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Artificial Intelligence

Duration
4 months
Delivery
Fully online
Total learning
420 hours

Career Outcomes

Career Opportunities & Progression

  • Junior AI Developer
  • AI Solutions Assistant
  • Junior Data Analyst
  • Machine Learning Assistant
  • Python Automation Assistant
  • Generative AI Application Trainee

Before you apply

Your programme at a glance

Apply / enquire
Qualification
Certificate (non-NVQ)
Awarding institution
NextGen Campus · Academic Transcript
Duration
Four months (16 teaching weeks)
Delivery
Fully online – live Saturday lectures, Sunday guided real labs delivered and monitored through the NextGen LMS, weekday LMS laboratories, the Student Project Book and final-month online capstone classes
Class schedule
Saturday 1.30 PM – 5.30 PM (live online lectures) - Sunday 9.00 AM – 6.00 PM (guided real labs on the LMS, 1-hour lunch)
Lecture / guided learning
192 hours – instructor-led
LMS laboratory hours
148 hours – Python, data, machine-learning, deep-learning and generative-AI labs in Google Colab via the NextGen LMS
Student Project Book
40 hours – including the Management Module
Modules
8 taught modules
Entry requirements
G.C.E. O/L with Mathematics and English passes, plus basic computer literacy. Python is taught from the beginning; an online interview confirms readiness.
Next intake
Confirm available batches with admissions

Class schedules follow the syllabus; confirm the batch timetable with admissions.

Complete Programme Map

Course Content Architecture

8 modules across 4 learning phases. Select a module to read its full curriculum.

8Taught modules
4Learning phases
46Syllabus topics
10Career directions
Four months (16 teaching weeks)Programme duration
NextGen CampusAwarding institution
01 · Programme foundations
02 · Learning domains
03 · Hands-on practice
Set up Colab, GitHub and the LMSKaggle Learn Python exercisesClean a messy retail datasetChurn classification modelDigit classifier in KerasInvoice data extraction with a test setHR policy RAG assistantRed-team and harden an AI app

Guided laboratories, practical activities and a Student Project Book build evidence of your learning.

04 · Project, portfolio & progression
Professional portfolioDocumented laboratories, project evidence and the Management Module
Career directionsJunior AI Developer · AI Solutions Assistant · Junior Data Analyst · Machine Learning Assistant · Python Automation Assistant
Further opportunitiesGenerative AI Application Trainee · Chatbot Developer Trainee · AI Model Evaluation Assistant · Prompt and Automation Assistant · AI Application Support Assistant
DeliveryFully online – live Saturday lectures, Sunday guided real labs delivered and monitored through the NextGen LMS, weekday LMS laboratories, the Student Project Book and final-month online capstone classes
AssessmentContinuous assessment, practical work, final project and Student Project Book
CompletionNextGen Campus qualification and Academic Transcript on successful completion

8 modules · Full syllabus

Complete Curriculum

Explore every phase of the programme, then expand the modules below for the complete syllabus, outcomes and practical work.

Phase 1 — AI & Data Foundations

  1. AI Foundations & Responsible AI
  2. Python Programming for AI
  3. Data Handling, Analysis & Visualisation

Phase 2 — Machine Learning & Deep Learning

  1. Machine Learning Fundamentals
  2. Deep Learning, Computer Vision & NLP

Phase 3 — Generative AI Engineering

  1. Generative AI & Prompt Engineering
  2. Building AI Applications: RAG, Agents & Deployment

Phase 4 — Responsible AI & Career Readiness

  1. AI Security, Ethics, Governance & Career Readiness

Phase 1 — AI & Data Foundations

M01AI Foundations & Responsible AI24 lecture h · 18 lab h

Builds a clear, hype-free understanding of AI and how business uses it, and establishes responsible, effective use of AI assistants from the first week.

Syllabus Content

  • AI definitions and types of learning
  • AI use cases by industry
  • Limits of AI and hallucination
  • Responsible AI principles
  • Prompt structure and patterns
  • PDPA and data you must not share

Learning Outcomes

  • Explain AI, ML, deep learning and generative AI
  • Identify realistic AI use cases in Sri Lankan industries
  • Apply the six responsible-AI principles
  • Write structured, reliable prompts and protect personal data

LMS Laboratories

  • Set up Colab, GitHub and the LMS
  • Compare AI assistants on business tasks
  • Build a tested prompt library

AI Integration

  • AI assistants used critically on real business tasks

Syllabus alignment: AI-901 aligned

M02Python Programming for AI24 lecture h · 19 lab h

Teaches Python from the beginning — the core language of AI — through practical automation and data tasks.

