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This article covers how and when to use  k-nearest neighbors classification with scikit-learn. Focusing on concepts, workflow, and examples. We also cover distance metrics and how to select the best value for k using cross-validation.

K-Nearest Neighbors (KNN) Classification with scikit-learn 

In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package.

Decision Tree Classification in Python Tutorial 

Learn about data augmentation techniques, applications, and tools with a TensorFlow and Keras tutorial.  

A Complete Guide to Data Augmentation

A Scikit-Learn tutorial to using logistic regression and random forest models to predict which baseball players will be voted into the Hall of Fame

Scikit-Learn Tutorial: Baseball Analytics  Pt 2

A scikit-learn tutorial to predicting MLB wins per season by modeling data to KMeans clustering model and linear regression models. 

Scikit-Learn Tutorial: Baseball Analytics Pt 1

This step-by-step tutorial will show you how to set up and use Jupyter Notebook on Amazon Web Services (AWS) EC2 GPU for deep learning. 

Deep Learning with Jupyter Notebooks in the Cloud

You can preprocess the heck out of your data but the proof is in the pudding: how well does your model then perform?

Preprocessing in Data Science (Part 3): Scaling Synthesized Data

Discover whether centering and scaling help your model in a logistic regression setting.

Preprocessing in Data Science (Part 2): Centering, Scaling and Logistic Regression

This article will explain the importance of preprocessing in the machine learning pipeline by examining how centering and scaling can improve model performance.

Machine Learning Tutorial

Scikit-Learn Tutorial: Baseball Analytics Pt 2

Scikit-Learn Tutorial: Baseball Analytics Pt 1

Deep Learning with Jupyter Notebooks in the Cloud

Preprocessing in Data Science (Part 3): Scaling Synthesized Data

Preprocessing in Data Science (Part 2): Centering, Scaling and Logistic Regression

Preprocessing in Data Science (Part 1): Centering, Scaling, and KNN