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Data Science

A curated collection of projects, notebooks, and resources exploring practical applications of Data Science, Machine Learning, and Artificial Intelligence.

Table of Contents

Introduction

This repository compiles my personal Data Science projects and experiments, ranging from classic machine learning models to deep learning and artificial intelligence. Many projects are inspired by or adapted from leading online courses (Udacity, Coursera), as well as participation in Kaggle competitions and practical classroom exercises.

Here, you'll find:

  • End-to-end solutions for real-world problems.
  • Step-by-step tutorials and code for various algorithms.
  • Notebook-based explorations and visualizations.
  • Materials and presentations used in Data Science classes and workshops.

Repository Structure

Machine Learning

This folder contains diverse machine learning projects and examples. Topics include:

  • Ensemble methods (e.g., Bagging)
  • Deep Learning (e.g., neural networks)
  • Solutions to Kaggle challenges
  • Python scripts and Jupyter notebooks for hands-on learning

Artificial Intelligence

Covering broader AI concepts, this folder explores:

  • Genetic programming
  • Expert systems
  • Other advanced AI topics beyond standard machine learning

Exploratory Data Analysis

Find examples and projects demonstrating:

  • Data mining workflows
  • Data visualization techniques
  • Best practices for cleaning, exploring, and presenting data

References

  • Serrano, A. G. (2013). Inteligencia artificial: fundamentos, práctica y aplicaciones. Alfaomega.
  • Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.
  • Wickham, H., & Grolemund, G. (2016). R for Data Science.
  • Udacity, Deep Learning.
  • Deep Learning Specialization. Deep Learning.ai, Coursera.
  • Udacity, Introduction to Machine Learning
  • VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. " O'Reilly Media, Inc.".
  • Kabacoff, R. (2015). R IN ACTION: Data analysis and graphics with R

Note: All material in this repository is for educational and research purposes. Please use responsibly and cite references where appropriate.

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