Naive Data

Photo by  Arseny Togulev This is the second part of the series on the Basics of Machine Learning. Please refer to this  link  for part 1. Assumptions of Simple Linear Regression Below are the main assumptions of linear regression: There is a  linear  relationship between X and Y. Error terms are  normally  distributed. Error terms are  independent  of each other. Change in one error term should not impact the other error terms. Error terms have  constant variance  i.e. variance shouldn’t increase or decrease as the error values change. Also, they shouldn’t follow any pattern. If the variance is not constant or homoscedastic then the inferences made on the model would be unreliable. Checking the Model Fit: We should always make sure that the model fit is really a good fit and is not by a chance. There are many ways to test it. T-Test: The t-distribution is also a kind of normal distribution; it is also symmetric and single-peaked, bu...

The Artificial Neural Network (ANN) is inspired by the animal brain. Even though currently the ANNs are not as powerful as the brain yet, these are one of the most powerful learning models in the field of machine learning. Understanding the Human Brain and ANNs Let's understand first how the human brain works and how it influences the ANNs. The biological neuron receives signals through its dendrites which are either amplified or inhibited as they pass through the axons to the dendrites of other neurons. ANNs are also based on a similar concept, where ANNs are a collection of a large number of simple devices called Artificial Neurons. The network learns  to perform certain tasks ( like identifying a car ) by training the neurons to fire an action when a particular input ( like an image of a car) is provided. Perceptron Perceptron is one of the earlier proposed models, which takes a weighted sum of the inputs and applies an activation function to it...