Generative AI Course Training in Bangalore
Career-Ready Generative AI Program (GAI)*
Master Generative AI with hands-on training in ChatGPT, Prompt Engineering, LLMs, LangChain, AI Agents, and OpenAI APIs. Build real-world AI projects and gain job-ready skills.
5,000+
AI Learners Trained
1,500+
Industry Connections
1-Year Flex Pass
Learning Access
Generative AI Course Training in Bangalore
Career-Ready Generative AI Program (GAI)*
Master Generative AI with hands-on training in ChatGPT, Prompt Engineering, LLMs, LangChain, AI Agents, and OpenAI APIs. Build real-world AI projects and gain job-ready skills.
8,000+
Students Trained
20+
Industry Experts
Duration
40 Hours Intensive Training Hands-On AI Learning Program
Learning Mode
Classroom & Live Online Interactive Instructor-Led Sessions
Live Projects
5+ Real-World AI Projects Build AI Assistants, Chatbots & Automation Tools
Certification
Industry-Recognized Certificate Generative AI Course Completion Certificate
Career Support
Portfolio & Interview Preparation AI Career Guidance & Mentorship
Flex Pass Access
1-Year Learning Access Re-Attend Future Batches at No Extra Cost
Course Overview
The NexEdge Generative AI Program is designed to equip learners with the most sought-after skills in Artificial Intelligence and emerging AI technologies. Combining expert-led instruction with hands-on practical learning, this program covers Prompt Engineering, Large Language Models (LLMs), AI Agents, OpenAI APIs, LangChain, Retrieval-Augmented Generation (RAG), and real-world AI applications.`

