How to build AI Agents with Claude 3 API in Python

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Justin B

2:07:18

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1. Introduction

1. Introduction.mp4

01:04

2. Familiarize yourself with the Udemy User Interface.mp4

02:41

3. AI Definitions and Acronyms.mp4

05:25

4. Testing AI Definitions and Acronyms.html

2. AI Overview

1. What are LLMs (Large Language Models).mp4

03:30

2. How do Transformers Work.mp4

10:12

3. Basics of Prompt Engineering.mp4

05:59

4. Zero Shot and Few Shot Prompting.mp4

03:55

5. How to Evaluate Prompts and Models.mp4

05:32

6. A Framework to Writing Good Prompts.mp4

05:18

7. What makes a Model Multimodal.mp4

07:18

8. What is RAG (Retrieval Augmented Generation).mp4

06:41

9. What is Fine Tuning.mp4

05:34

3. Claude 3 API Overview

1. Model Differences and Pricing Opus, Haiku, Sonnet.mp4

02:39

2. API Capabilities.mp4

03:19

3. Getting an Anthropic API Key.mp4

01:30

4. Client Libraries (SDKs).mp4

01:46

5. Using the Messages API.mp4

05:48

4. AI Application Architecture

1. Strategies to Reduce Latency.mp4

03:35

2. Rate Limits.mp4

02:44

3. AI Application Architecture.mp4

08:20

4.1 lease.txt

4.2 rag from scratch.zip

4. Building a RAG Pipeline from Scratch.mp4

14:59

5.1 main.zip

5. RAG Reranking for Limited Context Windows.mp4

10:09

6. What are AI Agents.mp4

09:20

Description

Learn AI Fundamentals, how to write good prompts and evaluations, Retrieval Augmented Generation, Vector Databases

What You'll Learn?

What are AI Agents and Retrieval Augmented Generation (RAG)
How to create a RAG pipeline from scratch
Claude 3 API Overview, differences between the 3 models, pricing, and rate limits
How to write good prompts and evals to iterate on AI applications

Who is this for?

Beginner Python developers learning more about building AI Applications

Business owners that want to understand how AI will transform their business

Project managers that want to learn more about how AI application architecture work

What You Need to Know?

Basic understanding of Python

Some programming experience is helpful

More details

Description
This comprehensive course is designed to equip you with the knowledge and skills to build cutting-edge AI applications using state-of-the-art techniques and tools. You'll delve into the fundamentals of artificial intelligence, explore the power of large language models (LLMs), and learn how to create intelligent systems that can understand, process, and generate human-like text.

Course Highlights:
Understanding AI Foundations: Gain a solid grasp of the core concepts of artificial intelligence and its applications in various industries.
Large Language Models (LLMs): Discover the capabilities and inner workings of LLMs, the driving force behind many AI-powered solutions.
Transformers: Uncover the revolutionary architecture that powers LLMs and understand how they process and generate text.
RAG, Fine-Tuning, Prompting: Master advanced techniques like Retrieval Augmented Generation (RAG), fine-tuning, and few-shot prompting to optimize model performance and create tailored AI applications.
Prompt Engineering: Learn the art of crafting effective prompts to elicit accurate and relevant responses from AI models.
Claude API Overview: Explore the Claude API, a powerful tool for building AI applications. Understand its model differences, pricing structure, and available client libraries.
AI Application Architecture: Design and implement robust architectures for AI applications, considering rate limits and performance optimization.
Building a RAG System: Learn how to build a RAG system from scratch, including context retrieval and reranking for improved results.
AI Agents: Discover the concept of AI agents and their role in creating interactive and autonomous AI applications.
By the end of this course, you will be able to:

Explain the core concepts of artificial intelligence and LLMs.
Utilize transformer architectures and understand their role in language processing.
Apply advanced techniques like RAG, fine-tuning, and few-shot prompting to optimize AI model performance.
Develop expertise in prompt engineering to elicit desired responses from AI models.
Leverage the Claude API to build AI applications, understanding model variations, pricing, and client libraries.
Design and implement effective AI application architectures, considering rate limits and performance optimization.
Build a RAG system from scratch, incorporating context retrieval and reranking.
Understand the concept of AI agents and their role in creating interactive and intelligent systems.
Whether you're a beginner or an experienced developer, this course will provide you with the foundation and practical skills to build innovative AI applications that can transform industries and improve lives. Join us on this exciting journey into the world of AI development!
Who this course is for:
Beginner Python developers learning more about building AI Applications
Business owners that want to understand how AI will transform their business
Project managers that want to learn more about how AI application architecture work

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Justin is a seasoned software engineer and AI creator with 5 years of experience in the field. He has a background in both theoretical and practical aspects of software engineering, and has worked on several projects involving machine learning. Justin is passionate about sharing his expertise with students and helping them develop the skills they need to succeed in the field of AI

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