https://github.com/nemat-al/introduction_to_machine_learning
Tasks for Introduction to Machine Learning Course @ ITMO University
https://github.com/nemat-al/introduction_to_machine_learning
k-means-clustering knn-algorithm logistic-regression machine-learning
Last synced: 8 months ago
JSON representation
Tasks for Introduction to Machine Learning Course @ ITMO University
- Host: GitHub
- URL: https://github.com/nemat-al/introduction_to_machine_learning
- Owner: nemat-al
- Created: 2024-04-19T16:24:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-19T16:57:55.000Z (over 1 year ago)
- Last Synced: 2025-01-23T08:44:35.428Z (10 months ago)
- Topics: k-means-clustering, knn-algorithm, logistic-regression, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 30.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Introduction_To_Machine_Learning
Tasks for Introduction to Machine Learning Course at ITMO University.
[First Task](https://github.com/nemat-al/Introduction_To_Machine_Learning/blob/main/Basics%20Statics%20Metrices.ipynb), is about calculating simple statical
metrices: sample median, variance estimate, sample mean, and standard deviation.
[Second Task](https://github.com/nemat-al/Introduction_To_Machine_Learning/blob/main/KNN.ipynb), is about applying KNN algorithm using Manhattan and Euclidean distances.
[Third Task](https://github.com/nemat-al/Introduction_To_Machine_Learning/blob/main/LogisticRegression.ipynb), is about training a Logistic Regression model on a dataset, then applying the model on test data and finding probabilities of the assigned classes to the test dataset, and finally calculating Recall, Precision, and AUC.
[Fourth Task](https://github.com/nemat-al/Introduction_To_Machine_Learning/blob/main/K-Means.ipynb), is about applying K-Means clustering algorithm on a dataset and calculating the distances from the dataset points to the centroids.