Indu Jawlacoders.hashnode.dev·Oct 14, 2024Understanding Machine Learning: Key Concepts and TechniquesMachine learning, a subfield of artificial intelligence, empowers computers to learn from data and make decisions without being explicitly programmed. It is typically categorized into two main types: supervised learning and unsupervised learning. Sup...DiscussMachine Learning
Arbash Hussaincckeh.hashnode.dev·Sep 20, 2024A Step by Step Guide to Linear Discriminant Analysis (LDA) in Machine LearningIntroduction Welcome to the ninth blog post in our Machine Learning series! Today, we'll explore Linear Discriminant Analysis (LDA), a powerful algorithm used for reducing dimensions and classification. By the end of this guide, you'll understand how...Discuss·113 readsMachine LearningMachine Learning
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
Anix LynchProanixblog.hashnode.dev·Aug 4, 2024Uncovering Trends with PCA, Clothing store case studyIn the fast-paced world of fashion retail, understanding your inventory and customer preferences can be overwhelming. With countless variables to consider—from size and price to popularity and style—how can store managers make sense of it all? Enter ...DiscussApplied machine learning
Anix LynchProanixblog.hashnode.dev·Aug 4, 2024Machine learning in e-commerceImagine you're a clothing store manager trying to understand customer preferences based on their purchase history. You have a large dataset of customer purchases, but it's too complex to analyze directly. Truncated SVD helps you simplify this data wh...DiscussMachine Learning
Retzam Tarleretzam.hashnode.dev·Jul 8, 2024Dimensionality Reduction - Unsupervised Learningprint("Dimensionality Reduction") Dimensionality reduction is a technique/model used in unsupervised learning to reduce the number of features (variables) in a dataset while retaining as much information as possible. A good example to illustrate dime...DiscussDimensionality Reduction
Retzam Tarleretzam.hashnode.dev·Jun 24, 2024Unsupervised Learningprint("Unsupervised Learning") Unsupervised Learning is a type of machine learning task that uses unlabeled input data to train a model. Unlike in supervised learning where the data is clearly labelled and we want to use the data (feature vectors) to...DiscussUnsupervised learning
Quantum Cyber Solutionsqcs.hashnode.dev·Jun 1, 2024Quantum Machine Learning: Revolutionizing Feature Extraction and Dimensionality ReductionPublished on Saturday, June 1, 2024 Quantum Machine Learning: Revolutionizing Feature Extraction and Dimensionality Reduction ============================================================================================= Authors Name Elon Tusk 😄 Tw...DiscussDimensionality Reduction
Quantum Cyber Solutionsqcs.hashnode.dev·Jun 1, 2024Quantum Machine Learning: Revolutionizing Feature Extraction and Dimensionality ReductionPublished on Saturday, June 1, 2024 Quantum Machine Learning: Revolutionizing Feature Extraction and Dimensionality Reduction ============================================================================================= Authors Name Elon Tusk 😄 Tw...DiscussDimensionality Reduction
Kunal Kumar Sahookunalkumarsahooai.hashnode.dev·Mar 9, 2024t-SNE: A Comprehensive GuideIntroduction In the ever-expanding domain of machine learning and data analysis, uncovering the the underlying structure of high-dimensional datasets is a daunting task. t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful dimensionality...Discuss·70 readst-sne
