About the programme
Contact JNTE or an authorised training centre for the detailed programme outline, practical requirements and current admission schedule.
Subjects & learning path
6 subjects · 60 topics · 600 marks. Read each topic, pass its test and complete the required practical reviews.
1. Programming Foundations for Artificial Intelligence · 100 marks
- Algorithms and executable instructions
- Python values and expressions
- Conditions and branching
- Iteration and accumulated results
- Functions and explicit interfaces
- Collections and structured records
- Text files and structured interchange
- Exceptions and defensive input handling
- Tests and debugging evidence
- Modular programming project · Qualifying practical
2. Mathematical Reasoning and Responsible Data · 100 marks
- Vectors and matrix representations
- Descriptive statistics and variation
- Probability and conditional evidence
- Sampling and uncertainty
- Data quality and missing values
- Feature representation and engineering
- Training, validation, and test separation
- Scaling and preprocessing pipelines
- Exploratory analysis and relationships
- Dataset documentation and responsible use · Qualifying practical
3. Search, Knowledge Representation and Intelligent Agents · 100 marks
- Intelligent agents and problem formulation
- Uninformed search strategies
- Heuristics and informed search
- Logic and knowledge representation
- Rule based inference and explanations
- Constraint satisfaction and scheduling
- Probabilistic reasoning with graphical models
- Decision making under uncertainty
- Reinforcement learning in bounded simulations
- Tool using AI systems and controlled workflows · Qualifying practical
4. Learning from Data and Predictive Reasoning · 100 marks
- Supervised learning and regression
- Classification and decision thresholds
- Decision trees and model complexity
- Ensembles and combined predictions
- Distance based and margin methods
- Clustering and unsupervised structure
- Dimensionality reduction and representation
- Classification metrics and error analysis
- Cross validation and tuning discipline
- Interpretation and model monitoring · Qualifying practical
5. Neural and Generative AI Applications · 100 marks
- Neural network building blocks
- Loss functions and gradient optimization
- Training loops and generalization controls
- Image representations and convolution
- Text processing and embeddings
- Attention and transformer representations
- Generative models and decoding
- Prompt design and structured output
- Retrieval augmented generation
- Generative system evaluation and safeguards · Qualifying practical
6. Applied AI Project and Professional Practice · 100 marks
- Problem framing and requirements
- Version control and change history
- Verification strategy and evidence
- Readable code and technical debt
- Risk registers and ethical design
- Reproducible experiments and comparison
- Technical documentation and limitations
- Planning and reviewable increments
- Release readiness and maintenance
- Applied AI capstone and human oversight · Qualifying practical
Registered students receive approved learning access after payment confirmation. Exam mode and access dates are managed through their account.
Additional modules & syllabus
Additional course materials will be provided through your account.
Before enrolling
Check your centre’s current authorisation status, review the official fee schedule, and retain your payment receipts.