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WHAT IS CLUSTERING?
It is basically a type of unsupervised learning method. An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labeled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, genera... | ['Machine Learning', 'Pyhton', 'Statistics', 'Unsupervised Learning', 'Artificial Intelligence'] |
Looking for a biologically more plausible way to train a neural network.
Traditionally, artificial neural networks have been trained using the Delta rule and backpropagation. But this contradicts the findings that the neurosciences have made on the function of the brain. There simply is no gradient error signal that i... | ['Machine Learning', 'Information Gain', 'Neural Networks', 'Unsupervised Learning', 'Recurrent Neural Network'] |
Is Skynet really the future of Artificial Intelligence?
Remember how Skynet, the artificial intelligence software developed to command the defense system in the movie Terminator?
Well, Skynet ended up gaining consciousness and betrayed humanity. The crazy part isn’t about Skynet obtaining consciousness, it’s about ho... | ['Machine Learning', 'Supervised Learning', 'Reinforcement Learning', 'Unsupervised Learning', 'Artificial Intelligence'] |
Mel-frequency cepstral coefficients (MFCC), zero-crossing rate are some of classical feature for audio. It can be extracted via the library easily. However, it may not able to provide a high-quality signal or input for deep learning models nowadays.
Two teams of researchers propose a different way to learn audio embed... | ['Machine Learning', 'Data Science', 'Unsupervised Learning', 'Audio Engineering', 'Artificial Intelligence'] |
Experiments
Now that we have seen the core spirit of semi supervised learning, where we first exploit unsupervised training without labels, and then train our classifier on top of our feature extractor with all or part of the labels of our samples, we will get our hands dirty with some experiments.
As MNIST has becom... | ['Machine Learning', 'Vae', 'Fastai', 'Variational Autoencoder', 'Unsupervised Learning'] |
1. The image
The first step is to get an image. I’ve used this one:
Photo by Daniela Cuevas on Unsplash
The great advantage of this algorithm is that, as you will see, it is pretty robust and it gives satisfying output quite independently from the input image, so you can choose your own photo.
2. The libraries
Let... | ['Machine Learning', 'Image Processing', 'Unsupervised Learning', 'Python', 'Artificial Intelligence'] |
Face Recognition using Machine Learning. Image Source: Pixabay
Alexa, the virtual assistant from Amazon. Watson, the IBM computer that plays Jeopardy.
Self Driving Cars from Tesla. Google maps software that can predict congestion.
Online recommendation engines that suggest to the user what to browse or buy.
Advance... | ['What Is Machine Learning', 'Supervised Learning', 'Unsupervised Learning', 'Machine Learning And Ai', 'Machine Learning Models'] |
Content Contributors: Shray Khanna, Honghui Wang, and Chhavi Verma
Warming Up
K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). Wait, what?
So, basically, k-means helps you to form groups in the unlabeled data. These g... | ['Machine Learning', 'Big Data', 'K Means Clustering', 'Anomaly Detection', 'Unsupervised Learning'] |
找出的特徵向量如同 PCA 等降維方法一般，但此對應著最佳劃分圖的方法。最終需要再將連續的特徵向量離散化，即為特徵的劃分，進而得到欲達成的分群。
－ 演算法 －
輸入：資料集 V ，以及分群數目 k
輸出：目標分群集合 Clusters
求出資料點的鄰接矩陣 A（Affinity matrix）。 對鄰接矩陣 A 的第 i 列加總作為對角矩陣 D 第（i, i）的元素值，並算出矩陣拉普拉斯矩陣 L 。 從矩陣 L 中找出對應前 l 個特徵值的特徵向量 x_1 , x_2 , … , x_l ，並建構 n x l 維的矩陣 X = [ x_1 | x_2 | … | x_l ]。 對矩陣 X 的每一列做正規化（R → [0 ,... | ['Clustering', 'Machine Learning', 'Spectral Clustering', 'Clustering Algorithm', 'Unsupervised Learning'] |
Machine Learning in Plain Language
Much like living beings, machines can also learn to do things. In this case the machine is the student and the world or another being — either natural or artificial — is the teacher.
