locations

Come chat with us—on campus or online.

Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave · Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Opening hours
Mon–Sun · 7 AM–9 PM
Online
Global
Live online classes and mentorship, with working-professional learning options. The foundation uses the software or simulation setup described in its tools section.
Format
Live online + mentorship
Options
Working-professional
Learning support

Support when you need to catch up.

Freeze your seat for up to 90 days and rejoin the next class at no extra fee. TAs run catch-up sessions every Saturday, and recordings of every live session are available for the lifetime of your account.

FAQs

Questions about prerequisites, tools and completion.

Do I need coding experience?+
Basic Python is required. Foundations of Engineering Computing supplies that starting point if needed.
Do I need advanced mathematics?+
School-level algebra is expected. The course develops the vectors, probability and gradient intuition used in its exercises.
Do I need a GPU or paid AI subscription?+
The teaching project is small and designed around a qualified accessible environment. Generative-AI evaluation can use supplied outputs; paid API access is not required.
Is this a prompt-engineering course?+
Prompt/output evaluation is one bounded module. The main emphasis is data, models, evaluation and reproducibility.
Is this a job-ready specialist course?+
This is foundation training. It develops prerequisites and a first technical project; specialist-role preparation belongs to the relevant advanced course.
Can experienced learners skip material?+
A practical diagnostic can recognize skills you already demonstrate. Progression still depends on the readiness required by your chosen pathway.
How is learning organized?+
The course combines explanations, guided exercises, a project and individual feedback. An advisor can explain the current class format before enrollment.
What if I need to pause or catch up?+
You can freeze your seat for up to 90 days and rejoin the next class at no extra fee. Saturday catch-up sessions and live-session recordings support learning. Recordings remain available for the lifetime of your account.
Are placement results on the page from this foundation?+
No. The shared figures and AI alumni stories come from RoboEdify’s Physical AI page. They are not outcomes specific to this foundation course.
How can I ask about fees and the learning setup?+
Contact a course advisor to discuss the current offering, preparation needs, software requirements, fees and support terms.

Still have a question?

Find the right foundation for your next step.
One million AI-native professionals by 2027.

Tell us what you already know and which engineering pathway interests you. We’ll help you identify the foundations to strengthen and the practical work to begin with.

Plan your learning

Course
Foundations of AI
Preparation
Computing readiness (Foundations of Engineering Computing or equivalent)
Level
Foundation
Curriculum
10 modules

Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.