1. Matrix Factorization (e.g., Singular Value Decomposition) Matrix Factorization is a technique used to decompose a matrix into simpler, constituent matrices, often revealing useful properties or patterns in the data. One of the most common methods ...
1. Linear Regression Linear Regression is one of the simplest techniques for predicting a continuous outcome by modeling the relationship between an independent variable and a dependent variable with a straight line. Key Concept:The goal of linear re...
Introduction In machine learning, we often work with datasets containing a large number of features or variables. While having more data might seem beneficial, high-dimensional datasets can lead to overfitting, increased computational costs, and redu...
Introduction Welcome back to the eighth blog post in our Machine Learning series! Today, we're diving into Principal Component Analysis (PCA), a powerful tool for dimensionality reduction. PCA simplifies complex datasets while keeping as much informa...
Hello 🤗, We'll continue where we left off and round up unsupervised learning in this chapter. We have extensively learned about unsupervised learning in the previous chapter, we learned about K-Means clustering and Principal Component Analysis (PCA)...
Origin of PCA Approach of PCA How to do PCA When is PCA used What PCA is and is not Advantages and Disadvantages of PCA Metric of Evaluation After PCA, what Next? Case Study of the Cocktail recipe Dataset Origin of PCA PCA was first develo...
Before I start, let's have some motivation: "Cry. Forgive. Learn. Move on. Let your tears water the seeds of your future happiness." Steve Maraboli This is the third lesson of the Anomaly Detection lecture series. In this lesson, we will see how ...
PCA is a dimensionality reduction technique we use in Data science. PCA is a unsupervised learning technique, meaning it does not rely on labeled data. It has several application like Image compression, Data visualization and Exploratory data analysi...
Today we learnt about the PCA What is PCA? Principal Component Analysis (PCA) in Machine Learning? Reducing the number of variables in a data collection while retaining as much information as feasible is the main goal of PCA. PCA can be mainly used f...
Unsupervised learning is a branch of machine learning that explores patterns and structures within data without the presence of labeled outputs. Unlike supervised learning, where the algorithm is provided with labeled training data to learn and make ...
