Docs:
- https://xgboost.readthedocs.io
- https://lightgbm.readthedocs.io
- https://catboost.ai
- https://featuretools.alteryx.com
- https://evalml.alteryx.com
- https://scikit-learn.org/stable/user_guide.html
- https://github.com/cod3licious/autofeat
- https://github.com/aerdem4/lofo-importance
- https://hyperopt.github.io/hyperopt/
- http://hyperopt.github.io/hyperopt/getting-started/search_spaces/
- https://github.com/hyperopt/hyperopt-sklearn
- https://www.ray.io/ray-tune
- https://optuna.org/
Coarse Model Selection Resources/References:
- Decision Tree
- Pros and Cons of Decision Trees by Aashima Yuthika
- Decision and Classification Trees, Clearly Explained!!! by Josh Starmer
- Regression Trees, Clearly Explained!!! by Josh Starmer
- Decision Trees Explained by James Thorn
- Random Forest
- Random Forests Part 1 - Building, Using and Evaluating by Josh Starmer
- Random Forests Part 2: Missing data and clustering by Josh Starmer
- Random Forest Explained by James Thorn
- Pros and Cons of Random Forest by Aashima Yuthika
- Extra Trees
- An Intuitive Explanation of Random Forest and Extra Trees Classifiers by Frank Ceballo
- What is the difference between Extra Trees and Random Forest? by Pablo Aznar
- How to Develop an Extra Trees Ensemble with Python by Jason Brownlee
- SVM
- Pros And Cons Of Support Vector Machine (SVM)
- Support Vector Machines Part 1 (of 3): Main Ideas!!! by Josh Starmer
- Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3) by Josh Starmer
- Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3) by Josh Starmer
- MIT OpenCourseWare: 16. Learning: Support Vector Machines by MIT
- Support Vector Machine (SVM) Explained by Vatsal
- Introduction to Support Vector Machines (SVM) by GeeksforGeeks alokesh985
- XGBoost
- AdaBoost, Clearly Explained by Josh Starmer
- Gradient Boost Part 1 (of 4): Regression Main Ideas by Josh Starmer
- Gradient Boost Part 2 (of 4): Regression Details by Josh Starmer
- Gradient Boost Part 3 (of 4): Classification by Josh Starmer
- Gradient Boost Part 4 (of 4): Classification Details by Josh Starmer
- XGBoost in Python from Start to Finish by Josh Starmer
- XGBoost Part 1 (of 4): Regression by Josh Starmer
- XGBoost Part 2 (of 4): Classification by Josh Starmer
- XGBoost Part 3 (of 4): Mathematical Details by Josh Starmer
- XGBoost Part 4 (of 4): Crazy Cool Optimizations by Josh Starmer
- XGBoost: An Intuitive Explanation by Ashutosh Nayak
- Slides on XGBoost by Tianqi Chen
- XGBoost Paper
- CatBoost
- XGBoost, LightGBM or CatBoost – which boosting algorithm should I use? by Nir Alal
- Understanding CatBoost Algorithm by Meet Raval
- LightGBM
- A Quick Guide to the LightGBM Library by Samrat Thapa
- LightGBM (Light Gradient Boosting Machine) by GeeksforGeeks shreyanshisingh28
- XGBOOST vs LightGBM: Which algorithm wins the race !!! by Sai Nikhilesh Kasturi
- CatBoost vs. Light GBM vs. XGBoost by Alvira Swalin
- Neural Networks
- Deep Learning Crash Course for Beginners by freeCodeCamp
- Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial by deeplizard
- Introduction | Deep Learning Tutorial 1 (Tensorflow Tutorial, Keras & Python) by codebasics
- TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial by TechWithTim
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
- PyTorch Tutorials by Aladdin Persson
Feature Engineering:
- LSTM Feature Importance by Chris Deotte
- Kaggle Tips for Feature Engineering and Selection | by Gilberto Titericz | Kaggle Days Meetup Madrid by Gilberto Titericz
- Beyond Feature Engineering and HPO | by Jean-François Puget | Kaggle Days Paris by Jean-François Puget
- Here’s All you Need to Know About Encoding Categorical Data (with Python code) by Shipra Saxena
- Fundamental Techniques of Feature Engineering for Machine Learning by Emre Rençberoğlu
- Feature Engineering: Processes, Techniques & Benefits in 2023 by Cem Dilmegani
- Best Practices for Feature Engineering
- Discover Feature Engineering, How to Engineer Features and How to Get Good at It by Jason Brownlee
- Feature Engineering by Sebastian Taylor
- scikit-learn Feature Selection Page
- Feature Importance — Everything you need to know by Sandeep Ram
- Which is the right feature importance? by Kaggle AmbrosM
- Feature Engineering for Categorical Data by Zhenghao Xiao
- Types of scalers in sklearn and their import statements by Kaggle Yogesh Singla
- 11 Dimensionality reduction techniques you should know in 2021 by Rukshan Pramoditha
- What‘s the advantages and disadvantages between the different dimension reduction methods? by Luis Argerich
Hyperparameter Tuning:
- 10 Hyperparameter optimization frameworks. by Sivasai Yadav Mudugandla
- Hyperparameter Tuning Techniques by Karndeep Singh
- How (Not) to Tune Your Model With Hyperopt by Sean Owen
- LGBM Optuna Hyperparameter Tuning w. Understanding by Kaggle BEXGBOOST
- XGBoost & Catboost Using Optuna 🏄🏻♂️ by Kaggle HAMZA
Ensembling:
- Ensembling in Machine Learning. by Burhanuddin Rangwala
- Blending Ensemble Machine Learning With Python by Jason Brownlee
- hard voting versus soft voting in ensemble based methods [duplicate]
- KAGGLE ENSEMBLING GUIDE
- Basic Ensemble Techniques in Machine Learning by Himanshi Singh
MLOps:
- MLOPS: A COMPLETE GUIDE TO MACHINE LEARNING OPERATIONS | MLOPS VS DEVOPS by Ashutosh Tripathi
- Streamlit cheat sheet by Daniel Lewis