Linear regression is one of the most used regression techniques in machine learning. However, linear regression doesn’t work all the time. As an alternative to linear regression, we can use polynomial regression. In this article, we will discuss polynomial regression. We will also implement polynomial regression using sklearn in Python. What is Polynomial Regression? Polynomial…
Logistic regression is one of the regression techniques often used in classification and prediction. In this article, we will discuss the basics of logistic regression. Additionally, we will implement logistic regression using the sklearn module in Python. What is Logistic Regression? Logistic regression is one of the machine learning techniques used to estimate the probability…
Regression analysis is used in machine learning for various prediction and classification problems. In this article, we will discuss multiple regression analysis, its assumptions, and its uses. We will also implement multiple regression analysis using the sklearn module in Python. What is Multiple Regression Analysis? Multiple regression analysis is a linear regression technique used to…
Linear regression is one of the simplest regression techniques in machine learning. In this article, we will discuss the basics of linear regression. Additionally, we will also implement linear regression using the sklearn module in Python. What is Linear Regression in Machine Learning? Linear regression is a regression technique that is used to find relationships…
In machine learning, we use various techniques to analyze data and create machine learning applications. In this article, we will discuss the basics of regression in machine learning. What is Regression in Machine Learning? Regression in machine learning is an approach to identify relationships between independent and dependent variables in a dataset. We use regression…
While analyzing a dataset, what happens if you directly apply the analytical tools to raw data collected from different sources? The analytical tools give garbage results. Similarly, you cannot use raw data in machine learning applications. You first need to perform data preprocessing to convert the raw data into a useful format. Data preprocessing includes…
A machine learning algorithm doesn’t differentiate between preprocessed data and raw data. When you feed a dataset to a machine learning algorithm, it will produce an output even if the output is wrong. For sufficiently preprocessed input data, the machine learning algorithm produces correct outputs. For rubbish data, the algorithm produces rubbish output. This is…
Nowadays, machine learning has become an inherent part of our daily lives. From our news feed to cab prices, everything is being controlled by machine learning applications. As the machine learning technology is moving forward, developers have built an array of machine learning tools that help other developers create machine learning applications in an easy…
We encounter various applications of machine learning like spam filtering of emails, YouTube video suggestions, surge pricing in Uber, etc in our daily lives. However, we don’t know exactly what machine learning techniques are being employed to facilitate these machine-learning applications. In this article, we will discuss various machine learning techniques that are used by…
Machine learning has found its way into almost every aspect of our life. In this article, we will discuss some of the trending machine learning applications in different industries. In the article, we will discuss the application of machine learning in industries like healthcare, finance, retail, marketing, manufacturing, transport, security, and customer service. Machine Learning…