Syllabus Content

  • Python syntax and data types
  • Lists, dictionaries and control flow
  • Functions and debugging
  • Files, CSV and JSON
  • Web APIs with requests
  • NumPy introduction

Learning Outcomes

  • Write Python using variables, data structures, conditions, loops and functions
  • Read and write files and call web APIs
  • Handle errors and use libraries
  • Automate a repetitive business task

LMS Laboratories

  • Kaggle Learn Python exercises
  • Automate a monthly report merge
  • Call a currency-rate API

AI Integration

  • AI-assisted code explanation and debugging, with learners explaining every line

Syllabus alignment: PCEP aligned

M03Data Handling, Analysis & Visualisation24 lecture h · 19 lab h

Develops data-analyst skills — cleaning, analysing and visualising real datasets with pandas, matplotlib and SQL.

Syllabus Content

  • DataFrames and cleaning
  • Grouping, merging and aggregation
  • matplotlib and seaborn
  • SQL SELECT, JOIN, GROUP BY
  • Exploratory data analysis
  • Storytelling with data

Learning Outcomes

  • Load, clean and transform data with pandas
  • Group, aggregate and join datasets
  • Choose and build effective charts
  • Query data with SQL and present findings

LMS Laboratories

  • Clean a messy retail dataset
  • EDA of a telecom churn dataset
  • SQL exercises in SQLite

AI Integration

  • AI-assisted analysis checked against the data

Syllabus alignment: Kaggle Learn pathway

Phase 2 — Machine Learning & Deep Learning

M04Machine Learning Fundamentals24 lecture h · 19 lab h

Builds the standard machine-learning workflow — training, evaluating and improving regression, classification and clustering models with scikit-learn.

Syllabus Content

  • Features, labels and the ML workflow
  • Linear and logistic regression
  • Decision trees and random forests
  • Evaluation metrics and overfitting
  • Cross-validation and tuning
  • k-means and PCA

Learning Outcomes

  • Apply the ML workflow and train-test split
  • Train regression and classification models
  • Evaluate with accuracy, precision, recall and F1
  • Tune models and segment data with k-means

LMS Laboratories

  • Churn classification model
  • Model tuning exercise
  • Customer segmentation

AI Integration

  • Model explanation and bias checks

Syllabus alignment: Kaggle Learn pathway

M05Deep Learning, Computer Vision & NLP24 lecture h · 19 lab h

Introduces neural networks and applies deep learning to images and text using transfer learning and pre-trained transformer models.

Syllabus Content

  • Neurons, layers and training
  • Overfitting, dropout and early stopping
  • CNNs and transfer learning
  • Embeddings and transformers
  • Hugging Face pipelines

Learning Outcomes

  • Explain how neural networks learn
  • Build and train networks with Keras on a GPU
  • Build image classifiers with transfer learning
  • Classify text and sentiment with transformers

LMS Laboratories

  • Digit classifier in Keras
  • Product-image classifier
  • Review sentiment analysis

AI Integration

  • Pre-trained models from the Hugging Face Hub

Syllabus alignment: Kaggle Learn - Hugging Face

Phase 3 — Generative AI Engineering

M06Generative AI & Prompt Engineering24 lecture h · 18 lab h

Develops skills to control large language models precisely and build applications with LLM APIs — chat, extraction, summarisation and multimodal tasks.

Syllabus Content

  • How LLMs generate text
  • System prompts and few-shot prompting
  • Structured JSON output
  • LLM APIs and conversation history
  • Summarisation and classification pipelines
  • Cost and API key security

Learning Outcomes

  • Explain tokens, context windows and LLM parameters
  • Apply advanced prompting and structured output
  • Build a chat application with an LLM API
  • Manage API cost, limits and key security

LMS Laboratories

  • Invoice data extraction with a test set
  • Customer-support chatbot
  • Batch document summariser

AI Integration

  • Institute-provisioned LLM API keys with spending limits

Syllabus alignment: AI-901 aligned

M07Building AI Applications: RAG, Agents & Deployment24 lecture h · 18 lab h

Builds grounded AI assistants with retrieval-augmented generation, gives applications safe tool use, and deploys them as web apps.

Syllabus Content

  • Embeddings and vector databases
  • Chunking and RAG pipelines
  • Agents and function calling
  • Human approval for sensitive actions
  • Streamlit deployment
  • Git and GitHub workflow

Learning Outcomes

  • Build a RAG assistant that cites its sources
  • Build an agent that calls tools safely
  • Build and deploy a Streamlit app
  • Manage code and secrets with Git and GitHub

LMS Laboratories

  • HR policy RAG assistant
  • Sales-database agent
  • Deploy a Streamlit app

AI Integration

  • LangChain, ChromaDB and Streamlit

Syllabus alignment: AI-901 aligned

Phase 4 — Responsible AI & Career Readiness

M08AI Security, Ethics, Governance & Career Readiness24 lecture h · 18 lab h

Secures and governs AI applications and prepares learners for employment with a professional portfolio and interview practice.

