A Practical Guide to Scikit-Learn

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.