There are the five major assumptions: 1. Linear relationship: There should be a linear and additive relationship between the dependent (Y) variable and the independent (X –> x1,x2,x3,…) variable(s). A linear relationship suggests that a change in response Y due to one unit change in x1 is constant, regardless of…
Unsupervised learning is a class of ML where we do not have any clue about the target data. We have to approach the problem by clustering the given unlabeled data based on the relationship between the input variables. We have to analyze the data; find patterns and relationships among them….
Supervised learning is a class of ML where machines can learn through examples. The historical data set has both input and output (also called target or response) values. Based on the training data set, the machine already has some idea of what the target value would look like, which kind…
I will discuss the most popular machine learning algorithms. There are so many algorithms available and I categorized algorithms based on the learning style. Let’s take a look at four different learning styles in machine learning algorithms: Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning Here is the list of…
What is Machine Learning(ML) As we humans learn from our past experiences, machines learn from past data. What does it mean? We tend to respond to a situation based on our past experiences. In our brain, we have stored all the situations we had encountered and the corresponding responses as…