Scripted Insights: Handwritten DS & ML Notes

Welcome to Scripted Insights, a collection of meticulously crafted notes on Data Science and Machine Learning, born from two immersive years of university exploration. These handwritten annotations distill complex concepts into an accessible format, guiding both enthusiastic beginners and seasoned practitioners.

From foundational algorithms to advanced statistical methods, from neural networks to ensemble techniques, Scripted Insights offers insights that pen and paper uniquely convey. This repository, a labor of love, now extends its benevolence to those eager to embrace the art of data driven innovation a tactile and personal journey into the realms of Data Science and Machine Learning.

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Contents

• Machine Learning: Discover a range of algorithms, techniques, and methodologies that form the foundation of machine learning. Delve into supervised, and unsupervised learning and more.

• Deep Learning Algorithms: Unveil the mysteries of neural networks, convolutional networks, and recurrent networks. Harness the power of deep learning to comprehend complex patterns.

• Data Science Concepts: Grasp the essence of data preprocessing, feature engineering, and exploratory data analysis. Lay the groundwork for extracting insights from raw data.

• Mathematics: Navigate through the realm of statistics and numeric integration, crucial to the data-driven decision-making process.

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Structure of this Repository - Note Batches

Discover curated note batches, each brimming with insightful knowledge. Every batch comprises two components: a PDF of handwritten notes and an accompanying README detailing covered topics.

- Batch X
   - Batch X.pdf
   - README.md

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Additional Resources

Blogs

Explore handpicked blog resources that provide in-depth insights into key concepts. These Blogs helped me in my journey. Dive into their valuable content

• Medium

  • Example Blog 1
  • Example Blog 2

• Analytics Vidhya

  • Example Blog 1
  • Example Blog 2

YouTube Channels

Enhance your learning experience through recommended YouTube channels. The Channel helped me upskill myself. They are the biggest contributors. Engage with dynamic visual explanations.

Explore their channel and Playlists as much as you can.

• Corey Schafer

  • Python
  • Pandas

• Krish Naik

  • ML Playlist
  • DL Playlist

• StatQuest with Josh Starmer

  • Linear Regression
  • Maths - Stats

• Code basics

Retrieval-Augmented Generation (RAG) and Fine-Tuning

1. What is RAG ? (Retrieval Augmented Generation)
2. What is Retrieval-Augmented Generation (RAG)?
3. Building a RAG application from scratch using Python, LangChain, and the OpenAI API
4. Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer
5. Building Production-Ready RAG Applications: Jerry Liu
6. RAG From Scratch

• RAG vs. Fine Tuning

• LoRA & QLoRA Fine-tuning Explained In-Depth

• Fine-Tuning-LLMs

• The scientist who coined retrieval augmented generation

• How to best integrate multimodal models into RAG systems for the industrial domain.

• Building GenAI Enterprise Applications with Vectara and Datavolo

• The LLM Triangle Principles to Architect Reliable AI Apps

• How to Think About Using Your Company’s Information with GenAI

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Contribution

Your engagement is valued. If you spot areas for improvement, feel free to contribute. Your additions can further enrich this knowledge hub. If you have some notes or resources feel free to add those to this repo

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Start Your Journey

Embark on your journey into the captivating domains of Data Science and Machine Learning. Unravel insights, embrace learning, and chart your path to innovation. Welcome to Scripted Insights.

Connect

Reach out for inquiries and discussions:

• Email: marbkable@gmail.com
• LinkedIn: Gaurav Jain
• gumroad: MarbKable

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Scripted Insights - Empowering Learning, One Stroke at a Time.