🤖 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.

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🎯 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.

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📚 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

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🛠️ Technologies Used

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

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📓 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

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🚀 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

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💡 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.

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👨‍💻 Author

Cristian Cruz

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

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