Certified AI & ML Engineer
Master the Engineering of Intelligence in 6 Months
Artificial Intelligence has become the driving force of the modern world. From personalized recommendations and medical diagnostics to self-driving vehicles and generative models like ChatGPT, AI is transforming how we live, work, and innovate. Behind these breakthroughs are not data analysts who merely interpret numbers, but AI Engineers who understand how to make machines learn, reason, and evolve.

The Certified AI & ML Engineer (CAME) program by the Institute of Software Engineering is built for this new generation of creators. It's a six-month, immersive journey designed to turn your curiosity into mastery, to help you not just understand AI, but engineer it.
What You'll Master
CAME is not just about "learning tools." It's about understanding the
science and engineering of
intelligence.
You'll begin by mastering Python for Data Science, then
build up from mathematical foundations, Linear
Algebra, Probability, and Calculus, that fuel
every machine learning model. From there, you'll dive deep
into the algorithms that drive intelligent systems.
Python & Mathematical Foundations
Master Python for Data Science alongside essential mathematical concepts, Linear Algebra, Probability, and Calculus, that power every machine learning model. Build the fundamental knowledge required to understand and implement AI algorithms from scratch.
Supervised & Unsupervised Learning
Learn how models like Linear Regression, Logistic Regression, and Decision Trees predict outcomes and classify data. Discover algorithms like K-Means Clustering and PCA that find hidden patterns in unlabelled data, powering customer segmentation and anomaly detection.
Advanced Ensemble Models
Explore powerful algorithms like XGBoost, LightGBM, and CatBoost, the same techniques behind many Kaggle competition winners. Master the advanced methods used by top data scientists to achieve state-of-the-art performance in competitive machine learning.
Deep Learning & Neural Networks
Master Neural Networks, CNNs, and RNNs, understanding how they enable machines to see, hear, and understand. Build and train deep learning models that power computer vision, speech recognition, and complex pattern recognition tasks.
Natural Language Processing
Build models that understand human language using Transformers, BERT, and GPT. Learn to fine-tune these powerful models for real-world applications, from sentiment analysis to question answering and text generation.
Generative AI & AI Agents
Learn to build systems that not only predict but create, from generating images with diffusion models to designing autonomous agents powered by LLMs and LangChain. Master the cutting-edge technologies shaping the future of AI.
This progression ensures you evolve from understanding what AI does to mastering how it does it, and ultimately, how to make it work for you.
From Models to Mastery
Most courses stop at "training models." CAME goes beyond that, to the engineering level. You'll learn how to containerize and deploy models using Docker, how to track experiments with MLflow, and how to automate entire ML pipelines using CI/CD principles. You'll also explore the emerging frontier of Retrieval-Augmented Generation (RAG), systems that allow LLMs to use private data, and Multimodal AI, where text, images, and audio converge. By the end of the program, you won't just be able to train models, you'll know how to design, optimize, deploy, and manage them at scale.
Key Benefits & Outcomes
Become a Full-Stack AI Engineer
Gain end-to-end expertise, from data preprocessing to MLOps and deployment. Master the complete AI lifecycle and position yourself for top-tier roles such as AI Engineer, Machine Learning Engineer, NLP Engineer, or MLOps Engineer.
Understand the "Why" Behind AI
Learn from first principles; implement algorithms like Gradient Descent and Backpropagation from scratch. Understand not just what AI does, but how and why it works, giving you the deep knowledge needed to innovate and solve complex problems.
Master the Tools of the Future
Work with industry-standard technologies including TensorFlow, PyTorch, Hugging Face, Docker, LangChain, and more. Build production-ready systems using the same tools and frameworks used by leading AI companies today.
Build a Professional Portfolio
Graduate with a portfolio of production-ready projects that showcase your technical depth. CAME doesn't just give you a certificate, it gives you industry credibility. It's proof that you can handle complex AI systems, deploy them, and make them work in production.
Who should enroll?
CAME is designed for anyone with ambition and logical thinking. Whether you're:
- A Software Developer ready to transition into the world of Artificial Intelligence and Machine Learning
- A Data Analyst or Scientist ready to move beyond dashboards to full-stack AI systems
- A Graduate or Undergraduate from Computer Science, Engineering, or related fields
- A Career Changer eager to future-proof your skills in one of the world's fastest-growing fields
No matter your starting point, if you have the drive to learn and the passion to build intelligent systems that make an impact, CAME will take you there.
No advanced math background?
No problem. Our AI & ML Engineering Foundations module is designed to bring you up to speed from the ground up, focusing on the essential mathematical foundations that power Artificial Intelligence and Machine Learning.
