Generative AI Bootcamp - Complete 65-Hour Course

Overview

Master practical Generative AI skills through hands-on projects, from Python fundamentals to building AI agents and applications for a Japanese Language Learning School, suitable for beginners to intermediate learners.

Syllabus

• Introduction to Generative AI
Overview of Generative AI and its applications
Key concepts and terminology
• Python Programming Fundamentals
Variables, data types, and operators
Control structures: loops and conditionals
Functions and modules
• Introduction to Machine Learning
Supervised vs. unsupervised learning
Key algorithms and concepts
• Neural Networks and Deep Learning
Basics of neural networks
Introduction to deep learning frameworks (e.g., TensorFlow, PyTorch)
• Generative Models
Introduction to generative models
Autoencoders and Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
• Project 1: Building a Simple GAN
Understanding GAN architecture
Training a GAN for image generation
• Advanced Generative Techniques
Transfer learning and fine-tuning
Reinforcement learning and AI agents
• Natural Language Processing (NLP)
Introduction to NLP concepts
Text generation and language models
• Project 2: AI Application for Language Learning
Designing an AI agent for the Japanese Language Learning School
Integrating NLP techniques for interactive learning
• Deployment and Integration
Model deployment strategies
Building APIs and integrating AI into applications
• Ethics and Best Practices in AI
Addressing bias and fairness in AI models
Ethical considerations and responsible AI usage
• Final Project: Comprehensive AI Solution
End-to-end development of an AI-powered application
Presentation and evaluation of projects
• Course Review and Next Steps
Summary of key learnings
Continuing education and resources for further learning in AI

Subjects

Computer Science