Industry Expert Trainers

Comprehensive AI Curriculum

Career-Focused Learning

Industry Expert Trainers
International Certifications

NLP

Google Ads Creative Certification

LLM

Prompt engineering

Generative AI

Generative AI on Cloud
Tools Covered

Python

Keras

TensorFlow

NLTK

BERT

Hugging face

LangChain

ChatGPT

DALL-E 2
Contact Our Team of Experts
Course Curriculum
Generative AI
- Overview of Generative AI
- Generative AI vs. Traditional AI
- Use Cases
- Understanding AI: Basics and Use Cases
- Differentiating ML, DL and AI
- What is NLP?
- History of NLP
- NLP End to end workflow
- Stopwords
- Tokenization
- Stemming
- Lemmatization
- POS tagging
- TFIDF
- One hot encoding
- Bag of words
- Unigram
- Bigram
- ngram
- Word embeddings Skip Gram
- Word2vec model
- RNN
- LSTM Models & GRU Models
- Transfer learning
- Encoder-decoder architecture
- Attention mechanism
- Transformer
- BERT
- Hands-on experience with text translation using the encoder-decoder architecture
- 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
- RLHF
- Encoder Models i.e.
- BERT
- Decoder Models GPT
- Encoder Decoder Model i.e.
- T5
- Real-world applications and case studies of 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
- Rouge1
- BLEU
- Meteor
- CIDEr
- Reinforcement Learning
- LLM Applications
- Deployment Strategies
- Hardware Requirements
- Langchain: A Framework for LLMs
- LLM Operations
- Scalability
- Best Practices
- Hugging Face
- GCP and Hugging Face Overview
- In-depth GCP
- Model Evaluation
- Prompt Design
- Azure ML
- Azure Cognitive Services
- Azure Databricks
- AWS Sagemaker
- AWS Jumpstart
- AWS Bedrock
- Responsible AI
- Google's Approach
- Ethical Issues
ChatGPT
- Foundations of NLP
- Introduction to NLP
- Key Concepts and Terminologies
- NLP Techniques and Algorithms
- Chatbots and Their Evolution
- Definition of Chatbots
- Evolution of Chatbots
- Types of Chatbots
- Introduction to OpenAI and LLMs
- Introduction to OpenAI and LLMs
- What are LLMs?
- How do LLMs work?
- Types of LLMs
- Practical uses of LLMs
- Introduction to GPT and ChatGPT
- Overview of GPT
- ChatGPT Capabilities
- GPT Architecture
- Understanding GPT-3, GPT 3.5, and GPT-4
- GPT-3 vs GPT-4
- Advancements in GPT-4
- Ethical Considerations
- Setting Up the ChatGPT Environment
- Accessing OpenAI API
- API Keys and Rate Limits
- Setup for Development
- Building a Simple Chatbot with ChatGPT
- Conversation Flows
- GPT in Chatbots
- Testing and Iteration
- Training and Fine-tuning ChatGPT
- Transfer Learning
- Pre-training and Fine-tuning ChatGPT
- Data for Training
- Dataset Preparation
- Fine-tuning Techniques
- Model Performance Monitoring
- Integrating ChatGPT with Other Services
- Webhooks and APIs
- Integration with Platforms
- Chatbots for Social Media
- Advanced Conversation Design
- Context and Long Conversations
- Personality and Tone
- Advanced Scripting
- RLHF and ChatGPT
- Reinforcement Learning Principles
- Human Feedback in Training
- Role of RLHF in GPT
- ChatGPT for Business Applications
- Customer Service Automation
- Personal Assistants
- Sales and Marketing Bots
- Safety and Ethical Considerations
- Bias Detection and Mitigation
- Ethical AI Use
- Safety Measures
- The Future of ChatGPT and Conversational AI
- Trends and Predictions
- Potential Upgrades
- Future of AI and Society
- Future of Chatbots and Conversational AI
- Beyond ChatGPT: The next frontier
- Opportunities and challenges
Prompt Engineering
- Understanding AI: Descriptive vs Generative AI
- The nature of AI
- Comparison of descriptive and generative AI
- Introduction to Natural Language Processing
- Core concepts in NLP
- Basics of language understanding
- Understanding Large Language Models (LLMs)
- Overview of LLMs
- Their scope
- Capabilities
- Use cases
- Introduction to GPT & Chat GPT
- What is GPT
- Its evolution
- Generational changes
- The Fundamentals of Prompt Engineering
- What is prompt engineering
- Its importance
- Types of prompts
- Content Generation with Prompts
- Strategies for generating text
- Video scripts
- Music using prompts
- Tokens and Parameters in AI
- The role and understanding of tokens
- Introduction to prompt parameters
- Zero-Shot to Few-Shot Learning
- Deep dive into zero-shot
- One-shot
- Few-shot learning
- Fine-Tuning AI Model Parameters
- Introduction to model parameter adjustments
- Hallucinations and Bias in AI
- Strategies for managing AI hallucinations and biases
- Advanced Prompt Engineering Techniques
- Methods for crafting complex prompts
- Incorporating creativity and context
- Refining and Optimizing Prompts
- Techniques for prompt refinement and iterative improvement
- Metrics for Evaluating Prompts
- How to assess prompt quality and performance
- Human Evaluation of Prompts
- Techniques for collecting and analyzing human feedback on prompts
- Testing Prompts on Different Models and Tasks
- How to assess prompt performance across different AI models and tasks
- Natural Language Processing
- Question-Answering Systems
- Conversational AI
- Sentiment Analysis
- Text Summarization
- Code Generation with Prompt Engineering
- GitHub Copilot Exploration
- Image & Video Content Creation using prompt engineering
- Using Midjourney and other tools. DALL-E 2 and GPT-4: A Comprehensive Overview Exploring the capabilities and limitations of DALL-E 2 and GPT-4 Real-world scenarios, case studies, tool-specific tips
- Music Generation with Prompt Engineering
- Create Poem
- Music
- Ethics & Bias in Prompt Engineering
- Ethical considerations
- AI transparency
- Responsible AI usage
- The Future of ChatGPT and Conversational AI
- Trends and Predictions
- Potential Upgrades
- Future of AI and Society
- Future of Chatbots and Conversational AI
- Beyond ChatGPT: The next frontier
- Opportunities and challenges
- Contact Form
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