This repository comprises Jupyter notebooks containing various Python libraries that I found very useful for data science. Most of the code was collected from articles on Medium, an online publishing platform, for the purpose of personal study and practice. In some cases, the code was modified by myself to ensure that it actually works.
- General Tips, PRegEx, Pathlib, PyCircular, Decorators, OpenCV, make & Makefile, Watchdog
- Pandas, NumPy, FiftyOne, PySpark, Upgini, Synthetic Dataset
- Sweetviz, Matplotlib/Plotly/Seaborn, PyGWalker
- scikit-learn, Mahalanobis Distance, Open3D, PyMLPipe, Reinforcement Learning, Predictive Maintenance
- Dash, Streamlit, Gradio, Modelbit, PyScript
- Basics
- CNN: Binary Classification, 1D/2D comparison, Transfer Learning, Multi-Classification, Multi-Label Classification
- Visions: Image Captioning, Image Segmentation, Object Detection
- Generative AI: DPDM
- Advanced Topics: Temporal Fusion Transformer, Physics-Informed NN, Graph Neural Network, Transformer
- Basics, TensorBoard, Autoencoder
- NLTK
- Optuna
- PyCaret
- Statistical Testing Flowchart, Distributions and Collinearity, Categorical Correlation, A/B Testing with Resampling/Booststrapping, Power Analysis