Course Outline:
Introduction to IBM Watson:
Overview of IBM Watson and its applications
Understanding AI, machine learning, and deep learning
Setting up an IBM Cloud account
IBM Watson Services Overview:
Watson Studio: Data science and machine learning
Watson Assistant: Building chatbots
Watson Discovery: Text analytics and search
Watson Knowledge Catalog: Data cataloging and governance
Watson Natural Language Understanding: Text analysis and insights
Watson Speech to Text and Text to Speech: Audio processing
Watson Visual Recognition: Image analysis
Watson Studio:
Introduction to Watson Studio
Creating and managing projects
Data preparation and cleansing
Building and deploying machine learning models
Using AutoAI for automated model building
Watson Assistant:
Introduction to Watson Assistant
Designing and building a chatbot
Creating intents, entities, and dialogs
Integrating with other services and platforms
Deploying and managing chatbots
Watson Discovery:
Introduction to Watson Discovery
Ingesting and enriching data
Creating queries and retrieving insights
Building search and analytics applications
Customizing and deploying Watson Discovery
Watson Natural Language Understanding (NLU):
Introduction to Watson NLU
Analyzing text for sentiment, emotion, entities, and concepts
Customizing models for specific use cases
Integrating NLU with other services
Watson Speech Services:
Speech to Text: Converting speech to text
Text to Speech: Converting text to natural-sounding speech
Customizing speech models
Real-time and batch processing
Watson Visual Recognition:
Introduction to Watson Visual Recognition
Analyzing and classifying images
Training custom image classifiers
Integrating visual recognition with other services
Watson Knowledge Catalog:
Introduction to Watson Knowledge Catalog
Organizing and cataloging data assets
Managing data governance and compliance
Enabling data discovery and collaboration
Watson Machine Learning:
Building and training machine learning models
Model evaluation and optimization
Deploying and managing models
Using Watson Machine Learning API
Integration and Deployment:
Integrating Watson services with other applications and systems
Using APIs and SDKs for custom development
Best practices for deploying and scaling AI solutions
Practical Applications and Case Studies:
Real-world examples and case studies
Practical exercises and projects
Best practices and tips for successful AI implementation
Skills Gained:
Proficiency in using IBM Watson for AI and machine learning
Ability to build, deploy, and manage AI models and applications
Skills in natural language processing, speech recognition, and image analysis
Knowledge of integrating Watson services with other systems and applications
Competence in data preparation, governance, and cataloging
Target Audience:
Data scientists
AI developers
Business analysts
IT professionals
Students and professionals interested in AI and machine learning