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Implementing AI using IBM Watson Chatbots

Learn how to develop Conversational AI Chatbots using IBM Watson and how to integrate it with FB Messenger.
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Duration

2 Days

Level

Beginner Level

Design and Tailor this course

As per your team needs

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This two day course focuses on practical aspects of Conversational Artificial Intelligence. The objective is to understand the need of Chatbots, suitable business problems/use cases to apply Chatbot related solutions, when to avoid Chatbot related solutions, key Chatbot solutions available for buying/building,  understanding AI and Conversational AI, Build Chatbots using IBM Watson assistant. The participants will gain a basic understanding about Chatbots, key options available to build chatbots, set up a Chatbot on IBM Cloud and integrate Chatbot with FB Messenger. 

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Anyone who is keen in 

  • Understanding if Chatbot is the right solution for a Business problem
  • Understanding the broader perspective of implementing Chatbots 
  • Understanding unbiased comparisons of key Chatbot solutions 
  • Building a Chatbot on IBM Cloud platform
  • Integrating it with a Social network 
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  • What are Chatbots?
  • Why are Chatbots necessary?
  • Where Chatbots fit in the AI Ecosystem?
  • How are Natural Language Processing and Chatbots related?
  • How are Chatbots different from Robotics Process Automation?
  • Chatbot Use Cases
  • Limitations of Chatbots
  • Google Cloud Dialog Flow
  • Amazon Lex
  • Azure Bot Service
  • Watson Assistant Chatbot IBM 
  • Rasa etc.
  • IBM Watson Assistant offerings
  • Why IBM Watson Assistant?
  • Signup to IBM Cloud
  • Guide through IBM Cloud
  • Guide through IBM Cloud-part2
  • What are Intents?
  • Create and test Intents 
  • Import Intents
  • Intents Quiz
  • What are entities?
  • Annotations, Import Entities and System Entities
  • Entities Quiz
  • What is Dialog?
  • Building Dialog
  • Understanding Dialog and Chatbot Ethics
  • Context Variables
  • Slots
  • Digressions
  • Adding Integration channels
  • Introduction to Transformers
  • Input Embeddings
  • Positional Encoding
  • MultiHead Attention
  • Concat and Linear
  • Residual Learning
  • Layer Normalization
  • Feed Forward
  • Masked MultiHead Attention
  • MultiHead Attention in Decoder
  • Cross Entropy Loss
  • KL Divergence Loss
  • Dataset Preprocessing
  • Data Masking
  • Embeddings
  • MultiHead Attention Implementation 
  • Feed Forward Implementation
  • Encoder Layer
  • Decoder Layer
  • Transformer
  • Loss with Label Smoothing
  • Defining the Model
  • Training Function
  • Evaluation Function
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No Prerequisites

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