Applied AI Engineer Program
100-Hour Practical AI & Product Development Pathway
Move beyond simply using AI tools. Build real AI-powered applications and product prototypes. Designed to take learners from core Python programming and data science fundamentals to Large Language Models, AI APIs, Retrieval-Augmented Generation (RAG), and deployable product prototypes.
Industry-Oriented Applied AI Pathway
The program is specifically designed for learners who want to move beyond simply using AI tools and develop the technical understanding and hands-on execution skills required to build real AI-powered applications, integrate model APIs, construct RAG workflows, and deliver capstone prototypes.
The Applied AI Engineer Program is a 100-hour industry-oriented program designed to develop practical and applied competencies in Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, and modern AI application development. The program follows a structured learning pathway that takes learners from the fundamentals of Artificial Intelligence and Python programming to Data Science, Machine Learning, Deep Learning, Large Language Models, AI API integration, and Retrieval-Augmented Generation. Learners are also introduced to Responsible AI, AI security, and AI product development, helping them understand how modern AI-powered applications and intelligent solutions are designed and developed.
Course Highlights
What Will You Learn?
Eight core learning outcomes that prepare you for live AI engineering & product development roles.
Core AI & GenAI Concepts
Explain core AI, Machine Learning, Deep Learning, and Generative AI concepts with clarity.
Python for AI Development
Develop clean Python programs, manage environments, and work with APIs & JSON data.
Data Preparation & Analytics
Prepare, analyse, and visualise complex data using modern Data Science tools (NumPy, Pandas, Matplotlib).
ML & Deep Learning Modeling
Develop, evaluate, and fine-tune Machine Learning and introductory Deep Learning models using Scikit-learn & TensorFlow.
LLMs & Prompt Engineering
Master Large Language Models, prompt techniques, context windows, and structured output generation.
AI API & Application Building
Integrate commercial & open-source AI APIs to construct responsive AI-driven web applications.
Vector Search & RAG Architecture
Build Retrieval-Augmented Generation workflows using document chunking, embeddings, and vector databases.
Responsible AI & Capstone Prototype
Apply Responsible AI & security principles while executing an end-to-end Capstone AI Product Prototype.
AIPAX Learning Pathway
A 10-stage sequential journey taking learners from core principles to real-world AI product deployment.
UNDERSTAND AI
Foundations, history, AI system lifecycle & ethical challenges.
PROGRAM WITH PYTHON
Core syntax, OOP, file handling, APIs & Git version control.
WORK WITH DATA
NumPy, Pandas, data cleaning, EDA & Matplotlib visualisations.
BUILD ML MODELS
Supervised & unsupervised ML, evaluation metrics & Scikit-learn.
UNDERSTAND DEEP LEARNING
Perceptrons, neural networks, TensorFlow/Keras, CNNs & NLP.
APPLY GENERATIVE AI
LLMs, Transformers, prompt engineering & structured outputs.
INTEGRATE AI APIs
REST APIs, environment keys, AI SDKs & Streamlit interfaces.
BUILD RAG WORKFLOWS
Document chunking, vector databases, embeddings & semantic search.
APPLY RESPONSIBLE AI
Bias mitigation, prompt injection security & privacy controls.
DEVELOP AN AI PRODUCT PROTOTYPE
End-to-end Capstone execution, testing & product presentation.
UNDERSTAND AI PROGRAM WITH PYTHON WORK WITH DATA BUILD ML MODELS UNDERSTAND DEEP LEARNING APPLY GENERATIVE AI INTEGRATE AI APIs BUILD RAG WORKFLOWS APPLY RESPONSIBLE AI PRODUCT PROTOTYPE
10 Focused Modules • 100 Total Hours
Explore the complete module breakdown, topic by topic, designed for practical mastery.
Explore the fundamentals of Artificial Intelligence, the evolution of AI, AI systems and lifecycle, learning-based systems, Generative AI, industry applications, and the challenges and limitations of modern AI.
Introduction to Artificial Intelligence
- Definition and concepts of Artificial Intelligence
- History and evolution of AI
- Traditional software systems vs AI systems
- Rule-based systems and learning-based systems
- Narrow AI and General AI
- Artificial Intelligence, Machine Learning and Deep Learning
- Introduction to Generative AI
AI Systems and Lifecycle
- Components of an AI system
- Data, algorithms and models
- Training and inference
- AI problem identification
- AI development lifecycle
- Model development and deployment overview
Applications of Artificial Intelligence
- AI in healthcare & medical diagnostics
- AI in banking, finance & risk assessment
- AI in manufacturing & predictive maintenance
- AI in retail & e-commerce personalisation
- AI in education & adaptive learning
- AI in cybersecurity & threat detection
- AI in agriculture & precision farming
- AI in logistics and supply chain optimization
- AI in human resources & talent matching
AI Challenges and Limitations
- AI hallucination & factual inaccuracies
- Bias in AI systems & dataset skewness
- Reliability & determinism of AI models
- Data dependency & quality constraints
- AI limitations & boundary conditions
- Introduction to Responsible AI principles
Build essential Python programming knowledge for AI development, covering data types, control structures, data structures, functions, modules, file handling, JSON, exception handling, object-oriented programming, APIs, Git and GitHub.
Python Development Environment
- Introduction to Python & ecosystem
- Python installation and environment setup
- Google Colab cloud environments
- Jupyter Notebook workflow
- Visual Studio Code setup & extensions
- Python syntax and program structure
- Comments and code documentation
Python Fundamentals & Operators
- Variables & dynamic typing
- Numeric data types, Strings, Boolean values
- Type conversion & explicit casting
- Input and output operations
- Arithmetic, Assignment, Comparison & Logical operators
- Membership operators & operator precedence
Control Flow & Data Structures
- If, If-else, Elif & nested conditional statements
- For loops, While loops & range function
- Break, Continue & nested loops
- Lists, Tuples, Dictionaries & Sets
- Indexing, slicing & comprehensions
- Data structure operations & methods
Functions, Modules & Object-Oriented Programming
- Defining functions, parameters & return values
- Positional, keyword arguments & variable scopes
- Lambda functions & functional utilities
- Python modules, import statements & standard library
- Package management using pip
- Classes, objects, attributes, methods & constructors
- Introduction to OOP inheritance
File Handling, APIs & Git
- Reading/Writing text files & CSV processing
- JSON data structure & JSON processing in Python
- Exception handling (Try, Except, Else, Finally)
- Virtual environments (venv, conda)
- API concepts, requests, responses & JSON payloads
- Git version control, repos, commits, push/pull & GitHub
Skills & Technologies Covered
Master the industry-standard libraries, frameworks, and AI platforms required for modern AI application engineering.
Programming & Development
Data Science & Preparation
Machine Learning
Deep Learning Fundamentals
Generative AI & LLMs
AI App Development
Modern AI & RAG
Responsible AI & Product
Who Should Join This Program?
No Prior Advanced AI Required
Basic computer knowledge and logical thinking are expected. Prior advanced knowledge of Artificial Intelligence or Machine Learning is not mandatory — the course starts with Python & AI foundations.
What You Need to Start:
- • Basic computer literacy & browser navigation
- • Logical problem-solving mindset
- • Dedication to complete 100 hours of structured learning
Ready to Become an Applied AI Engineer?
Join the 100-hour hybrid program. Move from AI concepts to building live Machine Learning models, RAG systems, and capstone AI product prototypes.