Visão Geral

O curso Chatbot Development with Python ensina como projetar, desenvolver e implantar chatbots inteligentes utilizando Python e frameworks modernos. Os participantes aprenderão desde a criação de chatbots baseados em regras até a integração com IA e Processamento de Linguagem Natural (NLP), permitindo interações mais humanas e personalizadas.

Objetivo

Após realizar este Curso Chatbot Development with Python, você será capaz de:

• Compreender os conceitos fundamentais de chatbots e automação conversacional
• Criar chatbots baseados em regras e em aprendizado de máquina
• Utilizar bibliotecas de NLP para compreensão de linguagem natural
• Integrar chatbots a plataformas como Telegram, Slack e websites
• Implementar chatbots inteligentes com frameworks como Rasa e ChatterBot

Publico Alvo

• Desenvolvedores, engenheiros de software, analistas de dados e entusiastas de IA interessados em criar assistentes virtuais, bots de atendimento, chatbots para redes sociais e soluções de automação conversacional.

Pre-Requisitos

• Conhecimento básico de Python
• Noções de APIs REST e JSON
• Familiaridade com conceitos básicos de NLP (desejável)

Materiais

Conteúdo Programatico

Introduction to Chatbots

1. What is a chatbot?
2. Types of chatbots: rule-based vs AI-based
3. Chatbot applications across industries

Setting Up the Environment

1. Installing Python and necessary libraries
2. Overview of popular chatbot frameworks
3. Setting up a virtual environment

Rule-Based Chatbots

1. Creating a simple chatbot with conditional logic
2. Handling user input and responses
3. Building interactive conversations

Natural Language Processing (NLP) Basics

1. Understanding NLP and text preprocessing
2. Tokenization, stemming, and lemmatization
3. Using NLTK and spaCy for text understanding

Building AI-Powered Chatbots

1. Introduction to intent recognition and entities
2. Training conversational models
3. Integrating NLP with chatbot workflows

Working with ChatterBot

1. Installing and configuring ChatterBot
2. Training the chatbot with conversation datasets
3. Managing context and custom responses

Developing with Rasa Framework

1. Overview of Rasa NLU and Rasa Core
2. Creating intents, stories, and dialogue management
3. Running and testing Rasa chatbots locally

Integration with Messaging Platforms

1. Connecting chatbots to Telegram, Slack, and Facebook Messenger
2. Using webhooks and APIs
3. Sending and receiving messages via HTTP

Chatbot Deployment

1. Hosting chatbots on the cloud (AWS, GCP, or Heroku)
2. Managing environments and dependencies
3. Monitoring chatbot performance and logging

Enhancing Chatbot Intelligence

1. Adding sentiment analysis and context awareness
2. Using external APIs for dynamic responses
3. Incorporating voice and speech-to-text capabilities

Security and Ethical Considerations

1. Data privacy and user consent
2. Avoiding bias in AI-based chatbots
3. Ensuring secure communication

Practical Projects

1. Building a customer support chatbot
2. Creating a Telegram assistant bot
3. Developing a website-integrated chatbot