Generative AI

Generative AI Course Syllabus

This course provides an in-depth understanding of Generative AI, covering fundamental concepts, frameworks, and applications. By the end of the course, learners will be able to build and deploy AI-powered solutions.

1. Python and Streamlit

Duration: 5 lessons, 1 week

Subtopics:

- Introduction to Python for AI

- Setting up Streamlit

- Building interactive AI applications

- Deploying Streamlit apps

- Best practices and performance optimization

2. Statistics and Probability

Duration: 1 lesson, 2 days

Subtopics:

- Basic probability concepts

- Descriptive statistics

- Inferential statistics overview

3. Machine Learning

Duration: 4 lessons, 1 week

Subtopics:

- Supervised vs Unsupervised Learning

- Feature Engineering

- Model Selection and Evaluation

- Hyperparameter Tuning

4. Deep Learning

Duration: 3 lessons, 1 week

Subtopics:

- Neural Networks Basics

- CNNs and RNNs

- Model Training and Optimization

5. Natural Language Processing

Duration: 2 lessons, 4 days

Subtopics:

- Text Preprocessing

- Word Embeddings and Tokenization

6. LangChain

Duration: 3 lessons, 1 week

Subtopics:

- Introduction to LangChain

- Building AI Agents

- Integrating LLMs with LangChain

7. Transformers

Duration: 3 lessons, 1 week

Subtopics:

- Introduction to Transformer Models

- Hugging Face Library Overview

- Fine-tuning Transformers for AI Applications

8. VectorDB

Duration: 2 lessons, 4 days

Subtopics:

- Understanding Vector Databases

- Using VectorDB for AI Applications

9. Prompt Engineering

Duration: 1 lesson, 2 days

Subtopics:

- Techniques for Effective Prompting

10. Large Language Model (LLM)

Duration: 2 lessons, 4 days

Subtopics:

- Understanding LLMs

- Training and Fine-tuning LLMs

11. Generative AI Interview Questions

Duration: 1 lesson, 2 days

Subtopics:

- Common Interview Questions and Answers