Mastering AI Agents Bootcamp: Build Smart Chatbots & Tools

Overview

Artificial intelligence is transforming the way we work, automate tasks, and interact with technology. This course is designed to help learners build AI-powered agents, automation bots, chat assistants, and task management systems using open-source tools without relying on external APIs or cloud-based services.

Whether you are a beginner exploring artificial intelligence or a developer looking to integrate AI into real-world applications, this course provides a hands-on approach to building AI-driven automation solutions.(AI)

Syllabus

• Introduction to AI Agents
Overview of AI agents and their applications
Key concepts: autonomy, interactivity, and learning
Setting up the development environment
• Fundamentals of Machine Learning
Types of machine learning: supervised, unsupervised, reinforcement
Basic algorithms and their use cases
Data collection and preprocessing
• Building Rule-Based Systems
Designing and implementing rule-based logic
Use cases for rule-based AI agents
Limitations and integrations with other AI systems
• Natural Language Processing (NLP) Essentials
Understanding NLP and its components
Tokenization, parsing, and semantic understanding
Building basic NLP pipelines
• Developing AI Chatbots
Designing conversational flows
Using open-source NLP libraries
Creating and testing a simple text-based chatbot
• AI for Task Automation
Identifying tasks suitable for automation
Building workflow automation scripts
Integrating AI with existing tools
• Machine Learning in AI Agents
Training ML models for specific tasks
Incorporating ML models into AI agents
Evaluating model performance and optimization
• Advanced AI Agent Architectures
Multi-agent system design and communication
Hybrid models combining rule-based and ML approaches
Scalability and efficiency in AI systems
• Privacy and Security in AI Development
Ensuring data privacy and compliance
Secure AI communication protocols
Ethical considerations in AI applications
• Case Studies and Real-World Applications
Analysis of successful AI agent deployments
Lessons learned from industry examples
Hands-on project: Building a comprehensive AI agent from concept to execution
• Capstone Project
Develop a fully functional AI agent addressing a specific problem
Presentation and peer review
Iteration based on feedback
• Course Wrap-Up
Summary of key learning points
Discussion on future directions in AI agent technology
Resources for further learning and development

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Taught by

Vivian Aranha

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Subjects

Computer Science