You’ll gradually develop a clear and practical understanding of Linear Algebra, Probability, Statistics, and Calculus, the core pillars behind algorithms such as Gradient Descent, Neural Networks, and Decision Trees. Rather than overwhelming you with theory, we focus on building strong mathematical intuition through hands-on examples and visual explanations, helping you see how each formula connects to real-world machine learning problems.
By the end of this module, you’ll confidently understand how math drives model training, optimization, and intelligent decision-making, a skill set that sets true AI & Machine Learning Engineers apart from those who just use pre-built tools.
Your Future Starts Here
This is more than a learning experience, it's a transformation. By the end of this program, you'll speak the language of AI fluently, understand its logic intuitively, and apply its power confidently.
You won't just keep up with the AI revolution, you'll help build it.
The Certified AI & ML Engineer (CAME) program is structured as a 6-module learning journey that builds your expertise step by step, from coding and mathematics to advanced AI systems and production deployment. Each module combines theory, hands-on labs, and real-world projects so that by the end, you'll graduate not just with knowledge, but with an impressive portfolio that proves you can build and deploy intelligent systems.
The program progresses logically from foundations through advanced techniques to production engineering. You'll start by mastering Python for Data Science and mathematical foundations, then dive deep into machine learning algorithms, advance to deep learning and computer vision, explore the language revolution with NLP and LLMs, and culminate with production engineering including MLOps, Generative AI, and AI Agents.
Program Curriculum
Module 1: AI & ML Engineering Foundations
Build a strong foundation in Python programming and the mathematical concepts essential for AI. Master NumPy for numerical computation, Pandas for data manipulation, and core mathematical foundations including Linear Algebra, Probability, and Calculus that power every machine learning model.
Core Topics
- Python Fundamentals & Object-Oriented Programming
- NumPy for numerical computation and array operations
- Data manipulation with Pandas & visualization with Matplotlib/Seaborn
- Linear Algebra: Vectors, matrices, eigenvalues, transformations
- Probability & Statistics: Bayes' Theorem, probability distributions
- Calculus Essentials: Derivatives, gradients, and the chain rule
Tools & Libraries
- Python, NumPy, Pandas
- Matplotlib, Seaborn
Key Projects
- Exploratory Data Analysis (EDA) on real-world dataset
- Implement Gradient Descent algorithm from scratch using NumPy
Module 2: Core Machine Learning & Model Training
Master fundamental machine learning algorithms and the complete model training lifecycle. Learn supervised learning with Linear and Logistic Regression, understand the bias-variance tradeoff, explore tree-based classifiers, and discover unsupervised learning through clustering algorithms.
Core Topics
- Linear Regression: Simple and multiple models, hypothesis representation
- Loss functions: RSS, RMSE, and model evaluation
- Model training lifecycle: Bias-variance tradeoff, cross-validation
- Regularization techniques: L1 (Lasso) & L2 (Ridge)
- Logistic Regression & classification: Sigmoid function, decision boundaries
- Classification metrics: Accuracy, precision, recall, F1-score, confusion matrix
- Tree-based models: Decision trees, ensemble methods
- Unsupervised learning: K-Means clustering, elbow method
Tools & Libraries
- Scikit-learn, NumPy
- Matplotlib, Seaborn
Key Projects
- Advertising sales prediction using regression
- Sentiment classifier with logistic regression
- Customer churn prediction using tree-based classifiers
- Document clustering and similarity retrieval
Module 3: Advanced Algorithms & Competitive ML
Explore high-performance algorithms used in competitive machine learning. Master dimensionality reduction with PCA, learn powerful gradient boosting machines like XGBoost, LightGBM, and CatBoost, and apply advanced feature engineering in a Kaggle-style competition.
Core Topics
- Curse of Dimensionality and its implications
- Principal Component Analysis (PCA) for feature extraction
- Advanced ensemble models: Principles of boosting
- Gradient Boosting Machines: XGBoost, LightGBM, CatBoost
- Hyperparameter tuning for optimal performance
- Advanced feature engineering techniques
- Model selection and ensembling strategies
- Competitive data science methodologies
Tools & Libraries
- Scikit-learn, NumPy, Pandas
- XGBoost, LightGBM
Key Projects
- Image compression using PCA
- Complex dataset analysis with gradient boosting
- Full Kaggle-style competition with advanced feature engineering
Module 4: Deep Learning & Computer Vision
Master neural networks and computer vision with CNNs. Understand how neural networks learn through forward and backpropagation, build convolutional neural networks for image tasks, and leverage transfer learning with pre-trained models like ResNet for complex computer vision problems.