Syllabus Content

  • OWASP Top 10 for LLM Applications
  • Guardrails and output filtering
  • PDPA No. 9 of 2022
  • NIST AI Risk Management Framework
  • Portfolio, CV and interviews

Learning Outcomes

  • Test and mitigate prompt injection and data leakage
  • Apply PDPA obligations to an AI solution
  • Describe AI governance with the NIST AI RMF
  • Present a professional AI portfolio

LMS Laboratories

  • Red-team and harden an AI app
  • Risk and data-protection assessment
  • Portfolio README and mock interview

AI Integration

  • The dedicated responsible-AI module

Syllabus alignment: OWASP LLM Top 10 - NIST AI RMF

Industry certification alignment is syllabus guidance. External examinations are separate; course completion does not award vendor certification.

Learning outcomes

Skills You Will Develop

  • Explain what AI, machine learning, deep learning and generative AI are, and where each is used in business.
  • Write Python programs to automate tasks and process data.
  • Clean, analyse and visualise data with pandas, matplotlib and SQL.
  • Train, evaluate and improve supervised and unsupervised machine learning models.
  • Build and train neural networks for image and text tasks.
  • Use large language models effectively through prompt engineering and APIs.
  • Build generative AI applications with retrieval-augmented generation and simple agents, and deploy them as web apps.
  • Identify and mitigate AI risks, including bias, prompt injection and data leakage, and apply Sri Lanka's Personal Data Protection Act.
  • Design, build, document and present a complete AI solution for a real business scenario.

Career Outcomes

Career Opportunities

Junior AI DeveloperAI Solutions AssistantJunior Data AnalystMachine Learning AssistantPython Automation AssistantGenerative AI Application TraineeChatbot Developer TraineeAI Model Evaluation AssistantPrompt and Automation AssistantAI Application Support Assistant

Potential roles to work towards, depending on your practical portfolio, experience and employer requirements. Some roles require further training or certification.

Tools & Platforms

Technologies used

  • Python
  • pandas and matplotlib
  • SQL
  • Google Colab
  • Large language model APIs
  • Machine-learning and generative-AI tools

Labs & practicals

How you will learn

  • Live online teaching and guided online practical activities
  • LMS module labs with trainer feedback
  • A Student Project Book documenting your learning
  • A final project, presentation and practical assessment

Admission requirements

Before you start

G.C.E. O/L with Mathematics and English passes, plus basic computer literacy. Python is taught from the beginning; an online interview confirms readiness.

You will need a computer and reliable internet for online classes and LMS activities.

NextGen Campus support

Student support

  • Academic guidance from your course coordinator
  • Trainer feedback on LMS laboratory work
  • Project planning and documentation support
  • LMS help for login and technical questions

Put your learning into practice

Projects & assessment

Build an AI application or AI web-based system for a real business scenario, then document and present your solution online.

Continuous assessment
40%
Practical / final project
40%
Student Project Book
20%

Complete the required practical work and project submissions. The handbook sets out attendance, pass requirements, grading and re-sit arrangements.

Capstone Project — 40 Hours (Online, Trainer-Monitored)

AI Solution Build — Apps, Web and Intelligent Automation

In the final month (weeks 13–16) learners attend extra online capstone classes — 40 hours in total, about 10 hours a week — in which each learner builds a complete AI application: an AI app, an AI web-based system, a chatbot, an agent or an intelligent automation, chosen from the capstone catalogue for a realistic Sri Lankan business scenario. Learners design, build, test, secure and deploy the solution, then present and defend it to a live online panel in week 16.

Capstone Deliverables

  • Approved proposal with problem statement, users and success measures
  • Solution design: architecture, interface, data sources and evaluation plan
  • Working, deployed AI app, web application or automation, with code on GitHub
  • Evaluation results, security test results and PDPA assessment
  • Technical documentation, user guide and project README
  • Live online panel presentation, demonstration and technical defence

Student Project Book & Management Module

The Student Project Book is each learner's professional record, kept throughout the course (about 2.5 hours a week). Learners record their daily activities — labs completed, code written, problems solved and decisions made — and submit a project plan every week on the LMS, which the trainer reviews. The Project Book also contains the Management Module and becomes the full record of the capstone build.

  • MU1 AI Project Management & Agile Scoping AI projects, sprints, backlogs and success measures
  • MU2 Data Protection & Responsible-AI Policy PDPA obligations, consent, data minimisation and AI use policies
  • MU3 Technical Writing & Documentation READMEs, model cards and user guides
  • MU4 AI Cost Management Token and API budgeting, cloud GPU cost and optimisation
  • MU5 Interview Skills and the AI Job Market CV, LinkedIn and GitHub portfolio; technical interviews; local and overseas markets

Payment Options

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Total course feeLKR 18,000Course tuition
Published course fee

Course payment

LKR18,000

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Installment arrangements

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Registration feeLKR 3,000 (one-time)
  • Lecturer-led learning and guided practical work
  • LMS laboratories and Student Project Book
  • Programme project and assessment

Course tuition and the one-time registration fee are listed separately. Confirm payment dates and installment availability with admissions. External professional certification examination fees are separate unless NextGen Campus specifically states otherwise.

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