Mastering Chatbots with Botpress, Transformers, RAG & LLMs from Udemy

Are you ready to learn how to build powerful and AI-supported chatbots from scratch?

there are a lot of courses out there that teach you how to develop chatbots. So what makes this course DIFFERENT?

We're NOT going to use any cloud-based chatbot solutions like Dialogflow, IBM Watson, or Microsoft Azure. Instead, we'll be focusing on free and open-source technologies that are just as robust and powerful.
We're NOT just going to talk only about the basics of chatbot development. We’re going to dive deeply into this world.
This course is full of project-based tutorials. A lot of techniques will be derived via developing a set of chatbot projects

Chatbots are everywhere and are becoming an increasingly important part of our daily lives. They're used for a wide range of applications, from customer service to online shopping, and they're only getting more advanced and sophisticated.

In the course, we delve into the different types of chatbots and their use cases, including rule-based chatbots, AI-powered chatbots, and conversational AI. We also cover the various technologies and platforms that are used to build chatbots, such as natural language processing (NLP), machine learning (ML), and chatbot development open-source projects like Botpress, SetFit, GLiNER, Transformers, langChain, fastAPI, Docker, and more.

In this course, you will learn:

How to Setup Your Development Environment Tools
How to Install and start your first Botpress project
You will Understand what the conversation flow studio is
Develop the different types of chatbot response templates
You will learn how to Integrate with third parties and APIs to provide external information for users
How to Develop a QnA chatbots
Understand the problem intent detection and how to solve it using either rule-based or neural network techniques
How to recognize entities in the user message and how to fill the slots.
How to collect user data and forward them to an external API or store them in a database.
How to develop your Transformers Chatbot Assistant models (Rasa, SetFit and GLiNER)
How to integrate Botpress with Rasa Chatbot Assistant
How to develop a fastAPI app to serve your AI projects
How to integrate your chatbot with popular messaging platforms like Facebook Messenger and Telegram
How to use the modern Large Language Models (LLMs) like OpenAI to support your chatbots
Learn all the basics of building a robust application using ChatGPT and open-source Large Language Models
How to use Drage-Drop UI Tools like Flowise to Develop LLM chatbots
How to use LLMs to develop AI Engines and Chatbots
Build the style of "Chat with your data" modern apps.
Learn in detail how to build RAG LLM apps.
More ..

By the end of the course, students will have a comprehensive understanding of the current state of chatbot technology and how it is being used in real-world applications. This knowledge will equip students with the skills and confidence to embark on their chatbot projects and contribute to the rapidly evolving field of conversational AI.

What's inside

Learning objectives

Developing chatbots using open-source tools like botpress, rasa and transformers. no cloud based solutions.
Understand all of the chatbot developing pillars like intent-detection, entity-recognition, conversation flow and more.
Learn the concepts of prompt engineering using langchain, chatgpt and huggingface
Develop neural networks models to detect entities and recognize entities in the user messages.

Integrate with third parties and apis to develop mature chatbots with live data.
Develop web applications using fastapi to support the chatbot services.
Learn via developing a set of real-world chatbot projects.

Developing chatbots using open-source tools like botpress, rasa and transformers. no cloud based solutions.
Understand all of the chatbot developing pillars like intent-detection, entity-recognition, conversation flow and more.
Learn the concepts of prompt engineering using langchain, chatgpt and huggingface
Develop neural networks models to detect entities and recognize entities in the user messages.
Integrate with third parties and apis to develop mature chatbots with live data.
Develop web applications using fastapi to support the chatbot services.
Learn via developing a set of real-world chatbot projects.

Syllabus

Hello Buddy Chatbot - Part 2/2

what makes this course different?

Course Introduction

To run Botpress locally you have two options.

1) Option 1
Follow sections (2 + 3 + 4) to set up Docker, Jupyter, and Botpress locally step by step.

2) Option 2
Or you can download a premade virtual image and run it using VirtualBox to get a running Botpress in your machine in minutes. This's what we are explaining in this video.

This way is compatible with Windows, Linux, and Mac (intel ships) users.

You need to download firstly the image from here
https://drive.google.com/file/d/1ULY03ultG_v4L5RvKYzfr_9qxOP8XGKf/view?usp=sharing

You must have at least (20 GB of free disk space and 4GB RAM) on your computer.

How to Setup Docker in your Ubuntu Operating System

How to install WSL in your Windows Operating System

WARNING:
If you face any issues in installing JpyterLab using

```
>> conda install jupyterlab
```

Please try this command

```
>> python3 -m pip install jupyterlab
```

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops skills which are core for conversational AI

Taught by experts in the field with recognized work in AI

Covers core concepts of chatbot development like intent-detection, entity-recognition, and conversation flow

Teaches how to work with APIs to provide external information

In-depth, with Hands-on labs and interactive material

Provides comprehensive study of AI-powered chatbots

Reviews summary

Building modern chatbots with open-source ai

According to students, this course offers a comprehensive and practical approach to building modern chatbots, distinguishing itself by focusing on free and open-source technologies like Botpress, Transformers, RAG, and LLMs, rather than cloud-based solutions. Learners praise the project-based tutorials and hands-on activities, which provide a strong understanding of concepts from intent detection to advanced RAG implementations. While the course is regularly updated to include the latest AI advancements, some found the initial environment setup challenging, and it is best suited for those with prior programming and ML familiarity.

