A practical guide to the Scikit-Learn library has been provided through the following tutorials:
- Introduction to and Installation of the Scikit-Learn Library
- How is the Modeling Process Done in Scikit-Learn?
- Data Representation Methods in Scikit-Learn
- The Use of Estimator API in Scikit-Learn
- Purpose and Types of Conventions in Scikit-Learn
- How Does Linear Modeling Work in Scikit-Learn?
- Effectiveness of Extended Linear Modeling in Scikit-Learn
- Stochastic Gradient Descent for Parameter Estimation in Scikit-Learn
- Types of Support Vector Machine (SVM) in Scikit-Learn
- Techniques for Anomaly Detection Process in Scikit-Learn
- Types of K-Nearest Neighbors (KNN) Algorithms and Learning Techniques in Scikit-Learn
- Classification With Nave Bayes in Scikit-Learn
- Decision Tree Algorithms in Scikit-Learn
- Purpose and Types of Boosting Methods in Scikit-Learn
- How Do Clustering Methods Perform in Scikit-Learn?
- Evaluation of Clustering Performance in Scikit-Learn
- Dimensionality Reduction Using PCA in Scikit-Learn
They are available in the Tutorials section of our website.
