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Advance Linear Regression

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Introduction: Linear regression is a fundamental statistical technique that is used to model the relationship between a dependent variable and one or more independent variables. It is a simple but powerful method that is widely used in data analysis and machine learning. Linear regression has a wide range of applications, including forecasting, trend analysis, and predictive modeling. In this blog post, we will provide a comprehensive introduction to linear regression, including its concept, applications, and implementation using Python. Concept of Linear Regression: Linear regression is a statistical technique that is used to model the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that is being predicted or explained, while the independent variables are the variables that are used to make the prediction or explain the variation in the dependent variable. Linear regression assumes that the relationship betw...
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Linear Regression in Machine Learning

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 Introduction In this article we will talk about  Linear Regression in Machine Learning. Before going deep in linear regression first we will discuss a little about what is Machine Learning and its different types of algorithms. What is Machine Learning Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems. Machine learning is becoming increasingly important in many areas of modern life, including finance, healthcare, marketing, e-commerce and more. It is used to solve a wide range of problems, such as image and speech recognition, natural language processing, fraud detection, and recommendation systems. There are main three types of machine learning : Supervised   machine learning models are trained with labeled data sets, which allow the models to learn ...
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