DAY1: 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

DAY2 :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 

DAY3: 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 

DAY4: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 

DAY5: 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. 

1. Interactive led training

2. Training provided by highly qualified and certified trainers, and practical experience necessary to leverage generative AI technologies effectively. 

3. Certification