🤖 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

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.

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