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.
In this blog, we will build an image data compressor using unsupervised learning technique, Process Component Analysis. We will be discussing these topics: 1) Image types and quantization 2) PCA overview 3) Step-wise implementation of PCA for image compression. 4) Techniques to optimize the tradeoff between compression and the number of components.
The clustering technique is prevalent in many fields, so many algorithms exist to perform it. K-means is one of them! K-means is an unsupervised learning technique used to partition the data into predefined K distinct and non-overlapping partitions. These partitions are called clusters, and the value of K depends upon the user's choice.
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.
Customer segmentation in machine learning is about splitting organization's customer base into smaller groups that reflect similarities in their behavior. It helps businesses develop customer-focused strategies, make segment-wise decisions, and maximize customer value. This blog explains cluster analysis for customer segmentation.
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.
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