🤖 AIML — Machine Learning

This repository contains my Machine Learning learning journey and Jupyter Notebook practice.

I have organized my notebooks based on different concepts and libraries used in Data Science and Machine Learning.

📚 Topics Covered

🐍 NumPy

• Arrays
• Indexing and slicing
• Array operations
• Reshaping
• Broadcasting

🐼 Pandas

• Series and DataFrames
• Data loading and exploration
• Data cleaning
• Missing value handling
• Duplicate handling
• Filtering and sorting
• GroupBy operations
• Data manipulation

📊 Matplotlib

• Line plots
• Bar charts
• Scatter plots
• Histograms
• Data visualization

🎨 Seaborn

• Count plots
• Box plots
• Distribution plots
• Heatmaps
• Categorical visualization

📈 Statistics

• Mean, Median and Mode
• Variance
• Standard Deviation
• Correlation
• Basic statistical concepts

🤖 Scikit-learn

• Data preprocessing
• Train-Test Split
• Feature Scaling
• Label Encoding
• One-Hot Encoding
• Regression
• Classification
• Model Training
• Model Evaluation

📂 Repository Structure

AIML---ML/
│
├── NumPy/
├── Pandas/
├── Matplotlib/
├── Seaborn/
├── Statistics/
├── Scikit-Learn/
|---ML
└── README.md

🛠️ Libraries & Tools

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook
• ML

🎯 Purpose

This repository is maintained to practice, organize, and document Machine Learning concepts through Jupyter Notebooks.

It will be continuously updated as new concepts, algorithms, and techniques are learned.

📌 Learning Path

Python
   ↓
NumPy
   ↓
Pandas
   ↓
Data Visualization
   ↓
Statistics
   ↓
Data Preprocessing
   ↓
Scikit-learn
   ↓
Machine Learning

📚 This repository represents my ongoing Machine Learning practice and learning process.