Skip to content

danielyouk/machine_learning_daniel

Repository files navigation

Machine Learning Ground Course - FastCampus

Welcome to the official repository for the Machine Learning Ground Course offered by FastCampus. This course is designed to provide comprehensive training in machine learning fundamentals, techniques, and applications.

Repository Overview

This repository serves as the primary resource hub for students and participants of the Machine Learning Ground Course. It includes lecture materials, code examples, and additional resources to enhance your learning experience.

Current Status

  • Course Materials: For a glimpse into the quality of the course content, please visit the materials folder.

Course Objectives

The Machine Learning Ground Course aims to:

  • Introduce participants to the basics and core concepts of machine learning.
  • Provide hands-on experience through practical coding sessions and projects.
  • Explore advanced topics in machine learning including performance metrics, model evaluation, and tuning.

Learning Outcomes

By the end of this course, participants will be able to:

  • Understand and apply various machine learning models and algorithms.
  • Efficiently use data and metrics to evaluate and improve machine learning models.
  • Build and deploy machine learning applications to solve real-world problems.

Further Ideas to be added

  • bias-variance-tradeoff

Get Involved

  • Contributions: As the repository is under active development, we welcome contributions from students and experts alike. Feel free to fork the repository and submit pull requests.
  • Feedback: Your feedback is invaluable to us. If you have suggestions or find any issues, please open an issue on GitHub.

Thank you for choosing to learn with us at FastCampus. We are excited to be a part of your machine learning journey!


FastCampus | Empowering the next generation of learners.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages