Machine Learning for Business

Welcome to the Machine Learning for Business course materials repository.
This book is designed and maintained by Dr. Chandravesh Chaudhari.

The goal of this project is to bridge the gap between machine learning theory and real-world business applications, providing learners with hands-on labs, case studies, and practical deployment workflows.

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Course Overview

This course takes you from mathematical foundations to state-of-the-art machine learning and AI systems with a strong focus on business decision-making.

Key features:

• Math & Probability Foundations (quick refresher)
• Data Wrangling & Visualization for business insights
• Supervised & Unsupervised Learning with applied labs
• Time Series Forecasting for inventory & sales planning
• Neural Networks, Transformers & LLMs
• LLM Agents & Generative AI for business use cases
• ️ Production ML Essentials (monitoring, drift detection, dashboards)
• Capstone Projects & Practical Exam

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Table of Contents (Selected Highlights)

• Course Introduction
  Course goals, roadmap, and prerequisites

• Math & Notation Foundations
  Quick review of linear algebra, calculus, probability

• Data Wrangling & Visualization
  Loading, cleaning, dashboards for decision-making

• Supervised Learning (Regression & Classification)
  With applied labs such as Sales Forecasting and Churn Prediction

• Opinion Mining (Sentiment Analysis)
  Applied NLP lab on customer reviews

• Tree-Based Models & Ensembles
  Decision Trees, Random Forests, XGBoost

• Time Series & Forecasting
  ARIMA, Prophet, inventory planning case study

• Deep Learning & Transformers
  CNNs, RNNs, LSTMs, Transformers, Fine-tuning BERT

• LLM Agents for Business
  LangChain, tool-augmented LLMs, workflow orchestration

• Generative Models & Multimodal Learning
  GANs, diffusion, multimodal use cases, synthetic data

• Practical Production ML
  Deployment, monitoring, A/B testing, interpretability

• Capstone Projects & Assessment
  Real-world business case applications

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Contributing

Contributions are welcome!

If you’d like to improve the course (fix typos, add new examples, improve explanations, or contribute new business case studies), please follow these steps:

1. Fork the repository

2. Create a feature branch

   git checkout -b feature-new-topic

3. Commit your changes

   git commit -m "Added new section on XYZ"

4. Push to your fork and open a Pull Request

I will review your contributions and merge them if aligned with the course objectives. Please ensure your submissions are clear, well-documented, and reproducible.

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Running Notebooks

You have three options to run notebooks:

1. In the Browser (No Installation Needed)

   • Click the "Launch in JupyterLite" badge in any notebook to run it instantly in your browser via JupyterLite.

2. On Google Colab

   • Click the "Open in Colab" badge at the top of each notebook to run it in Google Colab.

3. Locally

   • Install dependencies:

     pip install -r requirements.txt

   • Run the build and serve locally:

     chmod +x build_jupyterlite.sh
     ./build_jupyterlite.sh

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👥 Contributors

Thanks to all the amazing people who have contributed to this project 💖

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👩‍🏫 About the Book Maintainer

Dr. Chandravesh Chaudhari

📧 chandraveshchaudhari@gmail.com 🌐 Website 🔗 LinkedIn

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