Introduction

Generative AI and its applications in various fields such as NLP, Computer Vision, Robotics, and Hallucination Management are transforming industries globally. This course will take you through the fundamentals of NLP, image processing, robotics integration with AI, and strategies to manage hallucinations in AI models. The course also covers advanced tools, deployment strategies, and real-world applications, making it an all-encompassing guide for professionals keen on mastering AI technologies.

This course includes

• 4+ hours on-demand video
• Unlimited Access, Anytime, on mobile and TV
• Certificate of completion 
  

Natural Language Processing (NLP):

In this section, you'll explore the world of Natural Language Processing (NLP). We’ll start with the basics and progress through advanced topics such as Named Entity Recognition (NER), Sentiment Analysis, and Language Generation Models like BERT and GPT. By the end of this section, you'll understand how machines process and interpret human language and the powerful applications of these techniques in industries like customer service and content generation.

Computer Vision:

Dive into the field of Computer Vision, where you'll learn the fundamentals of image processing. This section covers key topics such as feature extraction, object detection, image segmentation, and image generation. By understanding how AI can interpret and manipulate images, you’ll be equipped to develop applications in fields ranging from autonomous vehicles to medical imaging.

Robotics and AI:

Robotics and AI are now intricately linked, and this section introduces you to the cutting-edge technologies driving this evolution. From learning about the role AI plays in robotics to understanding reinforcement learning techniques, this section is designed to give you a foundational knowledge of how robotics and AI complement each other to create intelligent systems.

Hallucination Management in GenAI:

Hallucinations in Generative AI are a significant challenge. This section focuses on managing these errors through detection, evaluation, and mitigation techniques. With practical examples and case studies, you’ll explore advanced strategies to tackle hallucinations in GenAI models, ensuring their robustness in real-world applications.

Integration and Deployment of GenAI:

Understanding how to integrate and deploy Generative AI models is crucial for real-world applications. This section dives into the development landscape, deployment methods, and tools like AWS Bedrock and Anthropic. You’ll also gain hands-on experience with practical examples, preparing you for the challenges of deploying AI solutions in production environments.

AI Tools:

AI tools are essential in the development and deployment of AI models. This section provides a deep dive into the top AI tools used in the industry. With 11 detailed parts, you'll explore a wide range of tools that will help you streamline the development process and integrate AI into various business functions.