📊 Data Science, ML & AI Learning Journey

Complete Masterclass: From Python Foundations to Production ML, Deep Learning & Multimodal RAG Systems

🏆 Milestone • 🌟 About • 📚 Curriculum • 🚀 Roadmap • 🎯 Projects • 💡 Skills • 🤝 Connect

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🏆 Completion Milestone

Important

🎯 OFFICIAL COURSE GRADUATION ANNOUNCEMENT

This comprehensive Data Science, Machine Learning & Artificial Intelligence curriculum has been 100% fully completed!

Metric	Details
🚀 Start Date	September 14, 2025
🎉 Completion Date	August 04, 2026
⏱️ Total Learning Duration	11 Months (~46 Weeks of Intensive Study)
📚 Total Modules Mastered	18 Out of 18 Modules (100%)
📓 Jupyter Notebooks	60+ Interactive Code Notebooks
💻 Code & Media Resources	200+ Scripts, SQL Guides, Data Files & Models
🏆 Graduation Status	GRADUATED — GOD LEVEL MASTERY ACHIEVED

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🌟 About This Course

Welcome to the complete record of my Data Science & AI Learning Journey!

This repository documents an intensive, hands-on journey from the absolute basics of Python programming to building production-grade Machine Learning pipelines, Deep Learning neural networks, web-deployed model APIs, and modern Multimodal RAG (Retrieval-Augmented Generation) AI systems.

💡 What Makes This Repository Special?

• 🎯 100% Practical & Project-Driven: Every concept is backed by working Python code, Jupyter notebooks, or real-world datasets.
• 🧱 End-to-End Skill Building: From simple if/else loops to full ColumnTransformer pipelines, Flask web applications, and vector embedding audio/video RAG pipelines.
• 📁 Structured & Clean Architecture: Organized into 18 logical modules covering Data Analytics, SQL, Statistics, Machine Learning, Deep Learning, Web Dev, and Generative AI.

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📚 Curriculum

📖 All 18 Completed Modules

No.	Module Name	Key Topics Covered	Content Summary	Status
01	🎓 Data Science Intro	Tools, Environment Setup, Data Science Lifecycle, Career Paths	1 PDF Guide	✅ COMPLETED
02	🐍 Python Refresher	Variables, Loops, Data Structures, Functions, OOP, Lambdas, File I/O, JSON	18 Notebooks + 2 Docs	✅ COMPLETED
03	🚀 Project: Coders of Delhi	Social Graph Theory, Recommendation Engine (People You May Know)	3 Notebooks + Datasets	✅ COMPLETED
04	🔢 NumPy Mastery	NDArrays, Indexing, Slicing, Broadcasting, Vectorization, Matrix Math	5 Notebooks	✅ COMPLETED
05	🐼 Pandas Deep Dive	DataFrames, Series, Data Cleaning, Merging, GroupBy, Aggregation	2 Notebooks + Datasets	✅ COMPLETED
06	📊 Data Visualization	Bar, Line, Scatter, Pie, Histograms, Boxplots, Heatmaps, Subplots	8 Notebooks + PDF	✅ COMPLETED
07	🕷️ Web Scraping	HTTP Protocol, BeautifulSoup4, Requests, DOM Traversal, Scraping Pipelines	2 Notebooks + 49 HTMLs	✅ COMPLETED
08	🗄️ SQL & Databases	CRUD Operations, Complex Joins, Subqueries, Views, Indexes, Stored Procedures	20 SQL Guides	✅ COMPLETED
09	📈 Probability & Stats	Conditional Probability, Bayes Theorem, Uniform, Binomial & Normal Dist, CLT	13 Guides & Scripts	✅ COMPLETED
10	🤖 ML Introduction	Machine Learning Fundamentals, ML History, How Machines Learn Concepts	PPT & Study Guides	✅ COMPLETED
11	🔧 Sklearn Basics	First ML Models, Estimators API, Decision Trees, Model Selection	3 Notebooks + PDF	✅ COMPLETED
12	📋 ML Algorithm Types	Supervised vs. Unsupervised, Classification, Regression, Clustering Overview	3 Concept Guides	✅ COMPLETED
13	🎯 Demo ML Practice	Iris Classification, Accuracy Metrics, Train-Test Split, RMSE & MAE Evaluation	6 Notebooks + Datasets	✅ COMPLETED
14	🛠️ Practical ML & Pipelines	EDA, Imputation, One-Hot Encoding, Feature Scaling, ColumnTransformer, Joblib	10 Notebooks + Scripts	✅ COMPLETED
15	🧠 Deep Learning & Neural Nets	Perceptron Formula, Neural Network Architecture, PyTorch vs. TensorFlow, MNIST	2 Notebooks + Scripts	✅ COMPLETED
16	🌐 Web Dev for Data Science	HTML5, CSS3, Flask Application Routing, Jinja Templates, Dynamic APIs	20 Web App Files	✅ COMPLETED
17	🤖 LLM & GenAI Intro	LLM Architecture, Transformers, Tokenization, Prompt Engineering, RAG Concepts	5 Guides & PDF	✅ COMPLETED
18	🎙️ Multimodal RAG AI Teaching	Audio/Video Ingestion, Media Preprocessing, Chunking, Vector Search & Embeddings	54 Code & Media Files	✅ COMPLETED

