Machine Learning Courses Repository 📚

This repository serves as a collection of folders for self-studied courses I'm pursuing independently. Each folder represents a specific course, containing relevant materials, projects, and notebooks. 📑🔍

Folder Structure

• machineLearning_classificacaoSKLearn - Machine Learning: Classificação com SKLearn

  • Description: This Machine Learning course provides a comprehensive overview of the field, enabling practical application in a corporate setting. Topics include practicing with various examples, analyzing classification algorithms prevalent in everyday scenarios, interpreting results from a data scientist's perspective, and comparing linear and non-linear algorithms.
  • Materials: The course covers relevant study materials, practical exercises, and supplementary resources. It encompasses replicable study approaches and testing strategies, delving into topics such as Support Vector Machines, Decision Trees, and Dummy Classifiers. 📊💻

• machineLearning_classificacaoPorTrasDosPanos - Machine Learning: Classificação por trás dos panos

  • Description: This Machine Learning Classification course is designed to apply machine learning directly within your business context. It covers essential aspects such as understanding classification tasks, categorizing new clients based on their loyalty, and comprehending classification models: K-Nearest Neighbors, Bernoulli Naïve Bayes, and Decision Trees.
  • Materials: The course provides comprehensive study materials, practical exercises, and resources to compare the outcomes of diverse algorithms effectively. It encourages problem-solving approaches akin to those of a data scientist. 🧠🛠️

Purpose

The purpose of this repository is to organize my self-study materials for various courses, enabling easy access to course-related content and projects. By maintaining a structured repository, I aim to track progress and manage materials efficiently.

Note

Please note that the materials in each folder are for personal use and self-study. They may include resources from online courses, tutorials, or personal projects related to the respective courses.

Feel free to explore the folders and courses. Contributions, suggestions, or improvements are always welcome! 🚀🌟🔍