According to Tom Mitchell’s influential 1997 book, Machine Learning, there are three major parts of ... | ['Machine Learning', 'Supervised Learning', 'Reinforcement Learning', 'Unsupervised Learning', 'Artificial Intelligence'] |
picture credits to www.oberlo.com
In this project, I want to practice Natural Language Processing (NLP) and Unsupervised Machine Learning. After some research on what dataset I could obtain from the web, I found a women clothings dataset of a real e-commerce business. I think it could be cool and useful to a business ... | ['Machine Learning', 'E Commerce Business', 'Topic Modeling', 'Unsupervised Learning', 'Data Science'] |
Picture from Unsplash
Introduction
The goal of this project is to find out similarities within groups of people in order to build a movie recommending system for users. We are going to analyze a dataset from Netflix database to explore the characteristics that people share in movies’ taste, based on how they rate the... | ['Clustering', 'Machine Learning', 'K Means', 'Unsupervised Learning', 'Data Science'] |
Decision Tree Algorithm - Edureka
With the increase in the implementation of Machine Learning algorithms for solving industry-level problems, the demand for more complex and iterative algorithms has become a need. The Decision Tree Algorithm is one such algorithm that is used to solve both Regression and Classificatio... | ['Machine Learning', 'Supervised Learning', 'Ml Algorithm', 'Decision Tree', 'Unsupervised Learning'] |
Microsoft Introduction to AI — Part 1
Are you a bit like me and have wanted to learn about Artificial Intelligence although felt a little intimidated by the maths involved? Maybe you thought the concepts were too difficult to understand and you would be out of your depth. I recently completed the Microsoft Introductio... | ['Machine Learning', 'Microsoft', 'Supervised Learning', 'Unsupervised Learning', 'Artificial Intelligence'] |
Introduction to Unsupervised Learning
Up to know, we have only explored supervised Machine Learning algorithms and techniques to develop models where the data had labels previously known. In other words, our data had some target variables with specific values that we used to train our models.
However, when dealing wi... | ['Clustering', 'Machine Learning', 'K Means', 'Unsupervised Learning', 'Data Science'] |
NLP is a field of Artificial Intelligence in which we try to process human language as text or speech to make computers similar to humans. Humans have a large amount of data written in a very unorganized format. So, it’s difficult for any machine to find meaning from raw text.
To make a machine learn from the raw text... | ['Machine Learning', 'Skip Gram', 'NLP', 'Unsupervised Learning', 'Word2vec'] |
Image segmentation is an important step in many medical imaging applications. The general approach is to use supervised learning to train a deep learning model. However, deep learning models require large amounts of data, and manually annotating medical images is a challenging task for multiple reasons: it requires a m... | ['Unsupervised Learning', 'Deep Learning', 'Medical Imaging'] |
Arthur Samuel, an expert in the field of Artificial Intelligence and Computer gaming coined the term “Machine Learning” and defined it as “Field of study that gives computers the capability to learn without being explicitly programmed.”
(Arthur Samuel)
Machine learning is a subset of Artificial intelligence. It analy... | ['Unsupervised Learning', 'Machine Learning', 'Supervised Learning', 'Reinforcement Learning'] |
Understanding Contrastive Learning
Photo by Raquel Martínez on Unsplash
What is Contrastive Learning?
Contrastive learning is a machine learning technique used to learn the general features of a dataset without labels by teaching the model which data points are similar or different.
Let’s begin with a simplistic ex... | ['Machine Learning', 'Deep Learning', 'Semi Supervised Learning', 'Unsupervised Learning', 'Data Science'] |
Let’s calculate the covariance matrix for our data set:
array([[ 1.01010101, 0.97727597, -0.01747925, -0.04152546],
[ 0.97727597, 1.01010101, -0.03990115, -0.06169213],
[-0.01747925, -0.03990115, 1.01010101, 0.87546278],
[-0.04152546, -0.06169213, 0.87546278, 1.01010101]])
3. Eigen Decomposition.
· Eigenvectors ➞... | ['Principal Component', 'Pca', 'Visualization', 'Unsupervised Learning', 'Dimensionality Reduction'] |
Finding and characterizing data chaos with unsupervised learning
To illustrate how this works in practice, let’s introduce an anomaly into this dataset. We will use our command-line client to trigger one of our chaos operations: TimeColumnZero . This introduces zero values into a column at a specific point in time.
C... | ['Machine Learning', 'Data Quality', 'Anomaly Detection', 'Unsupervised Learning', 'Data Science'] |
Introduction to The Structural Topic Model (STM)
Photo by Malin Strandvall on Unsplash
One of the coolest things about topic modelling is that it has applications in a variety of areas. It can help motivate inquiry, provide unique insights into texts, and give you new ways to organize documents.
The Structural Topic... | ['NLP', 'Topic Modeling', 'Unsupervised Learning'] |
Applications of NLP
Using keyword extraction for unsupervised text classification in NLP
Photo by Markus Spiske on Unsplash
Text classification is a common task in Natural Language Processing. The main approach tends toward representing the text in a meaningful way — whether through TF-IDF, Word2Vec, or more advance... | ['Machine Learning', 'NLP', 'Keyword Extraction', 'Text Classification', 'Unsupervised Learning'] |
Let us code!
