unsupervised-learning

Customer segmentation is a machine learning application that involves grouping customers based on similarities in their behavior. This unsupervised learning technique helps companies create customer groups for targeted marketing. One way to group customers is through hierarchical clustering, which can be visualized using dendrograms. There are two types of hierarchical clustering: agglomerative (bottom-up) and divisive (top-down). In this article, we will demonstrate how to implement hierarchical clustering using Python.

Learn to build a music recommendation system using the k-means algorithm. We will use the audio features from the million song data and cluster them based on their similarities. In this blog, we will be discussing these topics: 1) Methods to build a recommendation system for songs 2) Step-wise implementation 3) Ordering songs for the recommendation, etc.

Image data is bulky in nature and requires high bandwidth in sharing. Hence, here we will develop a way to convert the bulky image into memory efficient data using an unsupervised learning technique PCA (Principal Component Analysis). We will be discussing Image types and image quantization, Step-wise implementation and code explanation for image compression using PCA, and Techniques to optimize the tradeoff between compression and the number of components to retain in an image.

Clustering is a powerful unsupervised learning technique that involves grouping similar data points together into subgroups or clusters. One of the most widely used clustering algorithms in machine learning is the k-means algorithm, which separates data into k distinct clusters based on pre-defined criteria. In this article, we provide a detailed, step-by-step explanation of how k-means works, and explore popular methods like the elbow method and average silhouette method for determining the optimal value of k in k-means. To illustrate our points, we also demonstrate how to implement k-means on the IRIS dataset using Python.

Principle component analysis (PCA) is an unsupervised learning technique to reduce data dimensionality consisting of interrelated attributes. The PCA algorithm transforms data attributes into a newer set of attributes called principal components (PCs). In this blog, we will discuss the dimensionality reduction method and steps to implement the PCA algorithm.

Machine learning can predict personalities based on social media usage. This is highly used in dating apps and recommendation systems. In this blog, we have discussed: 1) How personality prediction is useful? 2) Big five personality trait model 3) How ML predicts personality based on social media behavior? 4) Steps to implement personality predictor.

Based on the nature of input that we provide to a machine learning algorithm, machine learning can be classified into four major categories: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning. In this blog, we have discussed each of these terms, their relation, and popular real-life applications.