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🚀 Mastery Roadmap

Below is the visual progression of how this course unfolded over 11 months across 6 major learning phases:

graph TD
    classDef completed fill:#2ea44f,stroke:#22863a,color:#ffffff,font-weight:bold;
    
    A[Phase 1: Foundations<br/>Python, NumPy & Pandas<br/>Modules 01 - 05]:::completed --> B[Phase 2: Analytics & Scraping<br/>Viz, BeautifulSoup, SQL & Stats<br/>Modules 06 - 09]:::completed
    B --> C[Phase 3: Machine Learning Core<br/>Scikit-Learn & Algorithm Taxonomy<br/>Modules 10 - 13]:::completed
    C --> D[Phase 4: Production ML Pipelines<br/>EDA, ColumnTransformer & Joblib<br/>Module 14]:::completed
    D --> E[Phase 5: Deep Learning & Deployment<br/>Perceptrons, PyTorch, Flask & APIs<br/>Modules 15 & 16]:::completed
    E --> F[Phase 6: Generative AI & RAG<br/>Transformers & Multimodal RAG Systems<br/>Modules 17 & 18]:::completed

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🔍 Phase-by-Phase Deep Dive (Click to expand)

🌱 Phase 1: Foundations (Python & Data Basics)

• Mastered Python core syntax, control flow, functions, OOP, and lambda expressions.
• Learned fast numerical computations with NumPy NDArrays, vectorization, and matrix operations.
• Deep dived into Pandas DataFrames for data loading, indexing, cleaning, and aggregation.
• Built the Coders of Delhi social network graph recommendation engine.

🌿 Phase 2: Analytics, Web Harvesting & SQL

• Mastered data visualization using Matplotlib (bar, line, scatter, box plots) and Seaborn heatmaps.
• Built automated web scraping pipelines using requests and BeautifulSoup4 over 49+ web pages.
• Covered relational databases in MySQL: complex JOINs, foreign keys, window functions, and stored procedures.
• Studied mathematical foundations: Bayes Theorem, Binomial, Normal distributions, and Central Limit Theorem.

🌳 Phase 3: Machine Learning Core

• Understood the core philosophies of machine learning: Supervised vs. Unsupervised learning.
• Implemented Scikit-Learn estimators, Decision Trees, and model evaluation metrics (Accuracy, Confusion Matrix).
• Practiced end-to-end model training on the Iris dataset with train-test splits and error metrics (RMSE, MAE).

🛠️ Phase 4: Production ML Pipelines

• Implemented real-world Exploratory Data Analysis (EDA) on house prices and smartphone datasets.
• Created robust feature engineering pipelines using ColumnTransformer (SimpleImputer, OneHotEncoder, StandardScaler).
• Learned model serialization and offline inference using Joblib.

🧠 Phase 5: Deep Learning & Web Deployment

• Understood artificial neural network foundations, Perceptron formulas, and PyTorch vs. TensorFlow architectures.
• Built neural network classifiers for handwritten digit recognition on the MNIST dataset.
• Developed full-stack Flask web applications with Jinja2 template inheritance, custom HTML/CSS, dynamic forms, and REST APIs to deploy data science models.

🎙️ Phase 6: Generative AI & Multimodal RAG

• Explored Large Language Model (LLM) architectures, Transformers, and vector embeddings.
• Developed an end-to-end Multimodal RAG AI Teaching Assistant system that processes educational audio/video files, generates vector embeddings (embeddings.joblib), chunks text, and answers student queries contextually.

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🎯 Capstone Projects

🎙️ Multimodal RAG AI Teaching System Generative AI & Media Processing Pipeline An end-to-end RAG system that ingests video/audio educational content, converts media to audio (video_to_mp3.py), generates text chunks (merge_chunks.py), computes vector embeddings (embeddings.joblib), and responds to user queries (process_incoming.py). Tech Stack: Python, OpenAI / LLM Architecture, Vector Search, Joblib, Audio Processing Folder: 018 RAG based Al Teaching	🏠 Gurgaon House Price Predictor Production Machine Learning Pipeline A complete real estate pricing model featuring automated data preprocessing, missing value handling, categorical encoding, feature scaling, and model persistence. Tech Stack: Scikit-Learn, Pandas, ColumnTransformer, Joblib Folder: 014 Practical ML using Scikit-learn/004 Predicting Gurgaon City House Prices
🌐 Flask Data Science Web Application Full-Stack ML Deployment & REST API Web applications built with Flask, Jinja2 template inheritance, HTML5/CSS3 UI styling, and RESTful API endpoints for serving machine learning models over the web. Tech Stack: Flask, Jinja2, HTML5, CSS3, Python REST APIs Folder: 016 Web Development for Data Scientists	🌐 Coders of Delhi (Social Graph Engine) Graph Recommendation System Social network recommendation algorithms built from scratch to calculate "People You May Know" and "Pages You Might Like" based on user similarity matrices. Tech Stack: Python, JSON Data Structures, Graph Theory Folder: 003 Project 001 - Coders of Delhi
🧠 MNIST Digit Recognizer Neural Network & Perceptron Classifier Handwritten digit recognition trained using Perceptron formulas and deep neural networks, complete with pixel visualization routines. Tech Stack: PyTorch, TensorFlow/Keras, Scikit-Learn, Matplotlib Folder: 015 Deep Learning & Neural Networks	📚 Book Data Scraping Pipeline Web Scraping & Automated Data Extraction Harvested 49 HTML pages from an online library, parsed product titles, prices, and ratings with BeautifulSoup, and structured output into clean CSV files. Tech Stack: Requests, BeautifulSoup4, HTML Parsing, Pandas Folder: 007 Web Scrapping

