Course curriculum

  • 1

    Module 1: Introduction to Artificial Intelligence

    • Definition of AI

    • Brief history and evolution of AI

    • Current state of AI and future trends
  • 2

    Module 2: Foundations of AI

    • Types of AI: Narrow AI, General AI, and Superintelligent AI

    • - Major Approaches to AI: Symbolic, Machine Learning, and Hybrid
  • 3

    Module 3: Basics of Machine Learning

    • Basics of Machine Learning

    • Types of Machine Learning

    • Overview of Deep Learning

    • Understanding BiasVariance Tradeoff
  • 4

    Module 4: AI Technologies and Algorithms

    • Fundamental Algorithms in AI and ML

    • Introduction to Neural Networks

    • Natural Language Processing (NLP)

    • Computer Vision (CV)
  • 5

    Module 5: Practical Applications of AI

    • AI in Healthcare

    • AI in Business and Ecommerce

    • AI in Autonomous Vehicles

    • AI in Entertainment

    • AI in Finance

    • AI in Technology (Ai in Blockchain )

    • AI in Technology (AI in IOT)
  • 6

    Module 6: Ethical Considerations in AI

    • Understanding AI Ethics

    • Potential Pitfalls and Controversies in AI

    • Strategies for Responsible AI Deployment
  • 7

    Module 7: Prompt Engineering

    • What is Prompt Engineering?

    • Importance of Prompt Engineering

    • Applications of Prompt Engineering
  • 8

    Module 8: Understanding Prompts

    • Different types of Prompts

    • Components of a Prompt

    • Understanding Prompt Context

    • Problems and Challenges with Prompts
  • 9

    Module 9: Principles of Effective Prompt Engineering

    • Eliciting Desired Response

    • Eliciting Desired Response Hands On

    • Clarity and Specificity in Prompts

    • Dealing with Ambiguity

    • Handling Sensitive Topics and Content Safeguards

    • Prompt Strategies for Better Output

    • Useful Prompt Templates
  • 10

    Module 10: Creating Effective Prompts

    • Case Studies: Prompt Engineering Examples

    • StepbyStep Process of Creating Prompts

    • Prompt engineering for Text Summarization

    • Prompt engineering for Information Extraction

    • Prompt engineering for Question Answering

    • Prompt engineering for Text Classification

    • Prompt engineering for Code Generation

    • Prompt engineering for Paraphrasing

    • Analyzing and Evaluating Prompt Performance
  • 11

    Module 11: Working with OpenAI API

    • Overview of OpenAI API

    • ChatGPT PlayGround

    • How to setup ChatGPT addon?

    • Get started with ChatGPT in Google Docs

    • Get started with ChatGPT in Google Sheets

    • Data generation trick for ChatGPT in Google Sheets
  • 12

    Module 12: Advanced Prompt Engineering Concepts

    • Zeroshot and Fewshot Prompting

    • Dealing with Biases in Prompt Responses

    • Mitigating Inappropriate or Unwanted Responses

    • Engineering Prompts for Multilingual and Multicultural Contexts

    • Building Iterative and Interactive Prompt Chains
  • 13

    Module 13: Future of Prompt Engineering and AI Conversations

    • Evolution and Trends in AI Conversational Models

    • Career Opportunities in Prompt Engineering

    • Emergence of Opensource Large Language Models
  • 14

    Module 14: Other Popular Large Language Models

    • Bard Model

    • Claude Model

    • GROK AI
  • 15

    Module 14: Introduction to ChatGPT and AI

    • What is ChatGPT?

    • The history of ChatGPT

    • Applications of ChatGPT

    • ChatGPT vs other chatbot platforms

    • Industries using ChatGPT

    • The benefits and limitations of ChatGPT

    • Future developments in ChatGPT technology

    • Ethical considerations related to ChatGPT and AI
  • 16

    Module 15: AI and Machine Learning Concepts

    • What is AI?

    • Types of AI

    • What is Machine Learning?

    • Neural Networks

    • Deep Learning

    • Natural Language Processing (NLP)

    • Computer Vision

    • Robotics and AI
  • 17

    Module 16: Types of AI

    • Narrow AI

    • Strong AI

    • Superintelligence
  • 18

    Module 17: ChatGPT Functionalities and Working

    • How does ChatGPT work?

