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Machine Learning Algorithms Cheat Sheet

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Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way people learn, progressively improving its accuracy. This way, Machine Learning is one of the most interesting methods in Computer Science these days, and it's being applied behind the scenes in products and services we consume in everyday life. In case you want to know what Machine Learning algorithms are used in different applications, or if you are a developer and you're looking for a method to use for a problem you are trying to solve, keep reading below and use these steps as a guide. Machine Learning can be divided into three different types of learning: Unsupervised Learning, Supervised Learning, and Semi-supervised Learning. Unsupervised learning uses information data that is not labeled, that way the machine should work with no guidance according to patterns, similarities, and differences. On the other hand, supervised learning has a presence of a "teacher", who is in charge of training the machine by labeling the data to work with. Next, the machine receives some examples that allow it to produce a correct outcome.


All About K-Means Clustering

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. "Clustering is an unsupervised machine learning technique which finds certain patterns/structures in the unlabeled data to segregate them into different groups, according to their properties."


Adding Explainability to Clustering - Analytics Vidhya

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Clustering is an unsupervised algorithm that is used for determining the intrinsic groups present in unlabelled data. For instance, a B2C business might be interested in finding segments in its customer base. Clustering is hence used commonly for different use-cases like customer segmentation, market segmentation, pattern recognition, search result clustering etc. Some standard clustering techniques are K-means, DBSCAN, Hierarchical clustering amongst other methods. Clusters created using techniques like Kmeans are often not easy to decipher because it is difficult to determine why a particular row of data is classified in a particular bucket.


Best Papers to Read on the Mean Shift Algorithm

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Abstract: Two important nonparametric approaches to clustering emerged in the 1970's: clustering by level sets or cluster tree as proposed by Hartigan, and clustering by gradient lines or gradient flow as proposed by Fukunaga and Hosteler. In a recent paper, we argue the thesis that these two approaches are fundamentally the same by showing that the gradient flow provides a way to move along the cluster tree. In making a stronger case, we are confronted with the fact the cluster tree does not define a partition of the entire support of the underlying density, while the gradient flow does. Abstract: Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region. Mean shift algorithms have been effectively used for data denoising, mode seeking, and finding the number of clusters in a dataset in an automated fashion.


K-Medoid Clustering (PAM)Algorithm in Python

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Clustering of large-scale data is key to implementing segmentation-based algorithms. Segmentation can include identifying customer groups to facilitate targeted marketing, identifying prescriber groups to allow health care players to reach out to them with the right messaging, and identifying patterns or abnormal values in the data. K-Means is the most popular clustering algorithm adopted across different problem areas, mostly owing to its computational efficiency and ease of understanding the algorithm. K-Means relies on identifying cluster centers from the data. It alternates between assigning points to these cluster centers using the Euclidean distance metric and recomputes the cluster centers till a convergence criterion is achieved.


Unsupervised Learning Algorithms in One Picture - DataScienceCentral.com

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Unsupervised learning algorithms are "unsupervised" because you let them run without direct supervision. You feed the data into the algorithm, and the algorithm figures out the patterns. The following picture shows the differences between three of the most popular unsupervised learning algorithms: Principal Component Analysis, k-Means clustering and Hierarchical clustering. The three are closely related, because data clustering is a type of data reduction; PCA can be viewed as a continuous counterpart of K-Means (see Ding & He, 2004).


5 Clustering Methods in Machine Learning

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In the beginning, let's have some common terminologies overview, A cluster is a group of objects that lie under the same class, or in other words, objects with similar properties are grouped in one cluster, and dissimilar objects are collected in another cluster. And, clustering is the process of classifying objects into a number of groups wherein each group, objects are very similar to each other than those objects in other groups. Simply, segmenting groups with similar properties/behaviour and assign them into clusters. Being an important analysis method in machine learning, clustering is used for identifying patterns and structure in labelled and unlabelled datasets. Clustering is exploratory data analysis techniques that can identify subgroups in data such that data points in each same subgroup (cluster) are very similar to each other and data points in separate clusters have different characteristics.


Day 30: 60 days of Data Science and Machine Learning Series

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This article explains what data engineers are and what their varied tasks and duties are. Seaborn is a very prominent library used during Exploratory Data Analysis of any data science project you are working upon. At times, this cohort could feel overwhelming due to the sheer volume of material I would need to learn and practice.


A Simple Guide to Machine Learning Visualisations

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The Yellowbrick library also contains a set of visualisation tools for analysing clustering algorithms. A common way to evaluate the performance of clustering models is with an intercluster distance map. The intercluster distance map plots an embedding of each cluster centre and visualises both the distance between the clusters and the relative size of each cluster based on membership. We can turn the diabetes dataset into a clustering problem by only using the features (X). Before we cluster the data we can use the popular elbow method to find the optimal number of clusters.


Hierarchical Clustering: Explain It To Me Like I'm 10

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This is part numero tres of the Explaining Machine Learning Algorithms to a 10-Year Old series. If you read the two previous ones about XGBoost Regression and K-Means Clustering, then you know the drill. We have a scary-sounding algorithm, so let's strip it of its scary bits and understand the simple intuition behind it. In the same vein as K-Means Clustering, today we are going to talk about another popular clustering algorithm -- Hierarchical Clustering. Let's say a clothing store has collected the ages of 9 of its customers, labeled C1-C9, and the amount each of them spent at the store in the last month.