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In this chapter , We will discuss KNN Learning in Scikit-Learn. k-NN (k-Nearest Neighbor), one of the simplest machine learning algorithms, is non-parametric and lazy in nature. Non-parametric means that…

This chapter we will discuss K-Nearest Neighbors (KNN) and help you in understanding the nearest neighbor methods in Sklearn. Neighbor based learning method are of both types namely supervised and unsupervised. Supervised neighbors-based learning…

Here, we will learn about what is anomaly detection in Sklearn and how it is used in identification of the data points. Anomaly detection is a technique used to identify…

This chapter deals with a machine learning method termed as Support Vector Machines (SVMs). Introduction Support vector machines (SVMs) are powerful yet flexible supervised machine learning methods used for classification,…

Here, we will learn about an optimization algorithm in Sklearn, termed as Stochastic Gradient Descent (SGD). Stochastic Gradient Descent (SGD) is a simple yet efficient optimization algorithm used to find…

This chapter, we will discuss Extended Linear Modeling and focusses on the polynomial features and pipelining tools in Sklearn. Introduction to Polynomial Features Linear models trained on non-linear functions of…