A structured learning journey covering artificial intelligence, machine learning, generative AI, prompt engineering, model fundamentals, practical projects and responsible AI.

AI Foundations

1. AI Foundations

Start with the fundamentals of Artificial Intelligence and understand how modern AI systems work.

Topics to learn

  • What is Artificial Intelligence?
  • AI vs Machine Learning vs Deep Learning
  • Types of machine learning
  • Supervised and unsupervised learning
  • Training, validation and testing
  • Common AI use cases
  • AI terminology every technology professional should know

Recommended outcome: Understand the fundamentals before moving into machine learning and Generative AI.

2. Python for AI & Machine Learning

Build the programming foundation required for practical AI development.

Topics to learn

  • Python fundamentals
  • Variables, functions and modules
  • Lists, dictionaries and data structures
  • NumPy
  • Pandas
  • Data cleaning and preparation
  • Data visualization
  • Working with Jupyter Notebooks

Recommended outcome: Be able to analyse datasets and prepare data for machine learning.

3. Machine Learning Fundamentals

Learn how machine learning models learn from data and make predictions.

Topics to learn

  • Linear regression
  • Logistic regression
  • Decision trees
  • Random forests
  • Classification
  • Clustering
  • Feature engineering
  • Model evaluation
  • Overfitting and underfitting
  • Cross-validation

Recommended outcome: Build and evaluate basic machine learning models.

4. Deep Learning

Move from traditional machine learning into neural-network-based approaches.

Topics to learn

  • Neural network fundamentals
  • Activation functions
  • Forward and backward propagation
  • Training neural networks
  • Convolutional neural networks
  • Recurrent neural networks
  • Transformers
  • Introduction to PyTorch and TensorFlow

Recommended outcome: Understand the architecture behind modern AI systems.

5. Generative AI

Understand the technology behind today’s rapidly evolving Generative AI ecosystem.

Topics to learn

  • What is Generative AI?
  • Large Language Models
  • Tokens and context windows
  • Embeddings
  • Transformers
  • Foundation models
  • Text generation
  • Image generation
  • AI assistants
  • Retrieval-Augmented Generation (RAG)

Recommended outcome: Understand how Generative AI applications are designed and implemented.

6. Prompt Engineering

Learn how to communicate effectively with AI models.

Topics to learn

  • Prompt fundamentals
  • Zero-shot prompting
  • Few-shot prompting
  • Role prompting
  • Structured prompts
  • Chain-of-thought concepts
  • Context management
  • Prompt evaluation
  • Prompt optimization
  • Common prompting mistakes

Recommended outcome: Create reliable prompts for professional and technical use cases.

7. Building AI Applications

Move from theory to practical implementation.

Topics to learn

  • Working with AI APIs
  • Building AI-powered applications
  • Chatbot architecture
  • RAG applications
  • Vector databases
  • Embeddings
  • AI agents
  • Tool calling
  • AI application architecture
  • Integrating AI into existing applications

Recommended outcome: Build practical AI-powered solutions.

8. Responsible AI

Understand the risks and responsibilities associated with AI adoption.

Topics to learn

  • AI ethics
  • Bias and fairness
  • Privacy
  • Security
  • Explainability
  • Hallucinations
  • Data governance
  • Human oversight
  • Responsible AI implementation

Recommended outcome: Design and use AI responsibly in professional environments.

9. Practical AI Projects

Apply your learning through real-world projects.

Project 1 — AI Knowledge Assistant

Build an AI assistant that answers questions from a collection of documents.

Project 2 — RAG Application

Create a retrieval-augmented AI application using documents and embeddings.

Project 3 — AI Customer Support Assistant

Design an AI assistant capable of answering customer queries and escalating complex cases.

Project 4 — AI Productivity Assistant

Build an AI solution that summarizes information, generates content and assists with everyday tasks.

10. AI Career Roadmap

Choose your next specialization based on your career goals.

AI Engineer
→ Python → ML → Deep Learning → LLMs → AI Applications

Machine Learning Engineer
→ Python → Statistics → ML → Deep Learning → MLOps

Generative AI Engineer
→ Python → LLMs → Prompt Engineering → RAG → Agents

Data Scientist
→ Python → Statistics → Data Analysis → ML → Deep Learning

AI Product / Business Professional
→ AI Fundamentals → Generative AI → AI Use Cases → Responsible AI → AI Strategy

Your AI Learning Journey

Foundation → Python → Machine Learning → Deep Learning → Generative AI → Prompt Engineering → AI Applications → Responsible AI → Projects → Career Specialization