## Cost Functions

When you think about cost, what comes to your mind? Cost is the estimated price that we have to pay for a service. Likewise, a cost function measures the estimated tradeoff of the accuracy of a “cut” that’s taken by the model for predicting our desired values. A cost or a loss function is a…

## Everything you need to know about Model Fitting in Machine Learning

What is Model Fitting? Model fitting is a measure of how well a machine learning model generalizes to similar data to that on which it was trained. The generalization of a model to new data is ultimately what allows us to use machine learning algorithms every day to make predictions and classify data. The definition of a…

## Maximum likelihood estimation

Maximum Likelihood Estimation (MLE) is a technique used to estimate the parameters of a statistical model. But what are parameters? A parameter is a variable whose value can be estimated from historical data. For example, in the case of Linear regression (see our article on linear regression), the distribution is Y=mx+b, the parameters are m…

## Hidden Markov Models Algorithms

A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. We analyze the hidden Markov models to recover the sequence of states from the observed data. You can read more about it from our earlier…

## What are Convolutional Neural Networks?

Let’s say, you want to create a model that can differentiate if the animal in an image is a cat or a dog. We immediately think of a convolutional neural network, but why can’t we use a neural network? They both are pretty similar… They both make use of neurons as their basic functional unit…

## 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…

## Hidden Markov Model

According to L.R Rabiner et al[1], a hidden Markov model is a doubly embedded stochastic process with an underlying stochastic process that is not observable (it is hidden) but can only be observed through another set of stochastic processes that produce the sequence of observations. Basically, a hidden Markov model (HMM) is one in which you…

## 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…

## Kurtosis and Skewness

The most commonly found distribution in nature is the normal distribution, which has a bell-shaped curve. But we don’t need to get a perfect normal distribution every time. The data may have outliers that are going to distort this curve. The horizontal distortion of a normal distribution curve gets captured by the Skewness measure and…

## 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…