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A structured AIML learning repository covering Python, NumPy, Pandas, Matplotlib, Seaborn, and EDA. Includes notebooks, scripts, datasets, and mini-projects — forming the core foundation for future ML, DL, and AI agent development.

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Python-ML — My AIML Learning Journey

A structured and practical repository documenting my complete journey into Python, Data Handling, Data Visualization, EDA, and foundational Machine Learning. This repo contains notebooks, scripts, datasets, and mini-projects — all created as part of my preparation for Artificial Intelligence & Machine Learning specialization.

📌 What This Repo Contains

This repository is organized into clean folders, each focusing on a key AIML skill:

📁 Warm-Up/

Basic Python practice — loops, functions, lists, conditionals, OOP, etc. Perfect for brushing up fundamentals.

📁 Numpy/

Covers:

Array creation

Vectorized operations

Slicing

Broadcasting

Mathematical operations

Time comparison between lists & NumPy

📁 Pandas/

Covers:

DataFrames & Series

Merging, joining, concatenation

Handling missing values

GroupBy operations

CSV loading/saving

Real-world dataset cleaning

Datasets included for practice.

📁 matplotlib/

Covers:

Line plots

Bar charts

Scatter plots

Histograms

Subplots

Styling & colors

Real-world visualizations

📁 Projects/

A collection of beginner-friendly EDA and visualization projects. Includes:

IPL Analysis

Zomato Analysis

Custom datasets exploration More projects will be added as I progress.

🧰 Tech Stack & Libraries Used

Python 3

NumPy

Pandas

Matplotlib

Seaborn

(Upcoming) Scikit-learn

(Upcoming) TensorFlow / PyTorch

🎯 Purpose of This Repository

This repo represents:

✔ My fundamentals in Data Science & ML ✔ My hands-on practice in Python ✔ My journey into AIML specialization ✔ My transition from basics → ML → DL ✔ My readiness for future AI/ML projects

It also serves as a strong foundation for:

Machine Learning algorithms

Deep Learning

NLP

AI-based apps

LLM projects

Full-stack ML deployments

📅 Roadmap (Upcoming Additions) 🔹 ML Algorithms

Linear & Logistic Regression

SVM

KNN

Decision Tree

Random Forest

Naive Bayes

🔹 EDA Projects

Netflix dataset

Heart Disease

Students Performance

🔹 ML Deployments

FastAPI ML APIs

Predictive web apps

🔹 Deep Learning

Neural Networks

CNNs

RNN/LSTM

Transformers

This repo will evolve into a complete AIML portfolio.

🌟 Why This Repo Exists

I am pursuing AI/ML specialization, and this repository documents everything I learn and build — from Python basics to advanced ML and AI systems. It represents my discipline, curiosity, and growth as an aspiring AI Engineer.

🤝 Contributions

This is a personal learning repo, but suggestions and improvements are always welcome!

⭐ Show Your Support

If you found this structure useful or inspiring, consider giving the repo a ⭐ on GitHub!

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A structured AIML learning repository covering Python, NumPy, Pandas, Matplotlib, Seaborn, and EDA. Includes notebooks, scripts, datasets, and mini-projects — forming the core foundation for future ML, DL, and AI agent development.

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