About the dataset : It contains 217 columns of hobbies, where 1 means yes.
So, first step will be to import all the necessary libraries.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
Next step will be ... | ['Machine Learning', 'Data Visualization', 'Clustering Algorithm', 'Unsupervised Learning', 'Python'] |
“Isolation Forest” is a brilliant algorithm for anomaly detection born in 2009 (here is the original paper). It has since become very popular: it is also implemented in Scikit-learn (see the documentation).
In this article, we will appreciate the beauty in the intuition behind this algorithm and understand how exactly... | ['Machine Learning', 'Isolation Forests', 'Anomaly Detection', 'Unsupervised Learning', 'Data Science'] |
Copula-Based Outlier Detection — COPOD — is a new algorithm for anomaly detection. It has been implemented in the PyOD python package.
It has several key features that make it stand out from competing algorithms:
Deterministic
No hyperparameters! (This is important because is difficult to tune hyperparameters for ou... | ['Machine Learning', 'Outlier Detection', 'Anomaly Detection', 'Unsupervised Learning', 'Artificial Intelligence'] |
How to Use Unsupervised Clustering on Well Log Data with Python
Application of unsupervised cluster analysis on well log data to identify lithofacies (Image by Author)
Understanding the subsurface lithology is an important task in geoscience and petrophysics. Using a variety of electrical measurements generated from ... | ['Machine Learning', 'Well Logs', 'Petrophysics', 'Cluster Analysis', 'Unsupervised Learning'] |
Framingham Heart Study
Today, I’m going to go through an example of how to use TabNet for a classification task. The dataset contains results from the Framingham Heart Study, which is a study that began in 1948 and has provided (and is still providing) significant insights into risk factors for cardiovascular disease.... | ['Deep Learning', 'Classification', 'Structured Data', 'Neural Networks', 'Unsupervised Learning'] |
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition Book: https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
Photo by Braden Collum on Unsplash
No implement from scratch, only using frameworks
Learning: Intuitive => Project based => Less Theory => Even be read w... | ['Machine Learning', 'Supervised Learning', 'Unsupervised Learning', 'Regression', 'Artificial Intelligence'] |
Reinforcement Learning vs. Machine Learning vs. Deep Learning
Reinforcement learning is a branch of machine learning.
Unlike unsupervised and supervised machine learning, reinforcement learning does not rely on a static dataset, but operates in a dynamic environment and learns from collected experiences.
Data points... | ['Machine Learning', 'Autonomous Vehicles', 'Supervised Learning', 'Unsupervised Learning', 'Python3'] |
Image courtesy of Lawtomated; edit by Ani Aggarwal
For Unsupervised Learning
Author: Dhanin Wongpanich
Disinformation is quickly becoming one of the largest problems we face in this decade, playing a critical role in everything from political elections to the spread of the Covid-19 pandemic. In our fight against thi... | ['Supervised Learning', 'Social Media', 'Fake News', 'AI', 'Unsupervised Learning'] |
“Generative Models vs Discriminative Models”
On 28th February 2019 at Buildo, Data Science Milan has organized a meetup about Generative Models topic and how to use them beyond pictures.
“GANs beyond nice pictures: real value of data generation”, by Alexandr Honchar, AI Consultant
Let’s start with this question, wha... | ['Machine Learning', 'Variational Autoencoders', 'Generative Adversarial', 'Unsupervised Learning', 'Generative Model'] |
“A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.” ~Dave Waters
We are living in a world full of Humans and Machines. We the humans are learning and evolving from our past experiences for billions of years, on the other hand, the era of machines and ro... | ['Machine Learning', 'Supervised Learning', 'Reinforcement Learning', 'Unsupervised Learning', 'Data Science'] |
Backstory and Project Goal
Because of the rapid increase in scientific literature around COVID19, it is hard to keep up with the newest publications. Scientists and researchers are overwhelmed by the volume and struggle to find articles that are relevant to their work. In addition, the limitations of scientific confer... | ['Metis', 'NLP', 'Covid 19', 'Unsupervised Learning', 'Naturallanguageprocessing'] |
K-Means Clustering Project: Banknote Authentication
Using Python (Pandas, NumPy) to gather and assess the data and scikit-learn to train a K-Means model to detect if a banknote is genuine or forged. John Chen (Yueh-Han) Jun 1·5 min read
Have you ever been in a situation where you were handing money to the clerks at a... | ['Machine Learning', 'Data', 'Unsupervised Learning', 'Data Mining', 'Data Science'] |
A New Food Warehouse in Leuven (Business Recommendation)
City of Leuven
1. Introduction: Business Problem
Leuven is the capital of the province of Flemish Brabant in Belgium. It is located about 25 kilometers east of Brussels. The municipality itself comprises the historic city and the former neighboring municipalit... | ['Clustering', 'K Means', 'Unsupervised Learning', 'Leuven', 'Data Science'] |
Cluster Analysis is a multivariate statistical technique that groups observations based on some of their features or variables they are described by, such that:
Examples within a cluster are similar (in this case, we speak of high intraclass similarity).
