Evolution from Core Python to Generative AI

Overview

Explore the progression from Python fundamentals to building sophisticated Generative AI models, including neural networks, deep learning, and industry applications.

Syllabus

• Introduction to Python
Basics of Python syntax and semantics
Data types and structures
Control flow and functions
Introduction to modules and libraries
• Advanced Python Concepts
Object-oriented programming in Python
Exception handling
File handling
Understanding Python decorators and generators
• Data Handling and Visualization
Working with NumPy for numerical computations
Data manipulation with pandas
Visualization techniques using Matplotlib and Seaborn
• Introduction to Machine Learning
Overview of machine learning concepts
Supervised vs. unsupervised learning
Introduction to Scikit-learn
• Building Neural Networks
Understanding neural network fundamentals
Using TensorFlow and Keras for building neural networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
• Deep Learning Techniques
Transfer learning
Hyperparameter tuning
Advanced neural network architectures (e.g., ResNet, LSTM)
• Generative AI Models
Overview of generative models
Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
• Applications of Generative AI
Text generation and Natural Language Processing (NLP)
Image generation and transformation
Industry case studies and real-world applications
• Ethical Considerations in AI
AI ethics and responsible AI development
Bias in AI models and mitigation strategies
Privacy and security concerns
• Capstone Project
Designing and implementing a generative AI model
Real-world problem solving
Presentation and evaluation of model outcomes

Subjects

Programming