Applied-Machine-Learning-

Welcome to the "Applied Machine Learning" repository! This repository serves as a comprehensive resource for understanding and implementing various machine learning concepts in real-world scenarios.

Notebooks Included:

Jupyter Notebooks are provided for each topic, containing detailed explanations, code implementations, and practical examples. These notebooks are designed to be beginner-friendly yet offer insights for advanced users.

How to Use This Repository:

• Navigate through folders to find specific topics of interest. Each topic includes a dedicated notebook with step-by-step implementations and theoritical explaination.
• Clone or download the repository to your local machine and open the Jupyter Notebooks in your favorite environment. Follow along with the code and experiment with the provided examples.
• Feel free to contribute by adding your own implementations, enhancements, or additional topics. Your contributions can help create a valuable learning resource for the community.

Getting Started:

• Clone the repository:

     git clone https://github.com/Syeda-Farhat/Applied-Machine-Learning-.git
  

  Start exploring the notebooks and enhance your machine learning skills!

• Your feedback is highly valuable. If you encounter any issues, have suggestions, or want to contribute, please open an issue or submit a pull request.

• The repository will be regularly updated with new notebooks covering additional applied machine learning topics. Stay tuned for more content!

Topics

• Data Preprocessing and EDA using numeric Data : dataset .
• Data Preprocessing and EDA using txt Data : dataset.

Classification

• Binary Classification

  • Logistic Regression
  • Support Vector Machines (SVM)
  • Decision Trees
  • Random Forest
  • Neural Networks

• Multi-Class Classification

  • Multinomial Logistic Regression
  • Support Vector Machines (SVM) with multi-class support
  • Decision Trees for multi-class problems
  • Random Forest for multi-class problems
  • Neural Networks for multi-class classification

• Imbalanced Classification

  • Techniques for handling imbalanced datasets
  • Resampling methods (oversampling, undersampling)
  • Cost-sensitive learning
  • Ensemble methods for imbalanced data

• Text Classification

  • Natural Language Processing for text classification
  • Bag-of-Words and TF-IDF representations
  • Word embeddings (e.g., Word2Vec, GloVe)
  • Recurrent Neural Networks (RNN) for text classification
  • Transformer models (e.g., BERT) for text classification

• [Ensemble Methods]

  • Bagging (e.g., Bootstrap Aggregating)
  • Boosting (e.g., AdaBoost, Gradient Boosting)
  • Stacking multiple models

Regression

• [Linear Regression]

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression

• [Regularization in Regression]

  • Ridge Regression
  • Lasso Regression
  • Elastic Net Regression

• [Decision Trees for Regression]

  • Regression Trees
  • Random Forest for regression
  • Gradient Boosted Trees for regression

• [Support Vector Machines (SVM) for Regression]

  • Support Vector Regression (SVR)

• [Ensemble Methods for Regression]

  • Bagging and boosting techniques for regression tasks
  • Stacking models for improved regression performance