https://github.com/ruoheng-du/applied-machine-learning

Applied Machine Learning | Fall 2024
https://github.com/ruoheng-du/applied-machine-learning

imbalanced-data linear-models natural-language-processing neural-networks svm-model tree-based-models

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Applied Machine Learning | Fall 2024

• Host: GitHub
• URL: https://github.com/ruoheng-du/applied-machine-learning
• Owner: ruoheng-du
• Created: 2024-10-01T02:17:49.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-08T06:17:36.000Z (over 1 year ago)
• Last Synced: 2025-04-05T12:12:10.999Z (about 1 year ago)
• Topics: imbalanced-data, linear-models, natural-language-processing, neural-networks, svm-model, tree-based-models
• Language: Jupyter Notebook
• Homepage:
• Size: 2.93 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Applied Machine Learning | Fall 2024

This repository contains code implementations and analysis done in the Applied Machine Learning course during Fall 2024. The course focuses on practical applications of machine learning techniques, exploring various algorithms, tools, and approaches to solve real-world data-driven problems. The objective of the course is to understand machine learning algorithms to solve structured and unstructured data problems, and address challenges in model training, such as overfitting, underfitting, and class imbalance. The repository is organized into the following key notebooks:

1. **Linear_Models_SVMs.ipynb**  

   Implementation and analysis of linear models and Support Vector Machines (SVMs) for classification and regression tasks.



2. **Tree-based_Ensemble_Models.ipynb**  

   Application of tree-based ensemble methods like Random Forests and Gradient Boosting to enhance predictive performance and interpretability.



3. **Imbalanced_Dataset.ipynb**  

   Techniques and strategies to handle imbalanced datasets, including resampling methods and evaluation metrics tailored for imbalanced data.



4. **Natural_Language_Processing.ipynb**  

   Exploration of NLP techniques, including text preprocessing, tokenization, and machine learning-based language models.



5. **Neural_Networks.ipynb**  

   Development and training of neural network models for complex prediction tasks, including hyperparameter tuning and performance evaluation.



Please feel free to email me at ruoheng.du@columbia.edu for any more information.