    • ChatGPT 3 vs ChatGPT 4

    • ChatGPT Functionalities

    • Drafting emails and professional communication

    • Automating content creation

    • Research and information gathering

    • Brainstorming ideas and creative problem solving

    • Best Practices for Using ChatGPT
  • 19

    Module 18: Working with OpenAI API

    • Overview of OpenAI API

    • ChatGPT PlayGround

    • How to setup ChatGPT addon?

    • Get started with ChatGPT in Google Docs

    • Get started with ChatGPT in Google Sheets

    • Data generation trick for ChatGPT in Google Sheets

    • Text Analytics using ChatGPT
  • 20

    Module 19: ChatGPT Job Opportunities

    • Introduction to ChatGPT Job Opportunities

    • Job Search Strategies and Resources

    • Resume and Interview Preparation

    • ChatGPT: Freelance and Entrepreneurial Opportunities

    • Challenges and Opportunities in the Field of ChatGPT
  • 21

    Module 20: Data Privacy with ChatGPT

    • Challenge of data privacy

    • Mitigating data leakage using data masking

    • Using Private Large Language Models
  • 22

    Module 21: Plugins

    • Overview of ChatGPT Plugins

    • Handson with ChatGPT Plugins Wolfram Plugin

    • Handson with ChatGPT Plugins Link Reader Plugin
  • 23

    Module 22: Custom

    • Customize ChatGPT
  • 24

    Module 23: Introduction to Gemini AI

    • What is Gemini AI?

    • Key Features of Gemini AI

    • A brief on Gemini Versions: Nano, Pro, Ultra

    • ChatGPT vs Gemini
  • 25

    Module 24: Gemini Fundamentals

    • Overview on Multimodal models

    • Gemini AI Working Mechanism

    • Gemini: AI Technology Stack

    • Gemini AI Capabilities

    • Best Practices for Using AI Models
  • 26

    Module 26: Using Gemini AI for Creative Content Generation

    • Gemini AI Walkthrough

    • How to use Gemini AI to write a poem

    • How to use Gemini AI to create Youtube video script

    • How to use Gemini AI to generate blog post
  • 27

    Module 27: Using Gemini AI for Productivity

    • How to use Gemini AI to draft Email

    • How to use Gemini AI to write Cover letter

    • How to use Gemini AI to create Resume
  • 28

    Module 28: Using Gemini AI for Code Generation

    • How to use Gemini AI to generate, debug and test code part 1

    • How to use Gemini AI to create a Single Login Portal

    • How to use Gemini AI to create a Database Table

    • How to use Gemini AI to create a Website Template
  • 29

    Module 29: Using Gemini AI for Other Applications

    • How to use Gemini AI for translation

    • Gemini AI for Image translation

    • How to use Gemini AI for research
  • 30

    Module 30: Introduction to Generative AI

    • What is Generative AI?