are similar (in this case, we speak of high intraclass similari... | ['Clustering', 'Machine Learning', 'Python', 'Unsupervised Learning', 'Data Science'] |
Clustering comes from the set of unsupervised learning algorithms. The core idea here is to group (partition) the unlabelled data points based on their features, such that data items in one group would have similar properties. Being acquainted with the faintest machine learning concepts, one would know the idea of feat... | ['Machine Learning', 'K Means', 'Clustering Algorithm', 'Unsupervised Learning', 'Data Science'] |
Mall Customers Segmentation — Using Machine Learning
The use of machine learning can be seen almost everywhere around us, be it Facebook recognizing you or your friends, or YouTube recommending you a video or two based on your history — Machine Learning is everywhere!
However, the ‘magic’ of machine learning is not j... | ['Machine Learning', 'Clustering Algorithm', 'Business Analysis', 'Data Analysis', 'Unsupervised Learning'] |
Segmentation in marketing is a technique used to divide customers or other entities into groups based on attributes such as behaviour or demographics. It is useful to identify segments of customers who may respond in a similar way to specific marketing techniques such as email subject lines or display advertisements. A... | ['Clustering', 'Machine Learning', 'Towards Data Science', 'Unsupervised Learning', 'Artificial Intelligence'] |
Recently I came across a very interesting idea during my research for a project at work. I am currently dealing with a large dataset of images which has been captured when the light exits a laser. The goal is to understand the data better so that the data can be leveraged to provide insights for servicing these laser d... | ['Clustering', 'Machine Learning', 'Deep Learning', 'Unsupervised Learning', 'Deep Embedded Clustering'] |
Photo by Charisse Kenion on Unsplash
Machine learning is about making computers doing tasks without being explicitly programmed. We want computers to learn and use this knowledge to some situations.
There are different ways to do machine learning. With supervised machine learning, a computer learns the past to predic... | ['Clustering', 'Machine Learning', 'K Means', 'Unsupervised Learning'] |
A gentle Introduction to Customer Segmentation
Photo by Nick Karvounis on Unsplash
Overview
The aim of this post of mine is to show that you do not always need super complex and sophisticated machine learning models to get meaningful insights from your data.
For this mini-project I am using the popular K-Means clus... | ['Machine Learning', 'Customer Segmentation', 'K Means Clustering', 'Unsupervised Learning', 'Marketing Strategies'] |
In this installment of the series titled Learning Machine Learning, we will move on to the next class of machine learning techniques: non-supervised learning. If you would like to read my previous take on supervised learning, you can find it here.
Characters exploring their universe without supervision
Contemporary C... | ['Unsupervised Learning', 'Machine Learning', 'Artificial Intelligence', 'Technology'] |
When we are working with huge volumes of data, it makes sense to partition the data into logical groups and doing the analysis. We can use Clustering to make the data into groups with the help of several algorithms like K-Means.
In this article, I will try to address
a. Clustering
b. K-Means and working of the algor... | ['Clustering', 'Machine Learning', 'Mathematics', 'Unsupervised Learning', 'Data Science'] |
Posted by Julien Valentin and Sofien Bouaziz
Github repository: https://github.com/tensorflow/graphics
The last few years have seen a rise in novel differentiable graphics layers which can be inserted in neural network architectures. From spatial transformers to differentiable graphics renderers, these new layers lev... | ['Machine Learning', 'Announcements', 'Graphics', '3d', 'Unsupervised Learning'] |
We are using the minute weather dataset from Kaggle which contains weather-related measurements like air pressure, maximum wind speed, relative humidity etc. The data was captured in San Diego, over a three-year period from Sept 2011 to Sept 2014 and contains raw sensor measurements captured at one-minute intervals.
