01
ML Foundations
The mental model behind every model you will train.
- Supervised vs unsupervised
- Features and targets
- Training vs inference
- Generalisation
- Overfitting
UNLOX Global Programs · Machine Learning Engineering
Machine Learning Engineering
Become the ML Engineer Who Can Model,Deploy & Defend.
In collaboration with
Master applied machine learning by building real prediction, classification, recommendation and vision systems, evaluating them honestly, deploying them, and developing a portfolio that demonstrates what you can actually ship.
- Project-Based
- Mentor Supported
- Portfolio Driven
- IBM Learning Experience
How ML work happens here
- 01ProblemTranslate an industry problem into a machine learning task.
- 02DataPrepare, validate and engineer features from real data.
- 03BaselineEstablish a meaningful reference before improving anything.
- 04ModelTrain, tune and compare candidate algorithms.
- 05EvaluateMeasure error, fairness, stability and business relevance.
- 06DeployShip the model behind a usable interface or API.
- 07MonitorWatch drift, confidence and real production behaviour.
Portfolio output
Evidence, not completion
- Performance Forecasting EngineDeployed regression tool + model cardFoundational
- Predictive Risk Classification SystemDeployed risk classifier + threshold analysisFoundational
- Automated Feature and Model PipelineReproducible pipeline + reproducibility test reportApplied
In collaboration with
10
Industry-style ML projects
Machine Learning Engineering — Global Program
One structured program, delivered to learners worldwide
- Learning Model
- Project-Based
- Delivery
- Online
- Access
- Global
- Mentorship
- Timezone-Friendly
- Language
- English
- Portfolio
- Production-Style Projects
- IBM Component
- Included
- Eligibility
- Profile Review
Viewing from India
Next global cohort
Build alongside ambitious learners across borders.
Join a structured global learning experience designed around projects, mentor feedback and measurable proof of work.
Cohort intake
Next intake — September 2026
Applications are reviewed on a rolling basis. Cohort size is limited so mentor review stays meaningful.
- Next Cohort
- Next intake — September 2026
- Format
- Online + Project-Based
- Access
- Global
- Language
- English
- Mentor Support
- Timezone-Friendly · Asia / IST
- Applications
- Open
This is not another ML course
Don't Just Learn ML. Ship Models That Hold Up.
Traditional ML learning
vsThe UNLOX way
- Watch algorithm tutorialsFrame
- Copy notebooksPrepare
- Run sample datasetsBaseline
- Complete quizzesTrain
- Receive certificateEvaluate
- Still unsure how to ship a real ML systemExplain
then it compoundsDeployMonitorProve
Knowing algorithms is useful. Knowing how to turn data into a deployed, defensible model is what matters.
Domain overview
What You'll Actually Master
Machine learning is not one algorithm. This program teaches you how the major parts of a real ML system — data, models, evaluation, explainability and production — work together.
02
Python, Maths & Statistics for ML
The working toolkit used the way ML engineers use it.
- NumPy and Pandas
- Probability and distributions
- Loss functions
- Gradient descent
- Statistical significance
03
Data Preparation & Feature Engineering
Most model failures are data failures. This is where you prevent them.
- Missing values
- Encoding
- Scaling
- Feature selection
- Reproducible pipelines
04
Regression & Classification
The core predictive models that power most production ML.
- Linear and tree-based regression
- Logistic regression
- Decision trees
- Metrics
- Threshold selection
05
Imbalanced Learning & Anomaly Detection
Finding the rare events that matter most.
- Class imbalance
- Resampling
- Precision-recall
- Anomaly detection
- Review prioritisation
06
Recommendation Systems
Personalising what each user sees.
- Collaborative filtering
- Content-based filtering
- Ranking
- Cold-start
- Diversity
07
Computer Vision
Teaching models to see and classify images reliably.
- Image preprocessing
- Augmentation
- Transfer learning
- Per-class evaluation
- Visual error analysis
08
Natural Language for ML
Classifying and routing text at scale.
- Tokenisation
- TF-IDF
- Text embeddings
- Multi-class evaluation
- Confidence thresholds
09
Ensembles, Tuning & Explainability
Choosing a model you can defend, not just the highest score.
- Random forests
- Gradient boosting
- Hyperparameter tuning
- Local and global explanations
- Fairness
10
Production ML & MLOps
Keeping models trustworthy after they ship.
- Model serialisation
- Inference APIs
- Drift detection
- Versioning
- Rollback
11
Model Evaluation & Risk
The discipline that separates a demo from a production model.