    • Generative AI vs NonGenerative AI

    • Applications of Generative AI
  • 31

    Module 31: Generative AI for Text

    • Understanding Text Data

    • Introduction to Generative AI for Text

    • Overview of ChatGPT

    • ChatGPT in Action for Text Generation

    • Using Google Bard for Text Generation
  • 32

    Module 32: Generative AI for Images

    • Introduction to AI for Image Generation

    • Introduction to Stable Diffusion AI Models

    • Overview of DreamStudio Platform

    • Generating Images with Stable Diffusion

    • Editing Images with Stable Diffusion

    • Prompt Engineering for Image Generation

    • Challenges in Generative AI for Images
  • 33

    Module 33: Generative AI for Enterprises

    • What is Enterprise AI

    • Regular AI vs Enterprise AI

    • Introduction to Generative AI for Enterprises

    • Benefits of Generative AI in Business

    • Overcoming Challenges in Adopting Generative AI
  • 34

    Module 34: Generative AI for Public Services

    • Relevance of Generative AI for Public Services

    • Benefits of Implementing Generative AI in Public Services

    • Structure of a Generative AI Project

    • Generative AI in Education

    • Generative AI in Healthcare

    • Generative AI in Tourism

    • Challenges and Solutions in Applying Generative AI to Public Services
  • 35

    Module 35: Data Privacy in AI

    • Data Privacy Risks in Generative AI Systems

    • Mitigating Data Leakage using Data Masking

    • Privacy by Design in AI

    • Implementing a Data Privacy Culture
  • 36

    Module 36: Prompt Engineering for Text Analysis

    • Prompt engineering for Text Summarization

    • Prompt engineering for Information Extraction

    • Prompt engineering for Question Answering

    • Prompt engineering for Text Classification

    • Prompt engineering for Code Generation

    • Prompt engineering for Paraphrasing

    • Analyzing and Evaluating Prompt Performance
  • 37

    Module 37: Upcoming Trends in Generative AI

    • Generative AI for Sound

    • Generative AI for Videos

    • Other Gen AI Trends
  • 38

    Module 38: Getting Started with LLM

    • Hugging face

    • Overview of LLAMA2 and Gemma

    • Fine tunning Gemma
  • 39

    Module 39: Introduction to AI for Programmers

    • Overview of AI tools for coding

    • Importance and applications of AI in programming
  • 40

    Module 40: Github Copilot

    • Introduction to GitHub Copilot

    • Setting Up GitHub Copilot

    • Integrating Copilot into coding workflows
  • 41

    Module 41: Practical Usage of GitHub Copilot

    • Writing Code for a Landing Page

    • Debugging using GitHub Copilot
  • 42

    Module 42: ChatGPT for Programmers

    • Introduction to ChatGPT

    • Integrating ChatGPT into Coding Workflows

    • Practical Examples and Use Cases
  • 43

    Module 43: Leonardo AI for UI/UX

    • Overview of Leonardo AI

    • Leonardo AI for Image Generation
  • 44

    Module 44: Introduction to Large Language Models

    • LLM Overview

    • Evolution of LLMs

    • Capabilities and Limitations of LLMs

    • Applications and use cases of LLMs
  • 45

    Module 45: Core LLM Technologies

    • Tokenization, Vectors and Embeddings

    • Attention Mechanism and its variants

    • Introduction to Transformer Architecture

    • Creating Custom Language Models

    • Transfer Learning in NLP

    • Evaluation Metrics for LLMs: BLEU, ROUGE, Perplexity

    • Introduction to Hugging Face Transformers library

    • Overview of llama2 and Gemma

    • Fine Tuning Gemma Model
  • 46

    Module 46: Advanced LLM Techniques

    • Overview of popular LLMs: GPT-3/4, BERT, T5

    • Fine-tuning pre-trained models for specific tasks

    • BERT and its variants: RoBERTa, DistilBERT

    • GPT and its applications in text generation

    • Exploring other models: T5, XLNet, ELECTRA

    • Building conversational agents and chatbots - Part 1

    • Building conversational agents and chatbots - Part 2

    • Creative applications: text generation, storytelling - Part 1

    • Creative applications: text generation, storytelling - Part 2

    • Ethical considerations and bias mitigation in LLMs
  • 47

    Module 47: Computer Vision

    • Computer Vision (CV)

    • Introduction to Neural Networks

    • CNN from Scratch

    • CNN using Tensorflow
  • 48

    Module 48: Audio/Video Coding

    • Basics of audio signal processing

    • Feature extraction: MFCCs, Spectrograms - Part 1

    • Feature extraction: MFCCs, Spectrograms - Part 2

    • Audio classification and speech recognition - Part 1

    • Audio classification and speech recognition - Part 2

    • Basics of video signal processing

    • Frame extraction and video feature analysis - Part 1

    • Frame extraction and video feature analysis - Part 2
  • 49

    Module 49: LLM Frameworks and Tools

    • LangChain - Langchain for Conversational AI Applications

    • LangChain - Deploying Language Model APIs with Langchain

    • LangChain - Langchain for RAG Workflows

    • Ollama - Overview of Ollama for conversational AI

    • Ollama - Developing and deploying conversational agents with Ollama
  • 50

    Module 50: Practical Projects

    • Project 1: Text Classification Model

    • Bert_text_classification

    • Bert_text_classification_app

    • Data preparation and preprocessing

    • Text Generation Model

    • Project 2- Text Generation Model

    • Evaluation and fine-tuning

    • Project3 - Designing a conversational agent architecture

    • Conversational Agent

    • Conversational Agent App

    • Conversational Agent OpenAI

    • Conversational Agent OpenAI App

    • Conversational Agent LangChain

    • Conversational Agent Ollama

    • Conversational Agent Hugging Face
  • 51

    Module 51: Deployment and MLOps

    • Introduction to MLOps concepts and practices

    • Continuous Integration and Continuous Deployment

    • Monitoring model performance in production

    • Handling model drift and retraining

    • Automated model testing and validation