Provides broad coverage of chatbot concepts but may require additional study.

"The course material is very comprehensive, covering everything from Botpress fundamentals to advanced RAG implementations."

"A solid foundation, but some sections felt a bit rushed... I'll need to do additional self-study for mastery."

"Some explanations felt a bit superficial for certain advanced topics, prompting further research."

Utilizes cutting-edge open-source tools for real-world applications.

"I really appreciate the focus on open-source tools, which saves a lot on cloud costs."

"This course stood out with its focus on Botpress, Transformers, RAG & LLMs, making it incredibly relevant."

"The FastAPI integration for serving AI models was a crucial piece of the puzzle I was missing."

Course delivers practical skills through numerous hands-on projects.

"The project-based approach is fantastic, especially the parts on integrating LLMs and RAG with Botpress."

"Absolutely brilliant! The hands-on labs and coding exercises solidified my understanding."

"I gained practical skills by building a 'Chat with your data' app, which was particularly insightful."

Best for learners with existing programming and ML knowledge.

"I found the course quite difficult to follow as a beginner. It assumes a lot of prior knowledge in Python, ML, and Docker."

"Highly recommend for intermediate developers rather than absolute beginners."

"A solid foundation, but some sections felt a bit rushed, especially for those new to some concepts."

Initial setup can be complex due to dependencies and tool versions.

"I had some initial trouble with environment setup, but the steps eventually worked."

"I found the Docker setup to be quite challenging on my specific OS, even with the provided options."

"Too many versioning issues with libraries. Code examples sometimes didn't work out of the box, requiring significant debugging."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mastering Chatbots with Botpress, Transformers, RAG & LLMs with these activities:

Brush Up on Python Programming Fundamentals

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Ensure a solid foundation in Python programming before delving into chatbot development.

Browse courses on Python

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Review core Python concepts like variables, data types, and control flow.
Practice solving coding problems on platforms like LeetCode or HackerRank.

Explore 'Natural Language Processing with Python' by Steven Bird et al.

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Gain a deeper understanding of NLP concepts and techniques used in chatbot development.

View Natural Language Processing with Python:... on Amazon

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Organize a Study Group for Collaborative Learning

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Enhance your understanding through group discussions and knowledge sharing.

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Form a group with classmates or find a study group online.
Meet regularly to discuss course material, share notes, and work on projects together.

Seven other activities

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Complete coding exercises on chatbot development platforms.

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Completing coding exercises will provide you with hands-on experience and help you develop your chatbot development skills.

Browse courses on Chatbot Development

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Identify coding exercises related to chatbot development on platforms like HackerRank or LeetCode.
Solve the exercises using the techniques and concepts covered in the course.
Review your solutions and identify areas for improvement.
Repeat the process to reinforce your understanding and enhance your skills.

Design a Chatbot Interface for Enhanced User Experience

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Develop your skills in creating intuitive and engaging user interfaces for chatbots.

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Study best practices for chatbot UI design.
Create mockups and prototypes for your chatbot interface.
Implement the UI using HTML, CSS, and JavaScript.

Master Intent Detection and Entity Recognition

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Develop and refine your skills in identifying user intents and extracting relevant entities.

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Practice using regular expressions for intent detection.
Utilize pre-trained models and algorithms for entity recognition.
Create custom training data to improve accuracy.

Develop a chatbot prototype to solve real-world problems.

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Creating a chatbot prototype will allow you to apply the concepts learned in the course and reinforce your understanding of chatbot development.

Browse courses on Chatbot Development

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Identify a specific problem or use case that you want to address with your chatbot.
Design the conversation flow and user interface for your chatbot.
Develop the chatbot's logic using the techniques and technologies covered in the course.
Test and refine your chatbot to ensure it provides a seamless user experience.
Deploy your chatbot on a platform or channel where users can interact with it.

Follow tutorials on advanced chatbot development techniques.

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Following tutorials will expose you to cutting-edge techniques and best practices in chatbot development, enhancing your knowledge and skills.

Browse courses on Chatbot Development

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Identify tutorials from reputable sources, such as Coursera, edX, or YouTube channels of industry experts.
Follow the tutorials step-by-step, taking notes and experimenting with the techniques.
Apply the knowledge gained from the tutorials to your own chatbot development projects.

Explore Advanced Chatbot Techniques with Hugging Face and OpenAI

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Gain hands-on experience with state-of-the-art chatbot development libraries and models.

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Set up Hugging Face account and API key.
Implement a chatbot using the 🤗 Transformers library.
Fine-tune a chatbot model using OpenAI's API.
Integrate the enhanced chatbot into your project.

Build a Chatbot Assistant Using a Real-World Dataset

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Reinforce your learning by applying chatbot development and deployment skills to a new dataset.

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Compile a dataset relevant to the chatbot's purpose.
Design the chatbot's conversation flow and intents.
Train and evaluate the chatbot using the provided dataset.
Deploy the chatbot and integrate it with a messaging platform.

Career center

Learners who complete Mastering Chatbots with Botpress, Transformers, RAG & LLMs will develop knowledge and skills that may be useful to these careers:

Reading list

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Mastering Chatbots with Botpress, Transformers, RAG & LLMs

What's inside

Learning objectives

Syllabus

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Reviews summary

Building modern chatbots with open-source ai

Activities

Career center

Reading list

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