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#### Isomap Embedding — An Awesome Approach to Non-linear Dimensionality Reduction

Next in the series on Machine Learning algorithms a look at another dimensionality reduction technique known as Isometric Mapping or Isomap for short.

Read more at Towards Data Science#### What is Isomap?

We cannot visualize high-dimensional data above 3 dimensions. So what do we do when we are faced with this situation that is commonplace in nearly every Data Science application? Dimension reduction…

Read more at Towards Data Science#### Preserving Geodesic Distance for Non-Linear Datasets: ISOMAP

This article includes an interpretation of ISOMAP results, python implementation of ISOMAP, differences between geodesic distance and Euclidean distance, usage areas of ISOMAP. The role of the ISOMAP ...

Read more at Towards Data Science#### Decomposing Non-linearity with ISOMAP

Many applications of data science involves dealing with High-Dimensional data like images. With such amount of multivariate data rises an underlying problem of visualizing them. To do so, we usually…

Read more at Towards Data Science#### Manifold Learning [t-SNE, LLE, Isomap, +] Made Easy

Principal Component Analysis is a powerful method, but it often fails in that it assumes that the data can be modelled linearly. PCA expressed new features as linear combinations of existing ones by…

Read more at Towards Data Science#### Dimensionality Reduction with Scikit-Learn: PCA Theory and Implementation

In the novel Flatland , characters living in a two-dimensional world find themselves perplexed and unable to comprehend when they encounter a three-dimensional being. I use this analogy to illustrate ...

Read more at Towards Data Science#### Advanced Dimensionality Reduction Models Made Simple

When approaching a Machine Learning task, have you ever felt stunned by the massive number of features ? Most Data Scientists experience this overwhelming challenge on a daily basis. While adding feat...

Read more at Towards Data Science#### Understanding Classification Thresholds Using Isocurves

Your job as a data scientist isn’t done until you explain how to interpret the model and apply it. That means threshold selection for the business decision that motivated the model.

Read more at Towards Data Science#### A Guide to Dimensionality Reduction in Python

Dimensionality reduction is the process of transforming high-dimensional data into a lower dimensional format while preserving the most important properties. This technique has applications in many…

Read more at Towards Data Science#### 6.5. Unsupervised dimensionality reduction

If your number of features is high, it may be useful to reduce it with an unsupervised step prior to supervised steps. Many of the Unsupervised learning methods implement a transform method that ca......

Read more at Scikit-learn User Guide#### Unsupervised Regression for Dimensionality Reduction

Using regression to compute low-dimensional embeddings Image by Annie Spratt on Unsplash Every first-year student in data science and A.I. learns that regression is a supervised learning method. Orig...

Read more at Towards Data Science#### Building a k-Nearest Neighbors Classifier with Scikit-learn: A Step-by-Step Tutorial

Scikit-learn is a popular Python library for Machine Learning that provides tools for data analysis, data pre-processing, model selection… Continue reading on Level Up Coding

Read more at Level Up Coding#### Building a k-Nearest-Neighbors (k-NN) Model with Scikit-learn

k-Nearest-Neighbors (k-NN) is a supervised machine learning model. Supervised learning is when a model learns from data that is already labeled. A supervised learning model takes in a set of input…

Read more at Towards Data Science#### Dimensionality Reduction with Python

When building a machine learning model, most likely you will not use all the variables available in your training dataset. In fact, training datasets with hundreds or thousands of features are not…

Read more at Towards Data Science#### The Art of Dimensionality Reduction

Suppose you want to solve a predictive modeling problem, and for the same, you start to collect data. You would never know what exact features you want and how much data is needed. Hence, you go for…

Read more at Analytics Vidhya#### 1.6. Nearest Neighbors

sklearn.neighbors provides functionality for unsupervised and supervised neighbors-based learning methods. Unsupervised nearest neighbors is the foundation of many other learning methods, notably m......

Read more at Scikit-learn User Guide#### In-Depth: Manifold Learning

We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task—reducing the number of features of a dataset while maintaining the essential relationships between ...

Read more at Python Data Science Handbook#### The Ultimate Scikit-Learn Guide

Part 5: An introduction to spectral biclustering algorithm. Welcome back Machine Learning folks! Another week, another Scikit-Learn example to have a look at. In this episode, we are having a look at...

Read more at Python in Plain English#### Dimensionality Reduction For Dummies — Part 3: Connect The Dots

An intuitive solution to PCA using Eigenvalue Decomposition.

Read more at Towards Data Science#### Master Dimensionality Reduction with these 5 Must-Know Applications of Singular Value…

Singular Value Decomposition is a common dimensionality reduction technique. This article explores the applications of SVD and the different ways of implementing SVD in Python

Read more at Analytics Vidhya#### Isotonic Regression is THE Coolest Machine-Learning Model You Might Not Have Heard Of

The term “ Isotonic” originates from the Greek root words “ iso” and “ tonos.” The root “ iso” isn’t just a file format, it actually means equal. “ Tonos,” on the other hand, means to stretch. The…

Read more at Towards Data Science#### A Complete Guide On Dimensionality Reduction

Do you know, the interest users are generating 2.5 quintillion bytes of data per day? well the data can be from anywhere. Suppose the data collected from Internet based applications are generating…

Read more at Analytics Vidhya#### Euclidean and Manhattan distance metrics in Machine Learning.

Many of the Supervised and Unsupervised machine learning models such as K-Nearest Neighbor and K-Means depend upon the distance between two data points to predict the output. Therefore, the metric we…...

Read more at Analytics Vidhya#### Let’s learn about Dimensionality Reduction

For example:- We have data in spreadsheet format and we have vast amounts of variables (age, name, sex, Id, and so on..). In a simple way “The number of input variables or features for a dataset is…

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