https://github.com/eesunmoon/applied_machine_learning

[COMS4995] Applied Machine Learning
https://github.com/eesunmoon/applied_machine_learning

applied-machine-learning clustering cnn data-preprocessing eda linear-regression machine-learning matplotlib neural-networks nltk-python numpy pandas pca python scikit-learn seaborn tensorflow text-classification

Last synced: 4 days ago
JSON representation

[COMS4995] Applied Machine Learning

• Host: GitHub
• URL: https://github.com/eesunmoon/applied_machine_learning
• Owner: EesunMoon
• Created: 2024-10-14T01:46:58.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-08T16:36:07.000Z (14 days ago)
• Last Synced: 2024-12-08T17:27:59.083Z (14 days ago)
• Topics: applied-machine-learning, clustering, cnn, data-preprocessing, eda, linear-regression, machine-learning, matplotlib, neural-networks, nltk-python, numpy, pandas, pca, python, scikit-learn, seaborn, tensorflow, text-classification
• Language: Jupyter Notebook
• Homepage:
• Size: 4.35 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Applied_Machine_Learning

**[Fall 2024]**

## Overview

| #  | Concept                               | Details                                                                                                                |

|:---:|:-------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------:|

| 1   | **Exploratory Data Analysis (EDA) & Visualization**  |  |

| 2   | **Supervised Learning & Data Preprocessing** |  |

| 3   | **Linear Regression/Classification & Support Vector Machine(SVM)**     | [Logistic Regression](https://github.com/EesunMoon/Applied_Machine_Learning/blob/main/Assignment/HW1_%5Bem3907%5D.ipynb) Data Visualization and Analysis
Linear Models for Regression and Classification
Support Vector Machines |

| 4   | **Trees, Ensembles, Random Forest, Boosting**           | [Ensemble Method](https://github.com/EesunMoon/Applied_Machine_Learning/blob/main/Assignment/HW2_%5Bem3907%5D.ipynb) 
Decision Trees
Random Forests
Gradient Boosted Trees |

| 5   | **Model Evaluations**| |

| 6   | **Model Interpretation, Feature Selection, Dimensionality Reduction, Clustering** | |

| 7   | **Imbalanced/Sparse Data**            | [Imbalanced Data](https://github.com/EesunMoon/Applied_Machine_Learning/blob/main/Assignment/HW3_%5Bem3907%5D.ipynb) 
Imbalanced Data(Oversampling / Undersampling / SMOTE)
Model Prediction & Evaluation - AUC Scores / Confusion Matrix / ROC Curves |

| 8   | **Deep Neural Networks (DNN)**       |  |

| 9   | **Convolutional Neural Networks (CNN) & Recurrent Neural Networks (RNN)**       | [Neural Networks](https://github.com/EesunMoon/Applied_Machine_Learning/blob/main/Assignment/HW4_%5Bem3907%5D.ipynb) Convolutional Neural Networks |

| 10  | **Text Data Preprocessing & Embedding**       | [Text Classification](https://github.com/EesunMoon/Applied_Machine_Learning/blob/main/Assignment/HW5_%5Bem3907%5D.ipynb) Text Preprocessing (nltk) |

| 11  | **Recommender System**       |  |

| 12  | **ML Production**       |  |