Linear Regression is a machine learning algorithm, which is based on the principle of supervised learning. So as we know we have two types of Supervised Learning tasks, Linear regression (as the name suggests), performs a regression task. Regression is used to understand the relationship between dependent and independent variables. It is commonly used to make projections, such asContinue reading “Simple Linear Regression: Supervised Learning Algorithm”

# Author Archives: Smriti Mishra

## What is Supervised Learning?

Supervised learning is a type of learning method in Machine Learning or Artificial Intelligence. In supervised learning, we use labeled datasets to train algorithms that to classify data or predict outcomes accurately. As we feed labelled input data into the model, it adjusts its weights and biases iteratively, which ensures that the model has beenContinue reading “What is Supervised Learning?”

## Wanna know how Skydiving feels?

Freedom. Breathlessness. Strong forces of wind. The feeling closest to flying and seeing the world with a whole new perspective. That’s how I would like to describe skydiving. To be honest, I was always jealous of birds because they can fly, and I can’t. But when I tried skydiving, it gave me that perspective IContinue reading “Wanna know how Skydiving feels?”

## Singular Value Decomposition (Unsupervised Algorithms)

Singular value decomposition is used to reduce a dataset containing a large number of values to a dataset with significantly fewer values. This reduced dataset will still contain a large fraction of the variability present in the original data. It is used to extract and untangle information, like PCA. Eigenvalues and Eigenvectors An eigenvector of an n × n matrix A is a nonzeroContinue reading “Singular Value Decomposition (Unsupervised Algorithms)”

## Lost in Stockholm

“I hear echoes of a thousand screams As I lay me down to sleep There’s a black hole deep inside of me Reminding me That I lost my backbone Somewhere in Stockholm I lost my backbone Somewhere in Stockholm” As these lyrics play in the background, I think of Stockholm and how I actually foundContinue reading “Lost in Stockholm”

## Relationship Between Mind and AI : Talk by Me

How can neuroscience benefit from AI? As we all know, brains are far too complex for us to understand at present. I read a book called “The Psychopath Inside” by James Fallon, where he explains the brain in terms of a 3*3 Rubik’s cube (it’s still so impossibly difficult to understand and visualise without priorContinue reading “Relationship Between Mind and AI : Talk by Me”

## Hierarchical Clustering (Unsupervised Learning algorithm)

Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. – You start with raw unlabelled data and the endpoint is a set of clusters. – Each cluster is different from the other cluster, and the objects within each cluster are similar to each other. How does itContinue reading “Hierarchical Clustering (Unsupervised Learning algorithm)”

## Principal Component Analysis (PCA) – Unsupervised Learning

PCA is one of famous techniqeus for dimension reduction, feature extraction, and data visualization.– PCA is a method that rotates the dataset in a way such that the rotated features are statistically uncorrelated. – This rotation is often followed by selecting only a subset of the new features, based on how useful the features are. – PCAContinue reading “Principal Component Analysis (PCA) – Unsupervised Learning”

## Hidden Markov Models (Unsupervised Learning Algorithms)

It is one of the more elaborate ML algorithms – a statical model that analyzes the features of data and groups it accordingly. The HMM is based on augmenting the Markov chain. – A Markov chain is a model that tells us something about the probabilities of sequences of random variables, states, each of which canContinue reading “Hidden Markov Models (Unsupervised Learning Algorithms)”

## Unsupervised Learning Algorithm : K-means Clustering

A centroid based clustering algorithm. The main aim of this algorithm is to minimise the sum of distances between the data point and their corresponding clusters. The input data is unlabelled, so the algorithm divides the data into n number of clusters iteratively until it creates the most optimised clusters. The algorithm primarily performs twoContinue reading “Unsupervised Learning Algorithm : K-means Clustering”