- Error analysis
- Segment performance
- Business relevance
- Model cards
- Technical defence
12
Deployment & Interfaces
Making a model usable by someone who isn't you.
- Prediction interfaces
- APIs
- Containerisation basics
- Cloud deployment
- Documentation
ML system architecture
The system you learn to build
Select a part of the system to see what you actually understand, what you build with it, and the project that proves it. You leave understanding the whole ML system — not only how to fit a model.
Turning an industry problem into a learnable task.
- Target definition
- Feature vs label
- Success metric
- Constraints
- Baseline expectations
Build: A clearly scoped ML problem statement a model can actually solve.
Proven in: Performance Forecasting Engine
Projects you will build
Your portfolio is the proof
Each project is scoped like real work: a context, a problem, deliverables and an artifact you can show.
Deliverables
— Problem-definition document
— Data-quality report
— Feature definitions
Deliverables
— Processed dataset
— Baseline model
— Minimum two improved models
Deliverables
— Processed dataset
— Baseline classifier
— Model comparison
Deliverables
— Raw dataset
— Data-validation rules
— Feature definitions
Deliverables
— Imbalance analysis
— Baseline model
— Supervised detection model
Deliverables
— Interaction dataset
— Popularity baseline
— Content-based model
Deliverables
— Curated image dataset
— Data-quality report
— Baseline model
Deliverables
— Prepared text dataset
— Baseline model
— Minimum two text-representation approaches
Deliverables
— Baseline model
— Multiple candidate models
— Hyperparameter search
Deliverables
— Packaged ML pipeline
— Prediction API
— Input-validation system
How it works
One loop, repeated until it becomes instinct
Step 01 · Understand the Problem
Read the industry brief the way an ML engineer reads a business request.
Curriculum
A structured build path, stage by stage
Every stage names what you learn, what you build with it, and the outcome it produces.
In collaboration with
IBM learning experience components are mapped into the stages below.
The Python, maths, statistics and model-development foundations every project depends on.
ML Concepts, Python, Maths & Builder Setup
Module 1Learn
Supervised/unsupervised learning
Python, NumPy, Pandas
Vectors, probability, distributions
Training/validation/testing
Baseline models
Regression and classification metrics
Build
Codehood ML builder environment + baseline readiness assessment
Output
You can structure and execute a machine learning workflow independently.
PythonNumPyPandasGitGitHub
Learning perks
Everything working on you at once
Support, review, practice and proof are one connected system — the cards move on their own; hover or tap one to see the product behind it.
AI learning support
BLU
Duplicate webhook retries are creating duplicate transactions.
Before changing code: is your payment handler idempotent?
Next step → key each event by its provider event id.
AI learning support01
BLU
Explains concepts, helps debug models and guides project thinking.
- Debug prompts, not answers
- Explains the why behind a fix
- Available while you build, 24/7
Asks the next diagnostic question instead of pasting a fix.
Build work02
Projects
Scoped like real work: a brief, constraints, deliverables and a shipped artifact.
- Real brief with constraints
- Scoped deliverables per stage
- Deployed, documented artifact
10 briefs, one flagship build.
Human review03
Mentorship
Human review of feature choices, evaluation and model decisions.
- Written review on every submission
- Architecture and code-quality feedback
- Direction on what to fix next
Every submission returns with a written review report.
Interview readiness04
PrepFree
ML interview and career preparation.
- Mock interviews on your own builds
- Scored on framing and depth
- Repeat until answers are sharp
Scored on clarity, framing, depth and trade-offs.
Proof of work05
Portfolio
A reviewed, documented, deployed body of work you can show without explaining it away.
- Live deployed links
- Decision log per project
- Review history attached
Live links, decisions log and review history per project.
In collaboration with IBM06
IBM learning experience
The program includes an IBM learning experience component alongside the UNLOX AI curriculum. UNLOX delivers the program, the AI projects, the mentorship and the portfolio; the IBM component adds additional structured industry learning exposure.
- IBM learning components mapped in
- Industry-aligned tooling context
- Recognised credential on completion
Structured industry learning · Applied technical exposure · Assessment component
Career support07
Opportunities
Role mapping, portfolio positioning and interview practice tied to your build history.
- Role directions mapped to projects
- Portfolio positioning support
- Interview practice tied to your work
5 role directions mapped to your projects.
Checkpoints08
Assessments
Progress is measured on working systems and defended decisions, not quiz scores.
- Stage gates between builds
- Measured on working systems
- Defend your decisions, not quizzes
Stage gates before you move to the next build.
AI learning support01
BLU
Explains concepts, helps debug models and guides project thinking.
- Debug prompts, not answers
- Explains the why behind a fix
- Available while you build, 24/7
Asks the next diagnostic question instead of pasting a fix.
Build work02
Projects
Scoped like real work: a brief, constraints, deliverables and a shipped artifact.
- Real brief with constraints
- Scoped deliverables per stage
- Deployed, documented artifact
10 briefs, one flagship build.
Human review03
Mentorship
Human review of feature choices, evaluation and model decisions.
- Written review on every submission
- Architecture and code-quality feedback
- Direction on what to fix next
Every submission returns with a written review report.
Interview readiness04
PrepFree
ML interview and career preparation.
- Mock interviews on your own builds
- Scored on framing and depth
- Repeat until answers are sharp
Scored on clarity, framing, depth and trade-offs.
Proof of work05
Portfolio
A reviewed, documented, deployed body of work you can show without explaining it away.
- Live deployed links
- Decision log per project
- Review history attached
Live links, decisions log and review history per project.
In collaboration with IBM06
IBM learning experience
The program includes an IBM learning experience component alongside the UNLOX AI curriculum. UNLOX delivers the program, the AI projects, the mentorship and the portfolio; the IBM component adds additional structured industry learning exposure.
- IBM learning components mapped in
- Industry-aligned tooling context
- Recognised credential on completion
Structured industry learning · Applied technical exposure · Assessment component
Career support07
Opportunities
Role mapping, portfolio positioning and interview practice tied to your build history.
- Role directions mapped to projects
- Portfolio positioning support
- Interview practice tied to your work
5 role directions mapped to your projects.
Checkpoints08
Assessments
Progress is measured on working systems and defended decisions, not quiz scores.
- Stage gates between builds
- Measured on working systems
- Defend your decisions, not quizzes
Stage gates before you move to the next build.
Global learning experience
An IBM learning experience, inside an UNLOX program
The program includes an IBM learning experience component alongside the UNLOX AI curriculum. UNLOX delivers the program, the AI projects, the mentorship and the portfolio; the IBM component adds additional structured industry learning exposure.
In collaboration with
UNLOX delivers
- The program and curriculum
- Industry-style project briefs
- Mentor reviews and feedback loops
- Portfolio and career preparation
In collaboration with
The IBM component adds
- Structured industry learningAdditional learning content delivered as part of the IBM learning experience component of this program.
- Applied technical exposureGuided practical material that complements the UNLOX AI project work rather than replacing it.
- Assessment componentWhere included, assessment and completion criteria are stated in the program terms.
Where it sits in your journey
- 1UNLOX AI Program
- 2AI Project Learning
- 3IBM Learning Experience
- 4Portfolio
- 5Career Preparation
Environment details are confirmed in the program documentation shared on application.
Exact scope of the IBM learning experience component, platform details and any certification wording follow the approved program documentation. Nothing here implies IBM employment, placement, internship or a degree.
Credentials
Two credentials: one for the course, one for the work
Every learner receives a course completion credential and a project completion credential — each independently verifiable.
Sample credential shown for illustration. Learner name, project title and verification link are unique to each issued certificate.
IBM learning experience component
Course completion certificate
Issued on unlox.skillsnetwork.site, powered by IBM Developer Skills Network. Verifiable by QR and certificate URL.
- What it represents
- Completion of the IBM learning experience included in this program, with a passing grade on the course assessments.
- How it is earned
Complete the included IBM learning experience component
Receive a passing grade on the course assessments
- How to use it
- Adds verifiable industry learning exposure alongside your UNLOX program record.
QR verification Unique certificate URL
Transformation
The shift this program is built to produce
Capability is the outcome — and it is visible in what you can build, review and defend at the end.
Before
01Where most learners start
- Trains models inside notebooks only
- Copies tutorial code
- Understands algorithms separately
- Has no deployed ML system
- Cannot explain evaluation or model choice
During
02What changes while you build
- Structured ML curriculum
- Builds ten focused projects
- Establishes baselines and evaluates honestly
- Receives mentor reviews
- Learns deployment and monitoring
- Documents decisions
After
03How you operate at the end
- Ten deployed ML projects
- Understands regression, classification, vision and NLP pipelines
- Can discuss model selection and trade-offs
- Can evaluate and explain model output
- Has documented portfolio evidence
- Can defend own ML system decisions in a technical viva
What exists at the end
- 10 ML builds, deployed and documented
- Evaluation reports and model cards per system
- Reproducible pipelines
- A flagship production ML case study
Role explorer
Pick a role. See what you'd be able to do.
