• Module 1 : Introduction to Python 

  • What is Python
  • Why Python for machine learning
  • Setting up Python environment (Anaconda/Python.org)

  Module 2 : Basic Python Syntax

  • Variables
  • Basic data types (integer, float, string)
  • Lists and dictionaries
  • Control structures (for, while, if)

  Module 3 : Functions in Python 

  • Defining functions
  • Calling functions
  • Parameters and arguments
  • Run app on OS as a service and verify app running

  Module 4 : Introduction to Docker  

  • What is Docker
  • Why use Docker
  • Docker Installation

  Module 5: Docker Basics  

  • Docker architecture
  • Docker Images and Containers
  • Docker basic commands (pull, run, ps, stop, rm)

  Module 5 : Dockerfile and Building Docker Images  

  • What is a Dockerfile
  • Writing a basic Dockerfile
  • Building an image using Dockerfile

  Module 6: Running Python App in Docker  

  • Creating a Python app
  • Writing a Dockerfile for Python app

  Building image and running Python app in Docker

Module 1 : Introduction to Machine Learning 

• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, Reinforcement
• Real-world applications of Machine Learning

Module 2 : Python Libraries for Machine Learning 

• NumPy: Basics and Array Operations
• Pandas: Data Manipulation and Analysis
• Matplotlib/Seaborn: Data Visualization

Module 3 : Introduction to Supervised Learning 

• Understanding Regression: Simple Linear Regression, Multiple Linear Regression
• Understanding Classification: Logistic Regression, K-Nearest Neighbors

Module 4 : Introduction to Unsupervised Learning 

• Understanding Clustering: K-Means Clustering
• Understanding Dimensionality Reduction: Principal Component Analysis

Module 5 : Building a Simple Machine Learning Model 

• Choosing the right model and learning algorithm
• Training a model using Scikit-Learn

Evaluating model performance

Module 1 :  Definition and Types of AI

• Overview of AI and its types: Narrow AI, General AI, and Superintelligent AI.
• Discussion on Generative AI: Definition, types, and applications.

Module 2 :  Introduction to ChatGPT

• Introduction to Generative Pre-training Transformer (GPT) by OpenAI (1 hour)
• Overview of GPT, its versions, and usage.

Module 3 :  Introduction to Google’s Bard

• Overview of Bard, its functionality, and usage.
• Comparison between GPT and Bard.

Module 4 : NLP  and Text Generation  

• Using GPT for Text Generation
• Prompt engineering
• How GPT works for text generation.
• Demo and hands-on: Creating a text generation script using GPT.

Module 5 :  Using Google’s Bard for Text Generation (2 hours)

• How Bard works for text generation.
• Demo and hands-on: Creating a text generation script using Bard.

Module 1 :  Writing Chatbots using GPT (2 hours)

• Basics of chatbot scripting and dialogue management.
• Demo and hands-on: Developing a simple chatbot using GPT.

Module 2 :  Voice Generation using Play.ht  

• Introduction to Play.ht
• Overview of the tool, its features, and usage.
• Demo and hands-on: Voice Generation using Play.ht
• Creating voiceovers for generated text using Play.ht.

Module 3 :  Using  ChatGPT and Langchain to work on private data

• Brief Introduction Natural Language Processing (NLP)
• Understanding ChatGPT and Langchain: Basics and Applications
• API Integration and Fine-tuning of ChatGPT
• Langchain Overview, Architecture, and Components
• ETL (Extract, Transform, Load) Process and Processing Unstructured Data with Langchain

Module 4  :  Handson Lab on Working on private data using ChatGPT

• using ChatGPT and Langchain to Extract and Process Website Data
• Using Langchain for PDF Text Extraction and Processing
• Real-World Examples and Case Studies of Web Data and PDF Processing with ChatGPT and Langchain

Module 5 :  Image Generation using Leonardo.ai (5 hours)

• Introduction to Leonardo.ai
• Overview of the tool, its features, and usage.
• Demo and hands-on: Image Generation using Leonardo.ai

Module 1 :  AI Video Generation (3 hours)

• Introduction to AI in Video Generation
• Overview of the use of AI in video creation.
• Discussion on different tools available for AI video generation.
• Demo and hands-on: AI Video Generation

Module 2 :  AI Music Generation using Google’s Magenta  

• Introduction to Google’s Magenta
• Overview of Magenta, its features, and usage.
• Demo – Generating a music piece using Magenta.

Module 3 :  Building a WhatsApp Bot using GPT

• Introduction to WhatsApp Bots
• Overview of WhatsApp Bots, their applications, and functionalities.
• Demo and hands-on: Building a WhatsApp Bot using GPT

Module 4 :  Building an Android App using GPT 

• Introduction to Android App Development with AI
• Discussing the integration of AI in mobile applications.
• Creating a simple Android app integrating GPT.

Module 5 :   Course Conclusion and Next Steps  

• Review of the course
• Revisiting the course content, hands-on projects, and key learnings.
• Discussion on potential use-cases and applications of Generative AI
• Brainstorming session on various innovative applications of Generative AI.