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Generative Artificial Intelligence

Generative AI Certification - Unlock the Future of AI Innovation

Empower Your Career with Cutting-Edge AI Expertise:

Generative AI is revolutionizing industries by enabling machines to create human-like text, images, code, and more. Whether you are an aspiring AI professional, data scientist, developer, or business leader, our Generative AI Certification from Techtroma provides in-depth knowledge and hands-on experience in harnessing the power of AI to drive innovation.

Why Choose Our Generative AI Certification?
  1. Industry-Relevant Curriculum – Designed by AI experts to cover the latest advancements in machine learning, deep learning, and neural networks.
  2.  Hands-On Learning – Gain practical experience with ChatGPT, DALL·E, Stable Diffusion, GANs, Transformers, and LLMs through real-world projects.
  3. Expert-Led Training – Learn from seasoned AI professionals and industry mentors with extensive experience in AI and data science.
  4. Globally Recognized Certification – Enhance your resume and showcase your AI expertise with a certification from Techtroma, a trusted name in AI education.
  5. Career Advancement Support – Get access to job assistance, resume-building workshops, and networking opportunities with industry leaders.
Who Should Enroll?
  • AI & ML Enthusiasts
  • Data Scientists & Analysts
  • Software Engineers & Developers
  • Business Leaders & Decision-Makers
  • Students & Professionals Looking to Upskill in AI
What You Will Learn?
  • Foundations of Generative AI & Deep Learning
  • Understanding Large Language Models (LLMs)
  • Building AI-Powered Chatbots & Virtual Assistants
  • Image & Video Generation with GANs & Diffusion Models
  • AI for Text Generation & Code Completion
  • Ethical Considerations & Responsible AI Development
Course Highlights
  • Mode: Online & Classroom Sessions Available
  • Duration: Flexible Learning Options (6-12 Weeks)
  • Projects: Real-World AI Projects & Capstone
  • Tools & Technologies: OpenAI, TensorFlow, PyTorch, LangChain, Hugging Face
  • Certification: Industry-Recognized Techtroma AI Certification
Course Curriculum
Module 1: Introduction to ChatGPT and Natural Language Processing (NLP)
  • Understanding the fundamentals of ChatGPT and its role in AI-driven communication
  • Exploring the basics of Natural Language Processing (NLP) and its relevance to prompt engineering
Module 2: ChatGPT Prompt Engineering Essentials
  • Introduction to prompt engineering and its significance in optimizing ChatGPT responses
  • Crafting effective prompts to generate accurate and contextually relevant outputs
  • Strategies for managing conversation context within the prompts
Module 3: Context Management and Conversation Flow
  • Understanding context and its impact on AI-driven conversations
  • Techniques for maintaining coherent and contextually appropriate interactions
  • Handling ambiguous queries and refining prompts for improved conversation flow
Module 4: Advanced Prompt Engineering Techniques
  • Fine-tuning prompt engineering for specific use cases and domains
  • Optimizing response quality by incorporating user preferences and desired outcomes
  • Experimenting with different prompt structures and approaches to enhance ChatGPT performance
Module 5: Ethical Considerations in AI-Powered Communication
  • Exploring ethical implications and responsible AI usage in prompt engineering
  • Addressing biases and promoting inclusive conversations
  • Best practices for ensuring ethical and responsible AI-driven communication
Module 6: Real-World Applications and Case Studies
  • Examining real-world applications of ChatGPT prompt engineering in various industries
  • Analyzing case studies to understand successful implementations and learn from challenges
Module 7: Practical Hands-on Exercises and Projects
  • Engaging in hands-on exercises and projects to apply prompt engineering concepts
  • Developing practical skills through guided practice and feedback from instructors

Generative AI Engineer Project Lifecycle

Phase 1: Problem Definition & Data Preparation.

Define the use case (text, image, or code generation) and set objectives. Collect, clean, and preprocess data for training. Perform Exploratory Data Analysis (EDA) to identify patterns.

Phase 2: Model Development & Fine-Tuning

Select and fine-tune a pretrained model (GPT, Stable Diffusion, GANs) on domain-specific data. Optimize performance using hyperparameter tuning and evaluation metrics like BLEU or FID.

Phase 3: Deployment & Monitoring

Deploy the model via APIs, cloud, or on-device inference. Monitor performance, mitigate biases, and update models based on real-world feedback to ensure accuracy and scalability..

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