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Translate README to English and enhance content
Updated README to reflect English translations and improved clarity on course content and objectives.
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# 🤖 Fundamentos de Inteligencia Artificial y Machine Learning
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# 🤖 Artificial Intelligence & Machine Learning Fundamentals
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## 📖 Descripción
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## 📖 Overview
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Este repositorio documenta mi proceso de aprendizaje en **Inteligencia Artificial y Machine Learning**, incluyendo notebooks, ejercicios prácticos y experimentos desarrollados durante el curso **Fundamentos de IA y Machine Learning**.
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This repository documents my learning journey through the **Artificial Intelligence and Machine Learning Fundamentals** course, including notebooks, practical exercises, and experiments developed throughout the program.
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A lo largo del curso exploré los principales paradigmas del aprendizaje automático, desde modelos supervisados y no supervisados hasta técnicas modernas de visión computacional, procesamiento de lenguaje natural y aprendizaje por refuerzo.
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The course provided a comprehensive introduction to modern AI concepts, covering supervised and unsupervised learning, computer vision, natural language processing, and reinforcement learning.
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## 🎯 Objetivos del Curso
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## 🎯 Learning Objectives
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* Comprender los fundamentos de la Inteligencia Artificial.
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* Understand the core concepts of Artificial Intelligence and Machine Learning.
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* Construir modelos de clasificación y regresión.
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* Build and evaluate classification and regression models.
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* Evaluar y validar modelos de Machine Learning.
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* Apply model validation techniques and performance metrics.
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* Aplicar técnicas de optimización y ajuste de hiperparámetros.
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* Explore hyperparameter tuning and feature selection.
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* Implementar algoritmos de clustering y reducción de dimensionalidad.
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* Implement clustering and dimensionality reduction techniques.
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* Introducir conceptos de Deep Learning.
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* Gain hands-on experience with Deep Learning concepts.
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* Explorar aplicaciones de Computer Vision y NLP.
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* Explore Computer Vision and Natural Language Processing applications.
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* Comprender los principios básicos del Reinforcement Learning.
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* Understand the fundamentals of Reinforcement Learning.
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---
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## 📚 Temas Estudiados
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## 📚 Topics Covered
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### 🔹 Aprendizaje Supervisado
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### 🔹 Supervised Learning
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* Clasificación
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* Classification
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* Regresión
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* Regression
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* Métricas de validación
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* Model evaluation and validation metrics
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* Dataset Iris
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* Iris Dataset classification
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* Selección de características
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* Feature selection techniques
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* Ajuste de hiperparámetros
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* Hyperparameter tuning
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* Ensamble de modelos
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* Ensemble methods
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### 🔹 Aprendizaje No Supervisado
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### 🔹 Unsupervised Learning
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* Clustering
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* Clustering algorithms
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* Agrupamiento jerárquico
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* Hierarchical clustering
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* Segmentación de clientes
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* Customer segmentation
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* Reducción de dimensionalidad con PCA
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* Principal Component Analysis (PCA)
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### 🔹 Visión Computacional
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### 🔹 Computer Vision
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* Representación y procesamiento de imágenes
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* Image representation and processing
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* Redes Neuronales Convolucionales (CNN)
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* Convolutional Neural Networks (CNNs)
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* Clasificación de imágenes
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* Image classification
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* Identificación de objetos con MobileNetV2
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* Object recognition with MobileNetV2
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### 🔹 Procesamiento de Lenguaje Natural (NLP)
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### 🔹 Natural Language Processing (NLP)
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* Fundamentos de NLP
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* NLP fundamentals
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* Transformers
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* Transformer architectures
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* Aplicaciones prácticas del procesamiento de texto
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* Text processing applications
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### 🔹 Aprendizaje por Refuerzo
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### 🔹 Reinforcement Learning
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* Exploración vs Explotación
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* Exploration vs. Exploitation
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* Estrategias de aprendizaje
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* Learning strategies
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* Casos de uso empresariales
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* Business applications
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* Desafíos del Machine Learning moderno
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* Challenges in modern Machine Learning
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## 🛠️ Tecnologías Utilizadas
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## 🛠️ Technologies Used
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* Python
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* Python
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* Google Colab
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* Google Colab
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## 📓 Notebook Principal
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## 📓 Main Notebook
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El desarrollo completo del curso se encuentra documentado en el notebook principal:
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The complete course development and practical activities can be found in the main notebook:
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➡️ **[Abrir Notebook](./Curso_Fundamentos_IA_Machine_Learning.ipynb)**
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➡️ **[Open Notebook](./Curso_Fundamentos_IA_Machine_Learning.ipynb)**
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GitHub permite visualizar directamente el notebook con:
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GitHub allows direct visualization of:
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* Código fuente
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* Source code
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* Explicaciones teóricas
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* Explanatory notes
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* Resultados
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* Outputs and results
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* Tablas
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* Tables and visualizations
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* Gráficas
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* Experiment findings
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## 🚀 Competencias Desarrolladas
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## 🚀 Skills Developed
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✔️ Preparación y análisis de datos
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✔️ Data preparation and analysis
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✔️ Construcción de modelos predictivos
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✔️ Machine Learning model development
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✔️ Evaluación y optimización de modelos
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✔️ Model evaluation and optimization
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✔️ Computer Vision
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✔️ Computer Vision fundamentals
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✔️ Procesamiento de Lenguaje Natural
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✔️ Natural Language Processing basics
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✔️ Reinforcement Learning
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✔️ Reinforcement Learning concepts
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✔️ Desarrollo de proyectos utilizando Google Colab
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✔️ AI experimentation using Google Colab
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## 💡 Principales Aprendizajes
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## 💡 Key Takeaways
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Durante este curso adquirí una visión general del ecosistema de Inteligencia Artificial y Machine Learning, comprendiendo cuándo utilizar técnicas supervisadas, no supervisadas y de aprendizaje profundo para resolver diferentes tipos de problemas.
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Through this course, I gained a solid understanding of the Machine Learning workflow, including data analysis, model training, evaluation, and optimization. I also explored modern AI domains such as Computer Vision, Natural Language Processing, and Reinforcement Learning through practical examples and hands-on experimentation.
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Además, desarrollé experiencia práctica utilizando herramientas ampliamente empleadas en la industria para análisis de datos, entrenamiento de modelos y experimentación en entornos de ciencia de datos.
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This repository serves as a record of my progress and continuous learning in the fields of Artificial Intelligence and Data Science.
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## 👨💻 Autor
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## 👨💻 Author
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**Cristian Cruz**
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**Cristian Cruz**
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Estudiante de tecnología y entusiasta del desarrollo de software, la Inteligencia Artificial y el Machine Learning.
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Technology student passionate about Software Development, Artificial Intelligence, Machine Learning, and emerging technologies.
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⭐ Si este repositorio te resulta útil o interesante, considera darle una estrella.
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⭐ If you found this repository useful, consider giving it a star.
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