The AI Foundations Track introduces students to the exciting world of Artificial Intelligence and Machine Learning, with a special focus on the latest developments in Generative AI and Large Language Models. This comprehensive program builds understanding from first principles while ensuring practical application of cutting-edge AI technologies.

Program Structure

This 8-week intensive program balances theoretical understanding with hands-on implementation, starting with AI fundamentals and progressing to advanced architectures and real-world applications.

Month 1: Generative AI & LLM Fundamentals

• Week 1-2: Introduction to Generative AI

  • Understanding generative AI across modalities (text, code, images, multimodal)
  • The AI revolution: from rule-based systems to neural networks
  • Key concepts: tokens, embeddings, and neural representations
  • Hands-on with popular AI tools and platforms

• Week 3: Large Language Model Architecture

  • LLM fundamentals: tokens, embeddings, and attention mechanisms
  • Transformer architecture deep dive
  • Understanding model parameters and scaling laws
  • Tokenization and text preprocessing techniques

• Week 4: LLM Training & Fine-tuning

  • Pre-training process and data requirements
  • Fine-tuning techniques and transfer learning
  • Reinforcement Learning from Human Feedback (RLHF)
  • Practical experience with Hugging Face, LangChain, and OpenAI API

Month 2: Deep Learning & Advanced Architectures

• Week 5: Neural Network Fundamentals

  • Perceptrons and multi-layer neural networks
  • Backpropagation and gradient descent algorithms
  • Activation functions and network initialization
  • Building neural networks from scratch in Python

• Week 6: Specialized Neural Architectures

  • Convolutional Neural Networks (CNNs) for image classification
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
  • Sequence-to-sequence models and applications
  • Practical implementation using TensorFlow and PyTorch

• Week 7: Transformers & Attention Mechanisms

  • Self-attention and multi-head attention
  • Positional encoding and transformer blocks
  • Encoder-decoder architectures
  • Building transformer models from scratch

• Week 8: Generative Models & Advanced Concepts

  • Autoencoders and latent space representation
  • Variational Autoencoders (VAE) for generation
  • Generative Adversarial Networks (GANs)
  • Modern generative models and applications

Technology Stack

Core Frameworks & Libraries

• Deep Learning: TensorFlow 2.x, PyTorch, Keras
• AI/ML Libraries: Hugging Face Transformers, LangChain, OpenAI API
• Data Science: NumPy, Pandas, Matplotlib, Seaborn
• Development: Python 3.x, Jupyter Notebooks, Google Colab
• Version Control: Git, GitHub for ML projects

Cloud & Deployment

• Google Colab for GPU-accelerated training
• Hugging Face Hub for model sharing
• Basic cloud deployment concepts
• Model versioning and experiment tracking

Hands-On Projects

Project 1: Custom Neural Network Implementation

• Build neural networks from scratch using NumPy
• Implement backpropagation and gradient descent
• Train models on classic datasets (MNIST, CIFAR-10)
• Compare performance with framework implementations

Project 2: LLM Fine-tuning & Applications

• Fine-tune pre-trained language models for specific tasks
• Build chatbots and text generation applications
• Implement RAG (Retrieval-Augmented Generation) systems
• Create practical AI applications using LangChain

Project 3: Computer Vision with CNNs

• Design and train CNN architectures for image classification
• Implement transfer learning with pre-trained models
• Build image generation models using GANs or VAEs
• Create end-to-end computer vision applications

Capstone Project: AI from Scratch to LLM

• Comprehensive project demonstrating journey from basic AI to modern LLMs
• Implement multiple neural architectures from scratch
• Fine-tune and deploy a large language model
• Create a complete AI application with user interface
• Present technical concepts and business applications

Prerequisites

Basic programming knowledge recommended but not required. This track complements the Data Foundations track and can be taken concurrently or sequentially. Familiarity with Python basics is helpful but will be covered in the program.

Career Outcomes

Graduates will be prepared for AI/ML engineer roles and ready to advance to specialized tracks:

Immediate Career Opportunities

• AI/ML Engineer positions in tech companies
• Research Assistant roles in AI labs and universities
• AI Product Developer in startups and enterprises
• Machine Learning Consultant for businesses
• AI Content Creator and technical educator

Advanced Specialization Paths

• Machine Learning Track for production ML systems
• Generative AI Hero Track for enterprise GenAI solutions
• Agentic AI GCP Track for autonomous AI systems
• Data Science Track with AI/ML focus

Skills Acquired

• Deep understanding of AI and machine learning fundamentals
• Hands-on experience with modern deep learning frameworks
• Proficiency in building and training neural networks from scratch
• Experience with large language models and generative AI
• Practical skills in AI application development
• Foundation for advanced AI specializations

Industry Applications

Students will work on real-world AI applications including:

• Natural Language Processing: Chatbots, sentiment analysis, text generation
• Computer Vision: Image classification, object detection, style transfer
• Generative AI: Content creation, code generation, creative applications
• Business Intelligence: AI-powered analytics and decision support
• Healthcare: AI diagnostics and medical image analysis
• Finance: Algorithmic trading and fraud detection

Next Steps

This track provides the essential foundation for our advanced AI specializations:

• Machine Learning Track for production ML deployment
• Generative AI Hero Track for enterprise-grade GenAI solutions
• Agentic AI GCP Track for building autonomous AI agents
• Data Science Track with enhanced AI capabilities