Machine Learning

This repository contains assignments, projects, experiments, and practical activities developed during my Machine Learning course at university.

About

The purpose of this repository is to document my learning journey in Machine Learning, including the implementation of algorithms, data analysis techniques, model training, and performance evaluation. Each activity explores different concepts and methodologies commonly used in the fields of Artificial Intelligence and Data Science.

Topics Covered

Throughout the course, the following topics may be explored:

• Data Preprocessing and Normalization
• Exploratory Data Analysis (EDA)
• Supervised Learning
• Unsupervised Learning
• Linear Regression
• Classification Algorithms
• Artificial Neural Networks
• Perceptron and ADALINE Models
• Model Evaluation and Validation
• Feature Engineering
• Pattern Recognition
• Machine Learning Applications

Repository Structure

Machine-Learning/
│
├── Activity_06/
  └── server.py
  └── index.html
├── README.md

Each directory contains the code, datasets, reports, and supporting materials related to a specific activity or project.

Technologies and Tools

The projects and assignments may use:

• Python
• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• Jupyter Notebook
• Google Colab

Learning Objectives

The main goals of this repository are:

• Apply machine learning concepts in practical scenarios.
• Develop skills in data analysis and model development.
• Understand the strengths and limitations of different algorithms.
• Build a portfolio showcasing academic and technical progress in Machine Learning.

Disclaimer

This repository is intended for educational purposes and contains coursework developed as part of a university Machine Learning class.

Author

Vitor Henrique

Computer Engineering Student
Robotics Researcher and Enthusiast
Machine Learning Learner