Anix LynchProanixblog.hashnode.dev·Nov 5, 2024Part 4: 10 Advanced Topics in ML and Optimization with Math Notation Friendly Explained1. 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 ...DiscussLDA
Anix LynchProanixblog.hashnode.dev·Nov 5, 2024Part 1: 11 Basic Machine Learning Techniques with Math Notation Friendly Explained1. 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...Discuss#Regression
Riya Boseblogbyriyabose.hashnode.dev·Sep 27, 2024Cracking the Code: Mastering Dimensionality Reduction Techniques in Machine LearningIntroduction 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...Discuss #DimensionalityReduction
Arbash Hussaincckeh.hashnode.dev·Sep 9, 2024A Step by Step Guide to Principal Component Analysis (PCA) in Machine LearningIntroduction 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...Discuss·99 readsMachine LearningDimensionality Reduction
Retzam Tarleretzam.hashnode.dev·Jul 22, 2024Hands-on with Unsupervised Learning modelsHello 🤗, 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)...Discussprincipal component analysis
Adeniran Emmanuelemmanueladeniran.hashnode.dev·Jul 18, 2024Guide to Principal Component Analysis in Data ScienceOrigin 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...Discuss·1 likePca
Suraj Karkisavvysuraj.hashnode.dev·Jun 28, 2024Anomaly Detection Using Linear ModelBefore 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 ...DiscussAnomaly DetectionMachine Learning
Kishar Nathkishar.hashnode.dev·Apr 15, 2024What is PCA in Machine learning?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...Discuss·4 likesMachine Learning
Aman .aman65823.hashnode.dev·Mar 18, 2024PCA-Principal Component AnalysisToday 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...DiscussPca
K Ahameddatailm.hashnode.dev·Jan 13, 2024Unraveling the Unseen: Revealing Hidden Patterns in Unlabeled DataUnsupervised 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 ...DiscussMachine LearningUnsupervised learning
