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Introduction to Hierarchical Clustering Unsupervised learning  is a type of Machine learning in which we use unlabeled data and we try to find a pattern among the data. Clustering algorithms  falls under the category of unsupervised learning. In these algorithms, we try to make different clusters among the data. Hierarchical Clustering  algorithms build a hierarchy of clusters where each node is a cluster consisting of the clusters of its children node. fig. 1 Check out my blog on Hierarchical Clustering - An Unsupervised learning Algorithm  to learn more about it. Implementing it with Python and SciKit-Learn We will use the python programming language for its implementation. In python language, we will be using SciKit-Learn library. So let's start with implementation - We will be using our own dataset which will be generated by us.     1.  Importing necessary Libraries - To create a machine learning model we will use SciKit-Learn (sklearn) librar...

Introduction Unsupervised learning  is a type of Machine learning in which we use unlabeled data and we try to find a pattern among the data. Clustering algorithms falls under the category of unsupervised learning. In these algorithms, we try to make different clusters among the data. Hierarchical Clustering algorithms build a hierarchy of clusters where each node is a cluster consisting of the clusters of its children node. To check it's implementation in Python  CLICK HERE There are various strategies in Hierarchical Clustering such as : Divisive Agglomerative This type of diagram is called  Dendrogram. Divisive - It is a Top-down approach. So we start with all observations in a large cluster and break it down into smaller ones. Agglomerative - It is the opposite of Divisive as it is a Bottom-Up approach. Here, each observation starts in its cluster and pairs of the cluster are merged as they move up the hierarchy.  (Generally, Agglomerative is used more as compa...

What is Pose Estimation? Pose Estimation is one of the recent applications of Deep Learning and Computer Vision. In pose estimation we try to predict the pose of a person, it means representing an orientation of a person in graphical format. While reading out the intro it might look a bit complex but actually, it is not. We implement the idea by considering each joint of our body as a coordinate for our graphical representation. The bones which connect two joints are shown as a simple line. To make a posture simple and easy to visualize we do not represent each and every bone of a human body. Instead, we represent Hands, Legs, Shoulder and Face of a person. Background Previously, one of the famous application of computer vision was face detection and face recognition. We also used to apply computer vision for detecting whether a person is present in an image or not. If the presence is detected then we can highlight the area where it is present. So, after making some advancements and a ...