ML-E8: Machine learning basics (bias, variance, over-fitting, accuracy, precession, validation etc)
Machine learning (ML) is a powerful field that applies computational and statistical techniques to empower computer systems to learn and improve from experience, without explicit programming.
This article will cover essential basics, including primary types of ML tasks, essential concepts, and various processes involved in machine learning.
ML series menu: E1 E2 E3 E4 E5 E6 E7 E8 E9
Section 1: Types of Machine Learning Tasks
Machine learning tasks can broadly be divided into two categories: classification and regression. The distinction lies in the nature of the output or prediction that the machine learning model is trained to produce.
1.1 Classification
Classification is a type of machine learning task where the output variable is a category or a class. It involves predicting the class or category of an object or sample. Examples of classification algorithms include Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks.
Example: Consider an email spam detection system. Here, the system classifies emails into two categories — “Spam” or “Not Spam.” This is a binary classification problem.
