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Artificial Intelligence and Data Science Enthusiast. Updating Neural Network parameters since 2002.

Foundations of Neural Networks

Both Perceptrons and sigmoid neurons are units that takes in some inputs, does some calculation and provides an output. These are used typically for Supervised Learning problems.

Perceptron

Let’s say we have some Inputs (x,x,….,x). On feeding these inputs to a perceptron, each input is multiplied with a random initial weight…


Even though there are a lot of resources out there that explain Joins in SQL, there still happens to be a lot of confusion with this topic.

So, Let’s start with the different types of JOINS that MySQL has and what they are.

  • INNER JOIN
  • OUTER JOIN
  • LEFT JOIN
  • RIGHT…


How can Regression Trees be used for Solving Regression Problems ? How to Build One.

This Blog assumes that the reader is familiar with the concept of Decision Trees and Regression. If not, refer to the blogs below.

What Are Regression Trees ?

Having read the above blogs or Having already being familiar with the appropriate topics, you hopefully…


Overview of Decision Trees and How to build One

A Decision tree is a machine learning algorithm that can be used for both classification and regression ( In that case , It would be called Regression Trees ). This blog is concentrated on Decision trees for classification.

What is a Decision Tree ?

A Decision tree is similar to a computer science tree, with a…


Why do we need feature scaling ? When to use feature scaling and What feature scaling method to use ?

Feature Scaling is a pre-processing technique that is used to bring all the columns or features of the data to the same scale. This is done for various reasons.
It is done for algorithms that involves gradient descent and also for algorithms like K-means clustering and K-Nearest Neighbors.

Why Gradient Descent Requires Feature Scaling ?

Let’s consider…


What is Logistic Regression ? Why is it used for Classification ?

What is Classification Problem ?

In general , Supervised Learning consists of 2 types of problem setting.

  • Regression : It is the type of problem where the data scientist models the relationship between the independent variables and the continuous dependent variable using a suitable model and used that to give accurate predictions for future input…


What is Cross Validation , Why it is used and What are the different types ?

Cross Validation Cycle

What is Cross Validation and Why do we Need it ?

In a Supervised Machine Learning problem , we usually train the model on the dataset and use the trained model to predict the target, given new predictor values.
But, How do we know if the model we have trained on the dataset will producing effective and accurate results on the new…


Synergy Effect or Interaction Effect is a phenomenon that arises in the multiple linear regression setting in machine learning, when increase in the value of one Independent variable increases the impact of another Independent variable on the dependent Variable.
It’s okay if this above statement is not easily understandable. …


Created by ashwinhprasad with canva

When it comes to predictive modeling , A single algorithmic model might be not be enough to make the most optimal predictions.
One of the most effective methodologies in machine learning is Ensemble Modeling or Ensembles.

Ensemble Modeling is the combination of multiple machine learning models that follow the same…


Probability Distribution Thumbnail

What is a Probability Distribution ?

Probability Distribution : A probability Distribution shows the list of probabilities associated with each value or a range of values for a discrete or a continuous random variable.
Based on the Type of Random Variable, Probability Distributions can be split into Continuous Probability Distributions and Discrete Probability Distributions

Ashwin Prasad

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