Introduction to AI/Generative AI

• Python Refresher/Crash Course
  • Introduction
  • IDEs and Tools for data analysis
  • Data types and strings
  • Control structures
  • Functions and functional programming
  • Collections
  • Object Oriented
  • Modules and Packages

• Introduction to Data Science
  • Data Science
  • Machine Learning Overview
  • Data Analysis and ML Packages overview
  • Demo – Complete Machine Learning task

• Machine Learning Basic Concepts
  • Overview
  • Why Learn
  • Applications
  • Machine Learning Process
  • Learning Types:
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Active Learning
  • Reinforcement learning
  • Batch vs Online Learning
  • Instance-based vs model-based learning
  • CRISP – DM Methodology
  • Data Understanding
  • Data Preparation

• Supervised Learning
  • Overview
  • Regressions
  • Classification
  • Non-Linear Regression
  • Model evaluation using metrics

• Unsupervised Learning
  • Overview
  • Clustering
  • K-Means
  • Recommender systems

• Deep Learning and Neural Networks
  • Deep Learning
  • Neural networks overview
  • Network structure and hidden layers
  • Training the network
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Auto Encoders
  • NLP
• Generative AI
  • Generative Adversarial Networks (GANs)
  • Generative vs. Discriminative Models
  • Role in Modern Tech Landscape
  • Advanced GAN Types: cGAN, StackGAN, BigGAN
  • Practical Sessions on GANs
  • Variational Autoencoders (VAEs)
  • Implementing VAEs for Practical Use Cases
  • Natural Language Processing (NLP) and Text Generation
  • Advanced Text Generation Techniques
  • Deepfake generation and detection
  • Procedural content generation in gaming
  • Generative models in design and art

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