Last updated on May 13th, 2024 at 11:20 am
Are you interested in knowing linear regression and how it is used in machine learning? We got you.
Machine learning is a field of study that uses statistical and computational techniques. This enables machines to learn from data and improve their performance over time. Linear regression is a fundamental machine learning technique. It plays a critical role in predicting outcomes from data.
If you want to become a data analyst and learn data analysis and machine learning, then machine learning certification courses like Postgraduate Program in Data Science and Analytics are among the best options. In this article, we will discuss the importance of linear regression in machine learning. You can also find one of the best machine learning certification courses to improve your understanding.
Introduction to linear regression
Linear regression is a statistical technique. it is used to establish a relationship between two continuous variables. It is a popular method for predicting outcomes based on input data. The basic idea of linear regression is to find a line that best fits the data points. The line is defined by an equation of the form y = mx + c. Here y is the dependent variable, x is the independent variable, m is the slope of the line and c is the y-intercept.
Linear regression is widely used in various fields. This includes finance, economics, physics and social sciences. In machine learning, linear regression is used to predict the output variable based on one or more input variables.
Types of linear regression
Linear regressions are usually differentiated into two types: simple and multiple linear regressions.
Simple linear regression involves only one independent variable, while multiple linear regression involves two or more independent variables.
Simple linear regression is used to establish a relationship between two variables. Here one variable is dependent and the other is independent. For example, predicting the price of a house based on its size is an example of simple linear regression.
Multiple linear regression is used to establish a relationship between the dependent variable and multiple independent variables. For example, predicting the sales of a company based on multiple variables such as advertising budget, price and location is an example of multiple linear regression.
Importance of linear regression in machine learning
Linear regression is one of the most important techniques in machine learning. It is widely used in various applications such as:
Predictive modelling
Linear regression is used to predict the output variable based on input variables. For example, predicting the price of a house based on its size, location and other factors is a common application of linear regression.
Data analysis
Linear regression is used to analyse the relationship between two or more variables. It helps in identifying the significant variables that affect the outcome variable.
Feature selection
Linear regression is used to identify the most important features in a dataset. It helps in selecting the relevant features that are important for prediction.
Model evaluation
Linear regression is used to evaluate the performance of a model. It helps in determining the accuracy of the model and identifying areas for improvement.
Individuals who want to become data analysts should complete machine learning certification courses like Postgraduate Program in Data Science and Analytics to learn data analysis and machine learning concepts.
Postgraduate program in data analytics and machine learning
The Postgraduate Program in Data Science and Analytics is a 6-month job-oriented curriculum course for working professionals and young data analysts. Students who complete this course create analytical models that enhance company results. They can also comprehend how data science is used in practical settings.
With the course, you will gain helpful information about the uses of analytics and data science in actual organisations. You will also get equipped to work as a data science specialist in the developing industry. Also, it provides you with the opportunity to work with leading firms and learn about the most recent developments in the analytics field. Following are the various benefits of completing the Postgraduate Program in Data Science and Analytics:
The Postgraduate Program in Data Science and Analytics focuses on practical knowledge and a job-specific curriculum.
It offers guaranteed job opportunities where students have a chance to get placed in companies like Infosys, Goldman Sachs, Deloitte, KPMG, IBM and more.
Students who complete the program are provided with a KPMG certificate.
To sum up
Linear regression is a fundamental machine learning technique that plays a critical role in predicting outcomes from data. It is widely used in various applications such as predictive modelling, data analysis, feature selection and model evaluation. Understanding the basics of linear regression is essential for anyone working in the field of machine learning.
Those interested in becoming data analysts should enrol in our Postgraduate Program in Data Science and Analytics. For any queries, feel free to contact us.
