# 🤖 Artificial Intelligence & Machine Learning Fundamentals

![Python](https://img.shields.io/badge/Python-3.x-blue)
![Machine Learning](https://img.shields.io/badge/Machine-Learning-success)
![Google Colab](https://img.shields.io/badge/Google-Colab-orange)
![Status](https://img.shields.io/badge/Status-Completed-brightgreen)

## 📖 Overview

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.

The course provided a comprehensive introduction to modern AI concepts, covering supervised and unsupervised learning, computer vision, natural language processing, and reinforcement learning.

---

## 🎯 Learning Objectives

* 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.

---

## 📚 Topics Covered

### 🔹 Supervised Learning

* Classification
* Regression
* Model evaluation and validation metrics
* Iris Dataset classification
* Feature selection techniques
* Hyperparameter tuning
* Ensemble methods

### 🔹 Unsupervised Learning

* Clustering algorithms
* Hierarchical clustering
* Customer segmentation
* Principal Component Analysis (PCA)

### 🔹 Computer Vision

* Image representation and processing
* Convolutional Neural Networks (CNNs)
* Image classification
* Object recognition with MobileNetV2

### 🔹 Natural Language Processing (NLP)

* NLP fundamentals
* Transformer architectures
* Text processing applications

### 🔹 Reinforcement Learning

* Exploration vs. Exploitation
* Learning strategies
* Business applications
* Challenges in modern Machine Learning

---

## 🛠️ Technologies Used

* Python
* Google Colab
* NumPy
* Pandas
* Matplotlib
* Scikit-Learn
* TensorFlow
* Keras

---

## 📓 Main Notebook

The complete course development and practical activities can be found in the main notebook:

➡️ **[Open Notebook](./Curso_Fundamentos_IA_Machine_Learning.ipynb)**

GitHub allows direct visualization of:

* Source code
* Explanatory notes
* Outputs and results
* Tables and visualizations
* Experiment findings

---

## 🚀 Skills Developed

✔️ Data preparation and analysis

✔️ Machine Learning model development

✔️ Model evaluation and optimization

✔️ Computer Vision fundamentals

✔️ Natural Language Processing basics

✔️ Reinforcement Learning concepts

✔️ AI experimentation using Google Colab

---

## 💡 Key Takeaways

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.

This repository serves as a record of my progress and continuous learning in the fields of Artificial Intelligence and Data Science.

---

## 👨‍💻 Author

**Cristian Cruz**

Technology student passionate about Software Development, Artificial Intelligence, Machine Learning, and emerging technologies.

---

⭐ If you found this repository useful, consider giving it a star.