Core Topics
- Neural network architecture: Biological vs artificial neurons
- Activation functions: Sigmoid, Tanh, ReLU
- Building multi-layer network architectures
- Forward & backpropagation algorithms
- Optimizers: Adam, SGD for minimizing loss
- Handling vanishing/exploding gradients
- Convolutional Neural Networks: Convolution operation, pooling layers
- Hierarchical feature learning from images
- Transfer learning with pre-trained models like ResNet
Tools & Libraries
- TensorFlow / PyTorch
- NumPy
- Hugging Face
Key Projects
- Build a simple neural network from scratch
- MNIST digit classification with TensorFlow/PyTorch
- CNN for image classification task
- Custom image classifier using transfer learning
Module 5: The Language Revolution: NLP & LLMs
Build models that understand and generate human language using modern NLP techniques. Master text preprocessing and vectorization, learn RNN and LSTM architectures for sequential data, understand the revolutionary Transformer architecture, and fine-tune large language models like BERT and GPT.
Core Topics
- Text preprocessing: Tokenization, stemming, lemmatization
- Classical vectorization: Bag-of-Words, TF-IDF
- Word embeddings: Word2Vec concepts
- Recurrent Neural Networks (RNN) architecture
- Long Short-Term Memory (LSTM) networks for sequential data
- Transformer architecture: Self-attention mechanism
- "Attention Is All You Need" - Encoder-decoder models
- Large Language Models: Understanding BERT/GPT
- Prompt engineering techniques
- Fine-tuning pre-trained models with Hugging Face
Tools & Libraries
- Scikit-learn, NLTK, spaCy
- TensorFlow / PyTorch
- Hugging Face Transformers
Key Projects
- Text classification using traditional NLP techniques
- Sentiment analysis with RNNs and LSTMs
- Transformer model attention mechanism walkthrough
- Fine-tune Hugging Face LLM for custom task
Module 6: Production Engineering: MLOps, Generative AI & Agents
Deploy AI systems in production and explore the cutting edge of generative AI and autonomous agents. Learn Retrieval-Augmented Generation (RAG), multimodal AI, containerization with Docker, MLOps practices with MLflow, and build autonomous AI agents. Culminate with a capstone project integrating all learned skills.
Core Topics
- Retrieval-Augmented Generation (RAG) architecture
- Vector embeddings & vector databases (ChromaDB, FAISS)
- Text chunking and retrieval strategies
- Multimodal models: Understanding text and images
- Generative AI: Diffusion models for image generation
- MLOps principles and best practices
- Containerization with Docker
- Deploying models as REST APIs (Flask/FastAPI)
- Experiment tracking with MLflow
- CI/CD for ML pipelines
- Agentic framework: Reasoning, planning, and tool-use
- Using LLMs as reasoning engines for agents
- Connecting agents to external tools and APIs
Tools & Libraries
- LangChain, LlamaIndex
- Hugging Face, Docker
- Flask / FastAPI
- MLflow, Git
Key Projects
- Build custom Q&A bot using RAG
- Visual Question Answering (VQA) with multimodal models
- Containerize and deploy a model
- Deploy sentiment analysis model as web service
- Build a simple tool-using agent
- Capstone: Build a "code interpreter" AI agent that answers questions about datasets by writing and executing Pandas code
Course Schedule, Duration & Next Intake
Lectures are conducted on Sundays from 8:30 AM to 5:00 PM, providing a comprehensive full-day learning experience that balances theoretical understanding with hands-on implementation. The program is available in both online and on-campus modes, offering flexibility to suit your learning preferences.
Panadura Branch
Commencing on 2nd November
8:30 AM - 5:00 PM | Sundays
* Online & On-Campus
Course Fees & Payment Plans
The Certified AI & ML Engineer (CAME) program offers flexible payment options to make your AI education more accessible. We accept credit/debit cards, including installment plans via credit cards, as well as cash payments. For direct bank deposits, please contact IJSE via our hotline to confirm current fees and receive payment instructions, as fees may be periodically updated. Invest in your future with one of Sri Lanka's most comprehensive AI & ML engineering programs.



Minimum Entry Requirements
Applicants are expected to have previous experience in at least one programming language and a basic understanding of mathematics, including fundamental concepts such as algebra and logic. The program welcomes individuals with ambition and analytical thinking, whether you’re a software developer, data analyst, graduate, or career changer looking to enter the exciting world of Artificial Intelligence and Machine Learning.
Online Application
If you're interested in joining the Certified AI & ML Engineer, please fill out the
form below with your name and contact number, then click the submit button. Once we
receive your application, a representative from the Institute of Software Engineering
will get in touch with you as soon
as possible to guide you through the next steps.
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number
is correct before submitting)