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💡 Skills Unlocked

🐍 Programming & Data ✅ Python Syntax & OOP ✅ Lambdas & Map/Filter ✅ Data Cleaning & Imputation ✅ Pandas DataFrames & GroupBy ✅ NumPy Vectorization & Matrices ✅ JSON & File I/O Operations	🤖 Machine Learning & ML-Ops ✅ Supervised & Unsupervised ML ✅ Decision Trees & Classifiers ✅ Scikit-Learn ColumnTransformer ✅ One-Hot & Ordinal Encoding ✅ StandardScaler & MinMaxScaler ✅ Model Persistence with Joblib	🧠 Deep Learning & GenAI ✅ Perceptron & Neural Networks ✅ PyTorch vs. TensorFlow Basics ✅ Activations & Loss Functions ✅ LLM & Transformer Mechanism ✅ Retrieval-Augmented Generation ✅ Vector Embeddings & Ingestion
🗄️ SQL & Databases ✅ SQL SELECT, JOIN & WHERE ✅ Grouping & Aggregations ✅ Subqueries & Views ✅ Indexes & Query Optimization ✅ Foreign Key Constraints ✅ MySQL Stored Procedures	🌐 Web Dev & Deployment ✅ HTML5 & CSS3 Web Layouts ✅ Flask Web Server Setup ✅ Jinja2 Template Inheritance ✅ Dynamic Query Parameters ✅ REST API Endpoint Creation ✅ Deploying Models to Web UI	📊 Visualization & Scraping ✅ Matplotlib Custom Plots ✅ Seaborn Statistical Graphics ✅ BeautifulSoup4 HTML Parsing ✅ Automated Web Scraping ✅ Multimodal Media Extraction ✅ Data Pipeline Engineering

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🛠️ Technology Stack

Category	Technology / Framework / Library
💻 Programming Language	Python 3.11+
📊 Data Analysis & Science	NumPy, Pandas
📈 Data Visualization	Matplotlib, Seaborn
🕷️ Data Scraping & Web	BeautifulSoup4, Requests, HTML5, CSS3
🗄️ Database Management	MySQL, SQL
🤖 Machine Learning	Scikit-Learn, Joblib
🧠 Deep Learning	PyTorch, TensorFlow / Keras
🌐 Web Framework	Flask, Jinja2
🎙️ Generative AI & RAG	Transformers, OpenAI API, Vector Embeddings
📓 IDE & Workspace	Jupyter Notebook, VS Code, Git

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📈 Progress Tracker

Core Curriculum Modules (100% Completed!)

• 🎓 Module 01: Data Science Intro
• 🐍 Module 02: Python Refresher (18 Notebooks)
• 🚀 Module 03: Social Network Project (Coders of Delhi)
• 🔢 Module 04: NumPy Mastery (5 Notebooks)
• 🐼 Module 05: Pandas Deep Dive (2 Notebooks)
• 📊 Module 06: Data Visualization (8 Notebooks)
• 🕷️ Module 07: Web Scraping & BeautifulSoup
• 🗄️ Module 08: SQL & Databases (20 Tutorials)
• 📈 Module 09: Probability & Statistics
• 🤖 Module 10: Machine Learning Fundamentals
• 🔧 Module 11: Scikit-Learn Basics
• 📋 Module 12: Types of ML Algorithms
• 🎯 Module 13: Scikit-Learn Practice & Metrics
• 🛠️ Module 14: Practical ML Pipelines & Feature Engineering
• 🧠 Module 15: Deep Learning & Neural Networks
• 🌐 Module 16: Web Development for Data Scientists (Flask)
• 🤖 Module 17: LLM & Generative AI Intro
• 🎙️ Module 18: RAG-based AI Teaching & Multimodal Pipelines

Capstone Projects (All Finished!)

• 🎙️ Multimodal RAG AI Teaching System
• 🏠 Gurgaon House Price Predictor Pipeline
• 🌐 Flask Web Application & REST API Deployment
• 🌐 Coders of Delhi Social Graph Engine
• 🧠 MNIST Neural Network Digit Recognizer
• 📚 49-Page Web Scraping Data Pipeline

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🤝 Connect & Socials

Let's Connect & Collaborate!

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🏆 COURSE COMPLETED FULLY (Sep 14, 2025 – Aug 04, 2026)

Made with ❤️ and relentless dedication for Data Science & AI Mastery.