Machine-Learning-Introduction

Some basic machine learning topics for learning purposes

Overview

This repository contains a collection of Jupyter notebooks designed to introduce fundamental machine learning concepts and techniques. The materials cover a range of topics from basic regression to clustering and deep learning approaches.

Contents

Notebooks

• Introduction_and_Regression.ipynb: Covers basic ML concepts and regression techniques
• Clustering_and_Probabilities.ipynb: Explores clustering algorithms and probability-based methods
• Comparison_of_DBSCAN_and_K_means.ipynb: Detailed comparison between two popular clustering algorithms
• Non_parametric_methods_and_deep_learning.ipynb: Introduction to non-parametric approaches and neural networks

Datasets

The data/ directory contains several datasets used in the notebooks:

• air_temp.csv: Temperature data for time series analysis
• chooseK.csv: Dataset for K-means cluster analysis
• FMIData.csv: Finnish Meteorological Institute data
• mall_customers.csv: Customer segmentation dataset
• Mushroom data.csv: Mushroom classification dataset
• youtube_comments.csv: Text data for NLP demonstrations

Prerequisites

• Python 3.x
• Jupyter Notebook
• Required libraries:
  • NumPy
  • Pandas
  • Matplotlib
  • Scikit-learn
  • TensorFlow/Keras (for deep learning notebook)

Getting Started

1. Clone this repository
2. Install required dependencies
3. Launch Jupyter Notebook
4. Open any notebook to begin learning

Learning Path

For beginners, it's recommended to follow the notebooks in this order:

1. Introduction_and_Regression.ipynb
2. Clustering_and_Probabilities.ipynb
3. Comparison_of_DBSCAN_and_K_means.ipynb
4. Non_parametric_methods_and_deep_learning.ipynb