https://github.com/labex-labs/supervised-learning-classification

[Supervised Learning: Classification] This repository collects 10 of programming scenarios (labs and challenges) for Supervised Learning: Classification. During this course, we will continue to learn another important application in supervised learning - solving classification problems. In the fo...
https://github.com/labex-labs/supervised-learning-classification

List: supervised-learning-classification

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[Supervised Learning: Classification] This repository collects 10 of programming scenarios (labs and challenges) for Supervised Learning: Classification. During this course, we will continue to learn another important application in supervised learning - solving classification problems. In the fo...

• Host: GitHub
• URL: https://github.com/labex-labs/supervised-learning-classification
• Owner: labex-labs
• Created: 2024-05-22T07:15:07.000Z (8 months ago)
• Default Branch: master
• Last Pushed: 2024-08-01T01:27:51.000Z (5 months ago)
• Last Synced: 2024-08-02T04:23:07.925Z (5 months ago)
• Topics: awesome, awesome-list, challenges, course, education, hands-on, labex, labs, machine-learning, programming, scikit-learn
• Homepage: https://labex.io/skilltrees/ml
• Size: 8.79 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Supervised Learning: Classification

[![Supervised Learning: Classification](https://cover-creator.appbot.io/supervised-learning-classification.png)](https://labex.io/courses/supervised-learning-classification)

[![Start-Learning](https://img.shields.io/badge/Start-Learning-whitesmoke?style=for-the-badge)](https://labex.io/courses/supervised-learning-classification)

During this course, we will continue to learn another important application in supervised learning - solving classification problems. In the following lessons, you will be exposed to: logistic regression, K-nearest neighbor algorithm, naive Bayes, support vector machine, perceptron and artificial neural network, decision tree and random forest, and bagging and boosting methods. The course will start with the principle of each of these methods. You are supposed to fully understand the implementat

![scikit-learn](https://img.shields.io/badge/scikit-learn-whitesmoke?style=for-the-badge&logo=scikit-learn)

![Machine-Learning](https://img.shields.io/badge/Machine-Learning-whitesmoke?style=for-the-badge&logo=machine-learning)

## Environment

LabEx is an interactive, hands-on learning platform dedicated to coding and technology. It combines labs, AI assistance, and virtual machines to provide a no-video, practical learning experience.

![](https://tutorial-screenshot.getvm.io/images/vm-1725247253.png)

- A strict “Learn by Doing” approach with exclusive hands-on labs and no videos.

- Interactive online environments within the browser, with automated step-by-step checks.

- A structured content organization with the Skill Tree based learning system.

- A growing learning resource of 30 Skill Trees and over 6,000 Labs.

- The AI learning assistant Labby, built on ChatGPT, providing a conversational learning experience.

Learn more about [LabEx VM](https://support.labex.io/using-labex/virtual-machine).

## Exercises

|   Index | Name                                                    | Difficulty   | Practice                                                                                                                      |

|---------|---------------------------------------------------------|--------------|-------------------------------------------------------------------------------------------------------------------------------|

|      01 | 📖 Logistic Regression Classification with Scikit-Lea... | ★☆☆          | Start Lab   |

|      02 | 📖 K Nearest Neighbor Algorithm                          | ★☆☆          | Start Lab                           |

|      03 | 📖 Probabilistic Classification with Naive Bayes         | ★☆☆          | Start Lab          |

|      04 | 📖 Implementation of Gaussian Distribution Function a... | ★☆☆          | Start Lab |

|      05 | 📖 Nonlinear Pattern Recognition Techniques              | ★☆☆          | Start Lab               |

|      06 | 📖 Perceptron and Artificial Neural Network              | ★☆☆          | Start Lab               |

|      07 | 📖 Train Handwritten Digits Recognition Neural Networ... | ★☆☆          | Start Lab    |

|      08 | 📖 Decision Tree Classification with Python              | ★☆☆          | Start Lab               |

|      09 | 📖 Bagging and Boosting Method                           | ★☆☆          | Start Lab                            |

|      10 | 📖 Quickly Select Models with Cross Validation           | ★☆☆          | Start Lab            |

## More

- 🔗 [Machine Learning Programming Courses](https://github.com/labex-labs/awesome-programming-courses)

- 🔗 [Machine Learning Programming Projects](https://github.com/labex-labs/awesome-programming-projects)

- 🔗 [Machine Learning Free Tutorials](https://github.com/labex-labs/ml-free-tutorials)