Preparation here is capability-based: each role lists what you can actually do, and the project evidence that proves it.
Junior Machine Learning Engineer
Takes models from data to a measurable production service.
You can
- Prepare data and engineer features for a real problem
- Train, compare and select models with evidence
- Expose a model as a reliable, monitored service
Data preparationModel trainingEvaluationDeployment
Proven through
- Performance Forecasting Engine
- Production ML Monitoring System
Portfolio evidence
- Training pipeline
- Evaluation report
- Deployed endpoint
- Mentor review
Proof of work
You leave with a dossier, not a certificate alone
Everything you build is packaged so a reviewer can evaluate it in minutes.
portfolio.unlox.com/dossier
Deployed ML systems
Models running behind a real interface or API, not notebook screenshots.
Reviewed by mentorsVerifiable credentialsShareable in one link
Industry ecosystem
Built around
real industry exposure.
Projects, reviews, career preparation and industry interactions connect what you learn to how work actually happens.
UNLOX / Industry ecosystem
LiveLearner
Projects
Mentors
Industry
Portfolio
Career
● Learner
○ Projects
○ Mentors
○ Industry
○ Portfolio
○ Career
Companies in the UNLOX hiring network
Machine Learning Engineering
UNLOX Global Program
₹65,000
Shown for India · INR. Change your country in the header to see local pricing.
Scholarship opportunities may be available for eligible learners.
In collaboration with
What you invest in
- Structured applied machine learning curriculum
- Mentor evaluation and model-decision review
- Ten portfolio-grade ML builds
- IBM learning experience component
- ML interview and career preparation
What you leave with
- Deployed machine learning systems
- Evaluation reports, model cards and pipeline documentation
- The ability to defend your model decisions
- Credentials and proof of work
Global learner scholarship
Your geography shouldn't limit what you can build.
Scholarship opportunities may be available for eligible learners joining selected UNLOX Global Program cohorts.
Profile-based evaluation
Scholarship eligibility may depend on learner profile and cohort availability.
Selected cohorts
Scholarship availability can vary between programs and intakes.
Global applicants
Eligible international applicants can request an assessment.
Scholarship availability, eligibility and award value are subject to program terms, learner profile and cohort availability.
FAQ
Questions people ask before applying
Yes. The program is open to international learners. Sessions are scheduled to work across major timezone groups, and every live session is recorded so you can catch up if a slot does not suit you.
Apply
Talk to the admissions team
Share your details and our team will connect with you about the Machine Learning Engineering program — live session schedule, 4-month structure, fees and the next intake.
Applications open · Global program
You don't need another course.You need something you've built.
Join UNLOX Machine Learning Engineering and turn learning into projects, projects into proof, and proof into career readiness.
- Global Access
- Timezone-Friendly
- Project-Based
- Mentor Supported
- IBM Learning Experience
- Portfolio Driven
Viewing from India
Machine Learning Engineering
UNLOX Global Program
Check your eligibility in 2 minutes.
Share a few details and our team reviews your profile, cohort fit and scholarship options before any commitment.
Reviewing applications from India · Asia / IST
In collaboration with
More about this program
Machine Learning Engineering at UNLOX
What is applied machine learning engineering?
Applied machine learning engineering is the practice of turning data into working prediction systems. It covers preparing data, engineering features, training and comparing models, evaluating them honestly, explaining their behaviour and running them in production.
The job is less about knowing every algorithm name and more about being able to take a business problem, frame it as a learning task, build it, measure it, deploy it and explain why you built it that way.
What will you learn in this Machine Learning Engineering program?
The UNLOX Global Machine Learning Engineering program covers ML foundations, Python and statistics for ML, data preparation and feature engineering, regression and classification, imbalanced learning and anomaly detection, recommendation systems, computer vision, natural language processing, ensembles and explainability, and production ML and MLOps.
Each area is taught through building. Learning is verified by what you can produce, reviewed by mentors, and documented as portfolio evidence.
Career directions after the program
Learners target roles such as junior machine learning engineer, applied ML developer, computer vision associate, NLP/ML associate and MLOps associate. Each role expects evidence: deployed systems, evaluation records and the ability to defend technical decisions.
Every role you might target is backed by at least one machine learning system you built, evaluated and documented yourself.
Learning methodology
Learning is project-based. You define the prediction target, prepare data and features, establish a baseline, train and compare models, evaluate, review, improve, deploy, monitor, document and publish. BLU AI support and human mentor review run alongside every stage.