Machine Learning

In this NumPy Normalization tutorial, we are going to learn how to normalize an array using the NumPy library of Python. But before we hop on to that, let us first try to understand the definition and meaning of NumPy and Normalization. Normalization  Generally, normalization is a process that is used to rescale the real […]

Machine learning is the concept of programming the machine in such a way that it learns from its experiences and different examples, without being programmed explicitly. It is an application of AI that allows machines to learn on their own. Machine learning algorithms are a combination of math and logic that adjust themselves to perform

One of the most fascinating problems in machine learning is determining the similarity between two data points. From signature verification and face recognition to recommendation systems, learning to compare and decide similarities is important. A powerful approach to this problem is Similarity Learning using Siamese Networks. Siamese Networks are a type of neural network architecture

Contributed by: Arun K Sharma Imagine a large law firm takes over a smaller law firm and tries to identify the documents corresponding to different types of cases such as civil or criminal cases which the smaller firm has dealt or is currently dealing with. The presumption is that the documents are not already classified

Linear discriminant analysis is supervised machine learning, the technique used to find a linear combination of features that separates two or more classes of objects or events.  Linear discriminant analysis, also known as LDA,  does the separation by computing the directions (“linear discriminants”) that represent the axis that enhances the separation between multiple classes. Also, Linear

It's easy for the human mind to distinguish between different types of fruits by noticing their color and shape. But what if you ask a machine what type of fruit is in an image? Ever wondered what the machine will answer? This is where the concept of multiclass classification is introduced. You can detect the

Bias and Variance  For the same set of data, different algorithms behave differently. For example, if we want to predict the price of houses given for some dataset, some of the algorithms that can be used are Linear Regression and Decision Tree Regressor. Both of these algorithms will interpret the dataset in different ways and