https://github.com/geraked/machine-learning
Implementation of Machine Learning Algorithms From Scratch
https://github.com/geraked/machine-learning
ai amirkabir-university artificial-intelligence ce5501 classification clustering computer-engineering computer-science data-science geraked machine-learning machine-learning-algorithms machine-learning-from-scratch ml ml-from-scratch outlier-detection rabist regression
Last synced: 4 months ago
JSON representation
Implementation of Machine Learning Algorithms From Scratch
- Host: GitHub
- URL: https://github.com/geraked/machine-learning
- Owner: geraked
- License: mit
- Created: 2023-04-25T10:22:52.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-04-25T10:34:18.000Z (about 2 years ago)
- Last Synced: 2025-01-03T14:50:31.489Z (6 months ago)
- Topics: ai, amirkabir-university, artificial-intelligence, ce5501, classification, clustering, computer-engineering, computer-science, data-science, geraked, machine-learning, machine-learning-algorithms, machine-learning-from-scratch, ml, ml-from-scratch, outlier-detection, rabist, regression
- Language: Jupyter Notebook
- Homepage:
- Size: 51.3 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Machine Learning
Implementation of Some of the Machine Learning (ML) Algorithms From Scratch in Python
## Homework 1
[Problems](hw1/problem.pdf) | [Solutions (Report)](hw1/report.pdf)
- Data Visualization ([Source code](hw1/src/P2.ipynb))
- Univariate Polynomial Regression using Gradient Descent and Normal Equation ([Source code](hw1/src/P3.ipynb))
- Multivariate Polynomial Regression, Stepwise Feature Selection (Backward Elimination), Cook's Distance and DFFITS plots from scratch. Fixing Heteroscedasticity and Multicollinearity. ([Source code](hw1/src/P4.ipynb))
## Homework 2
[Problems](hw2/problem.pdf) | [Solutions (Report)](hw2/report.pdf)
- Decision Tree ([Source code](hw2/src/P3.ipynb))
- Ensemble Learning - Bagging algorithm using Decision Tree (Random Forest) ([Source code](hw2/src/P2.ipynb))
- KNN using Euclidean and Cosine distance, Confusion Matrix for Multi-class Classification, Optical Recognition of Handwritten Digits ([Source code](hw2/src/P4.ipynb))
## Homework 3
[Problems](hw3/problem.pdf) | [Solutions (Report)](hw3/report.pdf)
- Implementation of Naive Bayes Algorithm for Spam Email Detection ([Source code](hw3/src/P3.ipynb))
## Homework 4
[Problems](hw4/problem.pdf) | [Solutions (Report)](hw4/report.pdf)
- Custom kernels for Support Vector Machine (SVM) ([Source code](hw4/src/P2.ipynb))
## Homework 5
[Problems](hw5/problem.pdf) | [Solutions (Report)](hw5/report.pdf)
- K-means clustering, Outlier detection ([Source code](hw5/src/P2.ipynb))
- DBSCAN, Evaluation Metrics (Accuracy, Entropy, Purity) ([Source code](hw5/src/P3.ipynb))
- Q-learning through Epsilon-Greedy Algorithm ([Source code](hw5/src/P4.ipynb))
## Final Project
[Problems](final-project/problem.pdf) | [Solutions (Report)](final-project/report.pdf)
- Multi-output Regression ([Source code](final-project/src/P1.ipynb))
- Skin Detection ([Source code](final-project/src/P2.ipynb))
- Credit Approval ([Source code](final-project/src/P3.ipynb))
## Author
**Rabist** - view on [LinkedIn](https://www.linkedin.com/in/rabist)
## Details
- **Course:** Machine Learning (CE5501) - MS
- **Teacher:** [Dr. Ehsan Nazerfard](https://aut.ac.ir/cv/2384/EHSAN-NAZERFARD?slc_lang=en)
- **Univ:** Amirkabir University of Technology
- **Semester:** Fall 2022
## License
Licensed under [MIT](LICENSE).