IA-Machine-Learning

IA-Machine-Learning is a hands-on machine learning study repository containing practical notebooks and exercises focused on data analysis, regression models, and real-world datasets.

The project documents the learning journey through progressively structured notebooks.

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Overview

This repository includes step-by-step machine learning exercises covering:

• Introduction to Machine Learning concepts
• Data exploration and visualization
• Linear Regression
• Multiple Linear Regression
• Feature scaling (StandardScaler)
• Working with real datasets (e.g., movies, taxi data)
• Practical ML exercises

Each notebook builds on previous concepts to reinforce understanding.

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Notebooks Included

• 00. Studying ML – Introduction and foundational concepts
• 01. Hollywood Movies – Dataset analysis and visualization
• 02. New York Taxis – Real-world dataset exploration
• 03. Linear Regression – Simple linear regression implementation
• 04. Multiple Linear Regression – Multivariate regression and scaling
• 05. Machine Learning Exercise – Applied regression practice

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Requirements

• Python 3.x
• Jupyter Notebook or JupyterLab
• Common ML libraries:
  • NumPy
  • Pandas
  • Matplotlib / Seaborn
  • Scikit-learn

Install dependencies if needed:

pip install numpy pandas matplotlib seaborn scikit-learn

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Usage

Start Jupyter:

jupyter notebook

Open the notebooks in order (00 → 05) to follow the learning progression.

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Learning Goals

This repository is designed to:

• Build intuition around regression models
• Practice data preprocessing and scaling
• Work with real datasets
• Strengthen practical ML skills through hands-on exercises

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Status

Work in progress. Additional notebooks and improvements may be added over time.

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License

Specify your license here (e.g., MIT).