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Generative AI Certification Training
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About Course
🌐 What Is Generative AI?
Generative AI is an advanced branch of artificial intelligence focused on creating new and original content such as text, images, videos, code, and audio using machine learning models. It leverages deep learning architectures like Generative Adversarial Networks (GANs) and Large Language Models (LLMs) (e.g., GPT, Gemini, Claude) to generate human-like outputs. Generative AI is transforming industries by automating creativity, enabling intelligent automation, and accelerating innovation across multiple domains such as marketing, design, software development, and research.
Its core capabilities include:
- Content Generation: Automatically produce text, visuals, audio, and videos.
- Code Automation: Generate and optimize programming code with AI assistance.
- Conversational AI: Develop intelligent chatbots and virtual assistants.
- Data Augmentation: Enhance training data for ML models with synthetic data.
- Creative Design & Ideation: Support innovation in art, marketing, and product design.
📊 Course Features Typically Included
- ✅ Live instructor-led training and interactive sessions
- ✅ Hands-on labs with tools like ChatGPT, DALL·E, Midjourney, and Stable Diffusion
- ✅ Real-world projects on AI text, image, and video generation
- ✅ Assignments and portfolio-building exercises
- ✅ Certification and career guidance
- ✅ Lifetime access to learning resources and updates
🎓 Key Learning Outcomes
After completing the Generative AI Online Training, learners will be able to:
- Understand LLMs, GANs, and diffusion models architecture and workflows
- Build AI-powered chatbots, content generators, and image creators
- Integrate generative AI into applications, APIs, and workflows
- Use prompt engineering and fine-tuning for custom model behavior
- Apply ethical AI principles and responsible use frameworks
- Deploy generative AI solutions on cloud platforms like AWS, Azure, and Google Cloud
These skills prepare learners for roles such as:
Generative AI Engineer, AI Developer, Machine Learning Specialist, Prompt Engineer, AI Solution Architect, Data Scientist (AI)
📍 Bonus: Certification Tracks
Generative AI with Large Language Models (by AWS & DeepLearning.AI)
- Google Cloud Certified – Generative AI Engineer
- Microsoft Certified: Azure AI Engineer Associate
- Viswa Online Trainings – Generative AI Professional Certification
Generative AI Training Course Syllabus
Introduction to Generative AI
- Overview of Generative AI
- Generative AI vs. Traditional AI
- Use Cases
- Understanding AI: Basics and Use Cases
- Differentiating ML, DL and AI
Basics On NLP 1
- What is NLP?
- History of NLP
- NLP End to end workflow
- Stopwords
- Tokenization
- Stemming
- Lemmatization
- POS tagging
- TFIDF
Basics On NLP 2
- One hot encoding
- Bag of words
- Unigram
- Bigram
- ngram
- Word embeddings Skip Gram
- Word2vec model
NLP Models
- RNN
- LSTM Models & GRU Models
- Transfer learning
Advanced NLP
- Encoder-decoder architecture
- Attention mechanism
- Transformer
- BERT
Understand The Working Of LLMs
- LLM
- Use Cases
- Text Generation
- Chatbot Creation
- Foundations of Generative Models & LLM
- Generative Adversarial Networks (GANs)
- Autoencoders in Generative AI
- Significance of Transformers in AI
- “Attention is All You Need” – Transformer Architecture
- Reinforcement Learning
LLMs Foundation Models
- Encoder Models i.e.
- BERT
- Decoder Models GPT
- Encoder Decoder Model i.e.
- T5
Fine Tuning And Evaluating LLMs
- Instruction fine-tuning
- Fine-tuning on a single task
- Multi-task instruction fine-tuning
- Model evaluation
- Benchmarks
- Parameter efficient fine-tuning (PEFT)
- PEFT techniques 1: LoRA
- PEFT techniques 2: Soft prompts
- Lab 2 walkthrough
Evaluation Matrix
- Rouge1
- BLEU
- Meteor
- CIDEr
Reinforcement Learning And LLM Powered Application
- Encoder-decoder architecture
- Attention mechanism
- Transformer
- BERT
LLMOps
- Encoder-decoder architecture
- Attention mechanism
- Transformer
- BERT
Generative AI On Cloud – GCP
- In-depth GCP
- Model Evaluation
- Prompt Design
Generative AI On Cloud – Azure
- Azure ML
- Azure Cognitive Services
- Azure Databricks
Generative AI On Cloud – AWS
- AWS Sagemaker
- AWS Jumpstart
- AWS Bedrock
Ethics And Responsibility In Generative AI
- Responsible AI
- Google’s Approach
- Ethical Issues
Generative AI Course Key Features
- Live Instructor based training with software
- Certification Oriented content
- Hands-on complete Realtime training
- Flexible schedule demo's & classes
- Live recorded videos access
- Study material provided
- JOB Assistance
Course completion certificate
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CHOOSE YOUR OWN COMFORTABLE LEARNING EXPERIENCE
Live Virtual Training
- Schedule your sessions at your comfortable with timings
- Instructor-Led training real time projects
- Certification Guidance
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Self-Paced Learning
- Complete set of live-online training session recorded videos.
- Learn technology at your own pace.
- Get access for lifetime.
Corporate Training
- Learn as a full day schedule with discussions, exercises.
- Practical use cases.
- Design your own syllabus based
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Generative AI Online Training FAQ'S
What is Generative AI?
Generative AI refers to AI systems that can create new content such as text, images, music, or code by learning from large datasets.
What are some popular Generative AI models?
Examples include GPT (text), DALL·E (images), Stable Diffusion, MidJourney, and WaveNet (audio).
What is Prompt Engineering?
Prompt engineering is the process of designing and refining input prompts to guide generative AI models for better outputs.
What is the difference between LLMs and Diffusion Models?
LLMs generate text/code by predicting words, while Diffusion Models generate images/audio by iteratively refining noise into data.
What are the main applications of Generative AI?
Applications include chatbots, content creation, code generation, design, drug discovery, simulations, and personalization.
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