Types of Data in Machine Learning

Machine Learning Tutorial Introduction to Machine Learning Evolution of Machine Learning Types Of Human Learning What is Machine Learning? How do machines learn? Types of Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Difference between Supervised, Unsupervised, and Reinforcement Learning

Machine Learning Activities Types of Data in Machine Learning Types of Data in Machine Learning-2

Selection of Model Training A Model (For Supervised Learning) Model Representation and Interpretability

Basics of Feature Engineering Feature Transformation Feature Subset Selection

Classification Introduction Classification Model Classification Learning Steps k-Nearest Neighbour (kNN) Decision Tree Random Forest Model

Regression Simple Linear Regression Multiple Linear Regression (MLR)

Unsupervised Learning Clustering

Data refers to a collection of information, facts, or statistics that are typically stored and processed electronically. It can take many forms, including text, numbers, images, audio, or video, and is used for a variety of purposes, such as analysis, research, and decision-making. A data set is a collection of related information or records. The information may be on some entity or some subject area.

For example, we may have a data set on students in which each record consists of information about a specific student. Again, we can have a data set on student performance which has records providing performance, i.e. marks on the individual subjects.

Student Data Set:

Student Performance Data Set:

A record is a single row in a data set. Additionally, each data set contains a number of attributes, each of which provides details on a distinct characteristic. For example, there are four attributes—Gender, Roll Number, Name—in the student data set. Each of the student's ages, naturally, is a distinct characteristic of the student entity. Features, variables, dimensions, and fields are additional terms for attributes. Both the Student and Student Performance data sets have four features or dimensions, so it is specified that they should be stored in a four-dimensional data space. Since each row has a unique value for each of the four attributes or features, a row or record symbolises a point in the four-dimensional data space. It makes perfect sense that the value of an attribute can change from record to record.

Introduction to Machine Learning

Process of Machine Learning

Modelling and Evaluation

Feature Engineering

Supervised Learning: Classification

Supervised Learning: Regression

Unsupervised Learning