Prompt Engineering Course in Hyderabad

1. Introduction to prompt engineering.
2. what is prompt
3. what is response
4. Introduction to chat-gpt
5. Other AI tools similar to chat-gpt like bard, bing .
6. What is Artificial Intelligence
7. Introduction to Machine learning
8. Types of Machine Learning
-> Supervised Learning
-> Unsupervised learning
-> Reinforcement Learning.
9. Introduction to Neural Networks.
10.Introduction to Deep Learning.
11. Types of Deep learning .
-> Descriminative models
-> Generative models
10. Introduction to NLP (Natural Language Processing).
11. NLP with Machine Learning
12. NLP with Deep Learning.
13. LLM (Large language Modeling) introduction.
14. difference between NLP and LLM
15. Introduction to Generative AI.
16. Generative AI applications.
17. Generative AI use cases.
18. Major tasks in prompt engineering as below.
-> prompt to Text content
-> prompt to Image generation
-> prompt to Audio generation
-> prompt to video generation
-> prompt to task execution.
19. many tools available in market, what does prompt
engineer has to do ?
20. prompt engineering challenges.
21. Summary of this module.

1. what are prerequisites to learn prompt engineering and generative AI.
2. python programming fundamentals
3. python numpy essentials for AI, GenAI
4. python pandas essentials for AI, GenAI
5. What is a model in Machine Learning.
6. Types of Machine Learning Models.
-> predictive models
-> forecasting models
-> recommendation models.
7. Python Libraries for machine learning
8. difference between statistical approach and Machine Learning
approach.
9. Building a predictive model with Python numpy
-> regression model
-> classification model
10. Building a predictive model with Python-scikit learn
11. Building a classification model with python numpy
12. Building a classification model with python-scikit learn
13. sentiment analysis with nlp and ml (python)
14. sentiment analysis with nlp and DL (python)
15. sentiment analysis with Python-keras
15. how to evaluate model accuracy and best strategies.
16. Building unsupervised model (K-means)
17. summary of this module.

1. chatGpt, chatGpt models and Large Language models (LLMs)
2. LLMs and Neural networks and Deep Learning
3. Introduction to ChatGpt API and Open AI
4. Understanding Concept of ChatGpt Tokens and Temperature
5. What is Word Prediction(next word)
6. Model Fine Tuninging and Moderation
7. Introduction to Dall-E
8. what are different APIs available and Introduction
->Open AI
-> LLama
-> LangChain
-> HuggingFace
9. chatGpt’s Open AI limitations .
10. what LangChain can additionally do ?

1. more on prompt engineering
2. Prompt Engineering: What & Why?
3. Prompt Engineering & ChatGPT Custom Instructions
4. The Core Elements Of A Good Prompt
5. It’s a Chat! Use it!
6. Which Context Should You Add?
7. task and Solution
8. Zero-, One- & Few-Shot Prompting
9. Using Output Templates
10. Providing Hints To ChatGPT
11. Separating Instructions From Content
12. Ask-Before-Answer Prompting
13. Perspective Prompting
14. Contextual Prompting
15. Emotional Prompting
16. Laddering Prompting
17. Using ChatGPT For Prompting
18. Find Out Which Information Is Missing
19. Self-evaluative Prompting
20. ChatGPT-powered Problem Splitting
21. Let ChatGPT Share Its Thought Process
22. Reversing Roles
23. More Prompts & Finding Prompt Inspirations
24. Super Prompts Like CAN & DAN
25. Module Summary

1. prompt to Text Content Generation with Open AI (chatgpt api)
2. prompt to Text Content Generation with Open AI and LangChain
3. prompt to Text Content Generation with HuggingFace and LangChain
4. prompt to Image Generation with Open AI (dall-e) and LangChain
5. prompt to Audio Generation with Open AI and LangChain.
6. prompt to Video Generation with Open AI and LangChain.
7. special use cases with LangChain.
8. Implementation of custom chatbots with Open AI and LangChain.
9. Fine tuning models.
11. Introduction to DevOps, MLOps, LLMOps.
12. Deployment of your application using LLMOps.