mirror of
https://github.com/CristianCruz0309/Curso_Fundamentos_IA_Machine_Learning.git
synced 2026-08-15 03:00:16 +00:00
Cris
b36a110d8e
Updated README to reflect English translations and improved clarity on course content and objectives.
135 lines
3.6 KiB
Markdown
135 lines
3.6 KiB
Markdown
# 🤖 Artificial Intelligence & Machine Learning Fundamentals
|
|
|
|
|
|
|
|
|
|
|
|
|
|
## 📖 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.
|
|
|