Python Foundations to Libraries – Practice Notes and Projects 🐍📚

Welcome to my curated collection of Python practice notebooks! This repository showcases my hands-on learning journey in Python, covering both foundational concepts and essential libraries used in Data Science and Machine Learning. Each folder contains structured practice notebooks and mini-projects with explanations, examples, and applied use cases.

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🧠 Purpose

These are my practice codes built to understand and solidify core concepts before moving into advanced machine learning and AI projects. I’ve included real-world examples, coding exercises, and visualizations wherever possible.

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📁 Folder Structure

✅ PYTHON

• Core Python concepts and syntax
• Data types, loops, conditionals, functions, and OOP basics
• File handling and error handling exercises

📁 NumPy

• Numerical operations using NumPy arrays
• Vectorization, broadcasting, indexing, and slicing
• Hands-on practice with mathematical functions and statistics

📁 Pandas

• Data manipulation using DataFrames and Series
• Data cleaning, aggregation, groupby, and time series
• Exploratory Data Analysis using Pandas

📁 Exploratory Data Analysis (EDA)

• EDA frameworks on sample datasets (like Titanic, Iris, etc.)
• Univariate, bivariate, and multivariate analysis
• Feature inspection and correlation analysis

📁 Matplotlib

• Data visualization using Matplotlib
• Line plots, bar charts, histograms, scatter plots, subplots
• Customizations, color maps, and labels for storytelling

📁 PyTorch Deep Learning Projects & Practice

📄 Notebook	📝 Description
ANN	Basic Artificial Neural Network built using nn.Sequential
ANN_OptunaTuning	Tuning ANN hyperparameters with Optuna for better performance
AutoGrad	Demonstration of PyTorch's autograd module for automatic differentiation
CNN	Implementation of Convolutional Neural Networks for image classification
CNN_OptunaTuning	Hyperparameter tuning for CNNs using Optuna
Dataset&DataLoader	Custom dataset class and usage of PyTorch’s DataLoader for batch processing
Next_Word_Predictor	Simple NLP model to predict the next word in a sequence using RNNs
NN_Module	Neural Network defined using a custom class with nn.Module and forward pass
Optuna_Basics	Learning the basics of Optuna for model and parameter optimization
Question_Answering_System	A basic Question-Answering system using NLP embedding techniques
Rice_Classification	Image classification project for different rice varieties
Training_Pipeline	End-to-end training loop with metrics logging and model saving
Transfer_Learning	Applying pretrained CNN models (like ResNet) to a custom dataset

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🚀 Skills Practiced

• PyTorch fundamentals (autograd, nn.Module, optim, DataLoader)
• Convolutional Neural Networks (CNNs)
• Natural Language Processing with RNNs
• Transfer learning with pretrained models
• Hyperparameter optimization using Optuna
• Modular model training and evaluation pipelines
• Real-world classification projects

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