Applied GenAI with Python

Applied GenAI with Python

From Fundamentals to Real-World Applications

Empower your Python skills to build intelligent applications using cutting-edge Generative AI techniques and tools in 5 days

The advent of Generative AI has transformed the technological landscape, enabling machines to create content, code, and conversations with unprecedented sophistication. Python, renowned for its simplicity and versatility, serves as the backbone for developing these intelligent systems. This course is meticulously crafted for enthusiastic learners with foundational Python knowledge, aiming to bridge the gap between basic programming and advanced AI application development.

Participants will delve into the world of Large Language Models (LLMs), exploring their capabilities and learning how to integrate them into practical applications. The curriculum encompasses essential concepts such as Machine Learning (ML), Deep Learning (DL), API integrations with leading LLM providers like OpenAI, Hugging Face, and DeepSeek, as well as frameworks like LangChain and Retrieval-Augmented Generation (RAG). Additionally, learners will acquire prompt engineering techniques to interact effectively with LLMs, enhancing the efficiency and accuracy of AI-driven solutions.

Guided by an instructor with over 30 years of industry experience, the course emphasizes hands-on learning, real-world applications, and industry-relevant content, ensuring that participants are well-equipped to tackle contemporary challenges in the AI domain.

Learning Outcomes

By the end of this course, participants will be able to:

• Understand the fundamentals of Generative AI and LLMs.
• Implement basic ML and DL models using Python libraries.
• Integrate and utilize APIs from OpenAI, Hugging Face, and DeepSeek.
• Develop applications using LangChain and RAG frameworks.
• Apply prompt engineering techniques for effective LLM interactions.
• Utilize Python features like decorators, recursion, and parallel computing to enhance application performance.

Prerequisites

Participants should have:

• Basic proficiency in Python programming, including data types, control structures, functions, and object-oriented programming.
• Familiarity with Python libraries such as NumPy and pandas.
• Understanding of fundamental programming concepts and logical problem-solving skills.

Training Course Outline

1. Introduction to Generative AI and LLMs

• Overview of Generative AI: Definition, history, and applications.
• Understanding Large Language Models (LLMs): Architecture, capabilities, and limitations.
• Ethical considerations in AI development and deployment.

2. Fundamentals of Machine Learning and Deep Learning

• Introduction to Machine Learning: Supervised, unsupervised, and reinforcement learning.
• Deep Learning basics: Neural networks, activation functions, and backpropagation.
• Implementing simple ML and DL models using scikit-learn and TensorFlow/Keras.

3. API Integration with Leading LLM Providers

• Understanding APIs: RESTful APIs, authentication, and data exchange formats.
• Integrating OpenAI API: Accessing GPT models for text generation tasks.
• Utilizing Hugging Face Transformers: Loading pre-trained models and performing inference.
• Exploring DeepSeek API: Features and integration methods.

4. Developing Applications with LangChain Framework

• Introduction to LangChain: Purpose and core components.
• Building chains and agents for task automation.
• Implementing memory and state management in applications.
• Case study: Creating a conversational agent using LangChain.

5. Retrieval-Augmented Generation (RAG) Techniques

• Understanding RAG architecture and its benefits.
• Implementing RAG for document retrieval and question answering.
• Optimizing RAG pipelines for performance and accuracy.
• Practical exercise: Developing a document summarization tool using RAG.

6. Prompt Engineering for Effective LLM Interaction

• Designing effective prompts: Principles and best practices.
• Techniques: Zero-shot, few-shot, and chain-of-thought prompting.
• Automating prompt generation and evaluation.
• Hands-on activity: Crafting prompts for various AI tasks.

7. Advanced Python Techniques for AI Applications

• Decorators:
  • Understanding decorators and their use cases.
  • Implementing decorators for logging, authentication, and performance monitoring.
• Recursion:
  • Concept of recursion and its applications.
  • Solving problems involving deeply nested data structures.
• Parallel Computing:
  • Introduction to multiprocessing and multithreading in Python.
  • Best practices for concurrent execution and resource management.

8. Building Real-World AI Applications

• Developing a chatbot using integrated LLM APIs and LangChain.
• Creating a document summarization tool with RAG.
• Implementing a code generation assistant using prompt engineering.
• Project work: Participants will conceptualize, design, and develop a comprehensive AI application incorporating the learned concepts.

9. Deployment and Scaling of AI Applications

• Containerizing applications using Docker.
• Deploying applications on cloud platforms (e.g., AWS, Azure).
• Monitoring and maintaining application performance and scalability.

This course is designed to provide participants with a comprehensive understanding of Generative AI and its practical applications using Python. Through hands-on exercises, real-world projects, and expert guidance, learners will acquire the skills necessary to develop, deploy, and manage intelligent AI systems effectively.