smriti-mishra

Machine learning and statistics are like Chandler and Joey, best combination – you see? So today let’s talk about another supervised learning algorithm which is absolutely based on statistics. Enter Logistic Regression. It is the go-to method for binary classification problems (problems with two class values). Hence it is used when the target is categorical.…

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 as…

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 been…

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 nonzero…

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 prior…

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 it…

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. – PCA…