Unlock the Power of Generative AI
Course Overview
Generative AI is revolutionizing data science by enabling machines to create entirely new data, from realistic images to creative text formats. This course equips data scientists with the fundamentals and hands-on experience to leverage Generative AI for various applications.
After Completing this Generative AI, students will be able to:
- Explain the core concepts and applications of Generative AI.
- Differentiate between Discriminative and Generative AI.
- Master the art of Prompt Engineering to guide AI content creation.
- Understand and utilize Recurrent Neural Networks (RNNs) for sequence modeling.
- Implement and fine-tune Transformer models for Natural Language Processing (NLP) tasks.
- Work with Large Language Models (LLMs) for text generation.
- Build Generative Adversarial Networks (GANs) to generate and manipulate images.
- Employ Variational Autoencoders (VAEs) for image representation learning.
- Apply Generative AI for creative image synthesis and explore its ethical considerations.
Who can participate in the training?
- Data Scientists
- Machine Learning Engineers
- NLP Professionals
- Researchers interested in Generative AI applications
Prerequisites of Generative AI Training
- Basic understanding of Python programming.
- Familiarity with Machine Learning concepts.
Key Features of the Course
- 8 engaging modules with in-depth explanations and practical exercises.
- Hands-on Labs for real-world implementation of Generative AI techniques.
- Industry-relevant curriculum aligned with cutting-edge advancements.
- Expert-led instruction from experienced data science professionals.
- Interactive learning environment to foster knowledge sharing and collaboration.
- 24/7 Discussion Forums: Collaborate with peers and resolve queries.
Why Choose CloudZee?
- Microsoft Gold Partner
- 1,000+ Professionals Trained
- Job-Ready Skills
- Learn Anytime
Course Curriculum
Introduction to Generative AI
This module provides an overview of generative AI, exploring its core concepts and applications.
Recurrent Neural Networks (RNNs)
This module dives into RNNs, a type of neural network essential for sequence modeling tasks.
Transformers for Sequence Modelling
Here, you’ll learn about transformers, a powerful architecture becoming increasingly prominent in sequence modeling.
Language Modelling and Large Language Models (LLMs)
This module explores language modeling, a technique for generating text, and discusses LLMs, a type of generative AI with advanced capabilities.
Generative Adversarial Networks (GANs)
This module covers GANs, a type of generative AI where two neural networks compete to create realistic outputs.
Deep Generative Models for Computer Vision
Here, you’ll explore deep generative models specifically designed for computer vision tasks like image generation.
Generative AI in Image Synthesis
This module focuses on applications of generative AI in creating new images or modifying existing ones.
Ethical Considerations in Generative AI
This module explores the ethical implications of generative AI, covering potential biases and responsible development practices.
Learning Outcomes
By the end of this course, you will be able to:
Understand the core principles of Generative AI and how it differs from traditional AI models
Deploy and interact with AI models using Azure OpenAI, including GPT-based text generation
Use Microsoft Copilot features across Word, Excel, PowerPoint, and Teams to boost productivity
Apply responsible AI principles, including ethics, fairness, and data privacy
Exam Details & Certification
FAQs
Instructor
“Loved the hands-on approach to teaching. The modules on Azure OpenAI and Microsoft Copilot were especially useful. Great pace and relevant examples that matched real-world business use cases.”
— Natasha, Cloud Engineer