mirror of
https://github.com/CristianCruz0309/Curso_Fundamentos_IA_Machine_Learning.git
synced 2026-08-15 03:00:16 +00:00
Translate README to English and enhance content
Updated README to reflect English translations and improved clarity on course content and objectives.
This commit is contained in:
Cris
@@ -1,73 +1,73 @@
|
||||
# 🤖 Fundamentos de Inteligencia Artificial y Machine Learning
|
||||
# 🤖 Artificial Intelligence & Machine Learning Fundamentals
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## 📖 Descripción
|
||||
## 📖 Overview
|
||||
|
||||
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**.
|
||||
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.
|
||||
|
||||
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.
|
||||
The course provided a comprehensive introduction to modern AI concepts, covering supervised and unsupervised learning, computer vision, natural language processing, and reinforcement learning.
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Objetivos del Curso
|
||||
## 🎯 Learning Objectives
|
||||
|
||||
* Comprender los fundamentos de la Inteligencia Artificial.
|
||||
* Construir modelos de clasificación y regresión.
|
||||
* Evaluar y validar modelos de Machine Learning.
|
||||
* Aplicar técnicas de optimización y ajuste de hiperparámetros.
|
||||
* Implementar algoritmos de clustering y reducción de dimensionalidad.
|
||||
* Introducir conceptos de Deep Learning.
|
||||
* Explorar aplicaciones de Computer Vision y NLP.
|
||||
* Comprender los principios básicos del Reinforcement Learning.
|
||||
* Understand the core concepts of Artificial Intelligence and Machine Learning.
|
||||
* Build and evaluate classification and regression models.
|
||||
* Apply model validation techniques and performance metrics.
|
||||
* Explore hyperparameter tuning and feature selection.
|
||||
* Implement clustering and dimensionality reduction techniques.
|
||||
* Gain hands-on experience with Deep Learning concepts.
|
||||
* Explore Computer Vision and Natural Language Processing applications.
|
||||
* Understand the fundamentals of Reinforcement Learning.
|
||||
|
||||
---
|
||||
|
||||
## 📚 Temas Estudiados
|
||||
## 📚 Topics Covered
|
||||
|
||||
### 🔹 Aprendizaje Supervisado
|
||||
### 🔹 Supervised Learning
|
||||
|
||||
* Clasificación
|
||||
* Regresión
|
||||
* Métricas de validación
|
||||
* Dataset Iris
|
||||
* Selección de características
|
||||
* Ajuste de hiperparámetros
|
||||
* Ensamble de modelos
|
||||
* Classification
|
||||
* Regression
|
||||
* Model evaluation and validation metrics
|
||||
* Iris Dataset classification
|
||||
* Feature selection techniques
|
||||
* Hyperparameter tuning
|
||||
* Ensemble methods
|
||||
|
||||
### 🔹 Aprendizaje No Supervisado
|
||||
### 🔹 Unsupervised Learning
|
||||
|
||||
* Clustering
|
||||
* Agrupamiento jerárquico
|
||||
* Segmentación de clientes
|
||||
* Reducción de dimensionalidad con PCA
|
||||
* Clustering algorithms
|
||||
* Hierarchical clustering
|
||||
* Customer segmentation
|
||||
* Principal Component Analysis (PCA)
|
||||
|
||||
### 🔹 Visión Computacional
|
||||
### 🔹 Computer Vision
|
||||
|
||||
* Representación y procesamiento de imágenes
|
||||
* Redes Neuronales Convolucionales (CNN)
|
||||
* Clasificación de imágenes
|
||||
* Identificación de objetos con MobileNetV2
|
||||
* Image representation and processing
|
||||
* Convolutional Neural Networks (CNNs)
|
||||
* Image classification
|
||||
* Object recognition with MobileNetV2
