10 awesome ML datasets that deserve your attention!
A list of not-so-popular machine learning articles that are highly useful yet underrated
An Introduction to Computer Vision
What is Computer Vision? Computer Vision is a subcategory of artificial intelligence that deals with how computers can gain a high-level understanding of the visual world from digital images or videos. If AI enables computers to think, computer vision enables them to see, observe and understand. Though early experiments in computer vision started in the 1950s and…
Decoding Indian Journalism on Twitter with Data
I believe that if used the right way, data can become the fifth pillar to maintain and improve our democracies. It is up to us to stand up and present concrete facts before society to keep the powerful accountable to the last person standing in the queue. With this post, we debut our upcoming series…
Popular Machine Learning Datasets
Machine learning models require a large amount of data in order to learn about a specific subject. This collection of data is called a dataset. When working with machine learning methods we typically need a few datasets for different purposes. We can rely on open-source datasets to initiate ML execution. There are mountains of data…
Accuracy, Specificity, Precision, Recall, and F1 Score for Model Selection
You must have heard about the accuracy, specificity, precision, recall, and F score since they are used extensively to evaluate a machine learning model. You must have come across 2 specific types of errors called “type 1” and “type 2” errors. In this post, we will cover all these matrices one by one. To understand…
Min-Max Normalization
Min-max normalization is one of the most popular ways to normalize data. For every feature, the minimum value of that feature gets transformed into a 0, the maximum value gets transformed into a 1, and every other value gets transformed into a value between 0 and 1. It is calculated by the following formula: v’…
XGBoost
XGBoost stands for “Extreme Gradient Boosting”. XG Boost works only with the numeric variables. It is a part of the boosting technique in which the selection of the sample is done more intelligently to classify observations. This algorithm trains in a similar manner as GBT trains, except that it introduces a new method for constructing…
Gradient Boosting
In gradient boosting decision trees, we combine many weak learners to come up with one strong learner. The weak learners here are the individual decision trees. All the trees are connected in series and each tree tries to minimize the error of the previous tree. Due to this sequential connection, boosting algorithms are usually slow…
AdaBoost
AdaBoost is short for Adaptive Boosting.Generally, AdaBoost is used with short decision trees. What Makes AdaBoost Different ? AdaBoost is similar to Random Forests in the sense that the predictions are taken from many decision trees. However, there are three main differences that make AdaBoost unique: First, AdaBoost creates a forest of stumps rather than…
BOOSTING
The term ‘Boosting’ refers to a family of algorithms that converts weak learners to strong learners. Let’s understand this definition in detail by solving a problem: Let’s suppose that, given a data set of images containing images of cats and dogs, you were asked to build a model that can classify these images into two…