AI & Data · Foundation+
Machine Learning Concepts
Build intuition first. Compare evidence. Choose the right model.
Understand how machine-learning models think, fail and improve.
Advanced layer includedIndustry Edge tools and scenario-based labs beyond the recognized core. Curriculum v1.1 • Updated Jul 2026
Recognized curriculum, prerequisites, assessments, and public fees disclosed before counselling.
Industry Edge Lab includedAdvanced tools, emerging patterns, and scenario-based labs beyond the recognized core.Registration and admissions follow-up continue securely in BharatCampus ONE.
Included beyond the published curriculum
The recognized core is your foundation—not the finish line.
Every Machine Learning Concepts cohort also enters the CohortAI Industry Edge Lab: an evolving layer of advanced tool categories, emerging architecture and production patterns, and scenario-based labs. A guided counselling preview connects the most relevant capabilities to your target role, followed by a release-governed lab sequence after enrollment.
Release-governed advanced tools Emerging architecture patterns Scenario-based labs
Fit before enrollment
Who should join
Python learners, analysts and graduates seeking a rigorous bridge into data science or applied machine learning.
Role pathways
Your starting point
Python basics and high-school mathematics. A mathematics and statistics bridge is included.
This is not an exclusion filter. It helps us confirm whether you can start directly or would benefit from a short bridge before the cohort.Recognized core
Skills and technology stack
These employability foundations are part of the public learning promise and remain visible before registration.
Python NumPy pandas scikit-learn Jupyter Matplotlib Model evaluation Explainability
Build-first curriculum journey
8 missions. One defensible portfolio.
Mentor-led hybrid learning with 1.5-hour live instruction, all-day supervised practice access, weekly build evidence and a version-locked capstone.
M011 week · 7.5 live hours
Mathematics and statistics bridge
Math Without Fear: explain each concept using a model behaviour example.
- Vectors, matrices and functions
- Derivatives and optimisation intuition
- Probability and distributions
- Mean, variance and covariance
Guided labUse visual notebooks to connect mathematical concepts to model behaviour.
Portfolio evidenceMath intuition notebook and concept map.
AssessmentApplied reasoning quiz and notebook viva.
M021 week · 7.5 live hours
Data preparation and feature basics
Leakage Hunter: find the feature that makes the model suspiciously perfect.
- Data types and quality
- Missing values and outliers
- Encoding and scaling
- Train/validation/test splits
Guided labPrepare a noisy dataset using a reproducible pipeline without leaking target information.
Portfolio evidencePreprocessing pipeline and leakage audit.
AssessmentData-preparation practical.
M032 weeks · 15 live hours
Regression methods
Residual Detective: use the mistakes to discover what the model has not learned.
- Linear regression intuition
- Loss functions and residuals
- Regularisation
- Non-linear and tree-based regression
Guided labPredict a continuous business outcome and compare interpretable and non-linear models.
Portfolio evidenceRegression report, residual analysis and recommendation.
AssessmentMetric and residual interpretation assessment.
M042 weeks · 15 live hours
Classification methods
Threshold Board: choose the action point, not merely the model score.
- Logistic regression and probabilities
- k-nearest neighbours and margin concepts
- Decision boundaries
- Confusion matrix and class metrics
Guided labBuild a risk classifier and set a decision threshold based on business costs.
Portfolio evidenceClassification notebook, cost matrix and threshold policy.
AssessmentThreshold and calibration review.
M051 week · 7.5 live hours
Trees and ensemble models
Ensemble Arena: improve performance without losing reproducibility or trust.
- Decision-tree splitting and pruning
- Bagging and random forests
- Boosting intuition
- Bias/variance trade-offs
Guided labRun a controlled ensemble model tournament with cross-validation and explainability.
Portfolio evidenceExperiment table, tuning log and model-selection memo.
AssessmentEnsemble comparison viva.
M061 week · 7.5 live hours
Unsupervised learning
Hidden Groups Lab: prove the segments are useful rather than merely colourful.
- Clustering objectives and distance
- K-means and hierarchical clustering
- Density-based concepts
- Dimensionality reduction
Guided labCreate and validate actionable segments from behavioural data.
Portfolio evidenceSegmentation report and visual cluster narrative.
AssessmentUnsupervised validation and actionability review.
M071 week · 7.5 live hours
Evaluation and explainability
Model Court: present both the case for deployment and the strongest case against it.
- Cross-validation and uncertainty
- Metric selection and baselines
- Learning curves and error analysis
- Global and local explanations
Guided labAudit a model across performance, slices, explanations and inappropriate-use scenarios.
Portfolio evidenceModel card, explanation report and go/no-go recommendation.
AssessmentResponsible-ML review board.
M081 week · 7.5 live hours
Applied ML mini capstone
Evidence Demo: show why the chosen model is useful, reliable and appropriately limited.
- Problem definition and metric
- Data preparation
- Model comparison
- Evaluation and explanation
Guided labSolve a selected prediction or segmentation problem and communicate the recommended action.
Portfolio evidenceMini-capstone repository, model card, report and demo.
AssessmentPortfolio review and ML interview simulation.
The week feels different here
A repeatable rhythm from concept to evidence
Weekly rhythm
- 1Concept sprint and visual roadmap
- 2Guided implementation lab
- 3Independent build challenge
- 4Debug, review and production-pattern clinic
- 5Skill check, demo and learning reflection
Learner experience
CohortAI Skill Passport with milestone badges
Build-first weekly checkpoints rather than lecture-only completion
Peer demo and code/design review rituals
Mentor office hours and all-day practice-lab access
Capstone Demo Day with a business narrative, technical walkthrough and interview-style defence
Responsible AI-assisted learning policy: explain, verify and own every submitted artifact
Interview-ready proof
What you will be able to show—not merely claim
These are tangible outputs you can demonstrate, explain, and defend through a technical walkthrough, business narrative, or interview conversation.
Regression case
Classification case
Ensemble comparison
Clustering case
Applied ML mini capstone
Transparent evaluation
How your progress is assessed
Completion standard
Minimum 70% overall, mandatory capstone pass, at least 80% lab submissions and versioned portfolio evidence.
CohortAI Industry Edge Lab
Your published curriculum is the foundation. The Industry Edge is what keeps it moving forward.
The Advanced Modelling Lab explores gradient boosting, efficient tuning, explainability and fairness through evidence-led model comparisons.
Your counselling preview connects relevant capability categories to your target role. The complete release-governed lab sequence becomes part of the enrolled learning plan.Version-controlled learning promise
ML-CONCEPTS@1.1.0
Released 24 July 2026 under catalog CAT-2026.09.1. This page presents the approved curriculum, learner outcomes, duration, and public fee for informed comparison.
Straight answers
Frequently asked questions
Who is the Machine Learning Concepts program designed for?
Python learners, analysts and graduates seeking a rigorous bridge into data science or applied machine learning.
What prerequisites do I need?
Python basics and high-school mathematics. A mathematics and statistics bridge is included.
What is included in the public curriculum?
The public curriculum includes the recognized stack, all 8 module themes, guided labs, portfolio artifacts, assessment model, duration, and public fees. The Industry Edge Preview explains how advanced capability categories extend this foundation for your target role.
Where do counselling and enrollment happen?
CohortAI supports public discovery and program comparison. Registration, consent, counselling follow-up, application updates, and enrollment are managed securely in BharatCampus ONE.