DataSci-Mindmap

A comprehensive Data Science Mindmap for quick revision.

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Table of Contents

• Code Examples
• Detailed Discussion
• Project Examples
• Flow Diagram Mindmaps
• Contributing
• License

Code Examples

Each topic includes practical code examples in Python, covering:

• Model implementation
• Data preprocessing
• Evaluation metrics
• Visualization

Explore the respective markdown files in each folder for hands-on code snippets.

Detailed Discussion

In-depth explanations are provided for each algorithm and concept, including:

• Mathematical intuition
• Use cases
• Pros and cons
• Hyperparameters

Refer to the markdown files for detailed notes and references.

Project Examples

Sample end-to-end project outlines and mini-projects are included to demonstrate real-world applications of:

• Classification
• Regression
• Unsupervised learning
• Loss functions

Flow Diagram Mindmaps

For each topic, a flow diagram or mindmap is provided to visually summarize:

• Key concepts
• Relationships between algorithms
• Decision trees for model selection

Mindmaps are available as images or ASCII diagrams in the respective markdown files.

Contributing

Contributions are welcome! To contribute:

• Fork the repository
• Create a new branch
• Add or update markdown files, code, or diagrams
• Submit a pull request

Please see the CONTRIBUTING.md for more details (or create one if it does not exist).

License

This project is licensed under the MIT License. See the LICENSE file for details.

Project Structure

• LossFunctions/: Various loss functions used in machine learning, including:

  • Classification Loss Functions
  • Regression Loss Functions
  • Contrastive & Self-Supervised Losses
  • GAN & Generative Model Losses
  • Object Detection Losses
  • Ranking & Learning-to-Rank Losses
  • Reinforcement Learning Losses
  • Segmentation & Pixel-wise Losses

• ML-Classification-Models/: Collection of classification models, such as:

  • Decision Trees, Random Forest, Extra Trees
  • Logistic Regression, Ridge Classifier, SGDClassifier
  • Naive Bayes (Bernoulli, Gaussian, Multinomial, Complement)
  • K-Nearest Neighbors, Radius Neighbors
  • SVM, One-Class SVM
  • Ensemble Methods (Bagging, Boosting, Stacking, Voting)
  • Neural Networks (MLP, Deep Neural Networks, TabNet, Transformer-Based)
  • Outlier Detection (Isolation Forest, Local Outlier Factor)
  • Others (Bayesian Network, Rule-Based, etc.)

• ML-Regression-Models/: Collection of regression models, including:

  • Linear, Ridge, Lasso, ElasticNet, Polynomial, Quantile, Huber, Theil-Sen, Tweedie
  • Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost, Bagging
  • Bayesian Methods (Bayesian Ridge, Bayesian Neural Networks)
  • K-Nearest Neighbors, Isotonic, RANSAC, LARS
  • Support Vector Regression, TabNet, XGBoost, LightGBM, CatBoost
  • Stacking, Voting

• ML-Unsupervised-Models/: Unsupervised learning models and algorithms:

  • Clustering (K-Means, K-Medoids, Agglomerative, Divisive, BIRCH, DBSCAN, HDBSCAN, OPTICS, Mini-Batch K-Means)
  • Dimensionality Reduction (PCA, Kernel PCA, Truncated SVD, UMAP, t-SNE, LSA, ICA, NMF, MDS, Isomap, LLE)
  • Association Rule Mining (Apriori, ECLAT, FP-Growth)
  • Gaussian Mixture Models, Bayesian Gaussian Mixture
  • Autoencoders

Usage

Browse the markdown files in each folder for concise notes and mindmaps on each topic. Ideal for quick revision and interview preparation.

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Contributions welcome!