|
||||
|
||||
### 🔹 Procesamiento de Lenguaje Natural (NLP)
|
||||
### 🔹 Natural Language Processing (NLP)
|
||||
|
||||
* Fundamentos de NLP
|
||||
* Transformers
|
||||
* Aplicaciones prácticas del procesamiento de texto
|
||||
* NLP fundamentals
|
||||
* Transformer architectures
|
||||
* Text processing applications
|
||||
|
||||
### 🔹 Aprendizaje por Refuerzo
|
||||
### 🔹 Reinforcement Learning
|
||||
|
||||
* Exploración vs Explotación
|
||||
* Estrategias de aprendizaje
|
||||
* Casos de uso empresariales
|
||||
* Desafíos del Machine Learning moderno
|
||||
* Exploration vs. Exploitation
|
||||
* Learning strategies
|
||||
* Business applications
|
||||
* Challenges in modern Machine Learning
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Tecnologías Utilizadas
|
||||
## 🛠️ Technologies Used
|
||||
|
||||
* Python
|
||||
* Google Colab
|
||||
@@ -80,54 +80,55 @@ A lo largo del curso exploré los principales paradigmas del aprendizaje automá
|
||||
|
||||
---
|
||||
|
||||
## 📓 Notebook Principal
|
||||
## 📓 Main Notebook
|
||||
|
||||
El desarrollo completo del curso se encuentra documentado en el notebook principal:
|
||||
The complete course development and practical activities can be found in the main notebook:
|
||||
|
||||
➡️ **[Abrir Notebook](./Curso_Fundamentos_IA_Machine_Learning.ipynb)**
|
||||
➡️ **[Open Notebook](./Curso_Fundamentos_IA_Machine_Learning.ipynb)**
|
||||
|
||||
GitHub permite visualizar directamente el notebook con:
|
||||
GitHub allows direct visualization of:
|
||||
|
||||
* Código fuente
|
||||
* Explicaciones teóricas
|
||||
* Resultados
|
||||
* Tablas
|
||||
* Gráficas
|
||||
* Source code
|
||||
* Explanatory notes
|
||||
* Outputs and results
|
||||
* Tables and visualizations
|
||||
* Experiment findings
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Competencias Desarrolladas
|
||||
## 🚀 Skills Developed
|
||||
|
||||
✔️ Preparación y análisis de datos
|
||||
✔️ Data preparation and analysis
|
||||
|
||||
✔️ Construcción de modelos predictivos
|
||||
✔️ Machine Learning model development
|
||||
|
||||
✔️ Evaluación y optimización de modelos
|
||||
✔️ Model evaluation and optimization
|
||||
|
||||
✔️ Computer Vision
|
||||
✔️ Computer Vision fundamentals
|
||||
|
||||
✔️ Procesamiento de Lenguaje Natural
|
||||
✔️ Natural Language Processing basics
|
||||
|
||||
✔️ Reinforcement Learning
|
||||
✔️ Reinforcement Learning concepts
|
||||
|
||||
✔️ Desarrollo de proyectos utilizando Google Colab
|
||||
✔️ AI experimentation using Google Colab
|
||||
|
||||
---
|
||||
|
||||
## 💡 Principales Aprendizajes
|
||||
## 💡 Key Takeaways
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
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.
|
||||
This repository serves as a record of my progress and continuous learning in the fields of Artificial Intelligence and Data Science.
|
||||
|
||||
---
|
||||
|
||||
## 👨💻 Autor
|
||||
## 👨💻 Author
|
||||
|
||||
**Cristian Cruz**
|
||||
|
||||
Estudiante de tecnología y entusiasta del desarrollo de software, la Inteligencia Artificial y el Machine Learning.
|
||||
Technology student passionate about Software Development, Artificial Intelligence, Machine Learning, and emerging technologies.
|
||||
|
||||
---
|
||||
|
||||
⭐ Si este repositorio te resulta útil o interesante, considera darle una estrella.
|
||||
⭐ If you found this repository useful, consider giving it a star.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user