T... | ['Machine Learning', 'K Means', 'K Means Clustering', 'Scikit Learn', 'Unsupervised Learning'] |
Restaurant Location Recommender (Using K-Means)
Finding suitable neighbourhoods for opening an Indian Restaurant in the capital city of India Kumar Shaswat Jan 22, 2020·8 min read
Photo by Marco Chilese on Unsplash
In ths project will try to find an optimal location for a restaurant. Specifically, this report will b... | ['K Means', 'K Means Clustering', 'Unsupervised Learning', 'Python', 'Data Science'] |
Data Science from the Ground Up
Unsupervised Learning: Clustering Algorithms
Predictive models generally require what is called ‘labeled’ data to train on- that is that the data has some target variable that you have filled out already. Of course, the goal is to use the model on unseen data where you don’t know the v... | ['Clustering', 'Machine Learning', 'K Means', 'Data Science Ground Up', 'Unsupervised Learning'] |
Build Your Own K-Means Clustering Algorithm
Humans have an innate desire to create; to mold their surroundings as they will. This desire can manifest in a child turning a pile of multicolored legos into a spaceship or a woodworker turning a pile of wood into a canoe. Such acts of creating from scratch can lead to a se... | ['Machine Learning', 'K Means', 'Algorithms', 'Unsupervised Learning', 'Artificial Intelligence'] |
The rest of this post is organized as follows. First, we will explain the whole datasets and the organization of the training/validation/testing dataset. Next, we will brief the concept of autoencoder and the idea about applying it to anomaly detection. Finally, we will walk through the complete process of our solution... | ['Deep Learning', 'Anomaly Detection', 'Mnist', 'Unsupervised Learning', 'Autoencoder'] |
Understanding the Dataset
We will use the famous shopping mall dataset (refer data here )— https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python.
Let’s say you have data for 200 customers regarding their gender, age, income, spending score. Spending score is a proxy metric here used on a scale ... | ['Clustering', 'Machine Learning', 'Marketing Analytics', 'Customer Segmentation', 'Unsupervised Learning'] |
Contextualized representations
Deep contextualized representations have improved performance on an enormous range of NLP tasks, including word sense disambiguation.¹ ELMo was specifically designed to capture polysemy,² and BERT uses bidirectional training to learn context.³ Because BERT and ELMo learn contextualized r... | ['NLP', 'Deep Learning', 'Clustering', 'Unsupervised Learning'] |
A neural network is a progression of calculations that tries to perceive fundamental connections in a lot of information through a procedure that imitates the way the human brain works. In this sense, neural network mentions to frameworks of neurons, either natural or artificial in nature. Neural systems can adjust to ... | ['Artificial Neural Network', 'Supervised Learning', 'Neural Networks', 'Reinforcement Learning', 'Unsupervised Learning'] |
I. Introduction
A. Description & Discussion of the Background
London is a megacity, the capital of the United Kingdom, and one of the oldest of the world’s great cities, with its history spanning nearly two millennia. By far Britain’s largest metropolis, it is also the country’s economic, transportation, and cultural... | ['Machine Learning', 'K Means', 'Unsupervised Learning', 'Data Science', 'Udacity Nanodegree'] |
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Now when we... | ['Types Of Machine Learning', 'Supervised Learning', 'Unsupervised Learning', 'Reinforcement Learning'] |
Project Overview and Problem Statement
Identifying customer segments is an important and popular application in business and data science, in which being able to identify possible consumers can help corporates use their resources more effectively when making marketing decisions.
The goal of this project is to identif... | ['Unsupervised Learning', 'Machine Learning', 'Data Science', 'Customer Segmentation'] |
Centroid Neural Network for Clustering with Numpy
Photo by Alex Padurariu on Unsplash
Centroid neural network (CentNN) is an efficient and stable clustering algorithm that has been successfully applied to numerous problems. CentNN does not require a pre-determined learning coefficient but still yields competitive clu... | ['Clustering', 'Machine Learning', 'Centroid Neural Network', 'Clustering Algorithm', 'Unsupervised Learning'] |
The term Artificial Intelligence was coined for the first time way back in 1956 by John Mcarthy. But it took us nearly 60 years to experience AI in daily chores. Generative AI is a branch of AI that creates new data instances from data it is trained on and gives real-life data instances. The 3d Avatars or Memojis are u... | ['Machine Learning', 'Generative Adversarial', 'Neural Networks', 'Unsupervised Learning', 'Artificial Intelligence'] |
You’ve heard the concepts of deep learning vs machine learning lately but don’t know or understand what they are? So let’s try to learn and understand what these concepts are all together!
After reading this article about deep learning vs. machine learning, I am sure that these concepts will be shaped in your mind muc... | ['Machine Learning', 'Deep Learning', 'Artificial Neural Network', 'Supervised Learning', 'Unsupervised Learning'] |
Introduction
The Expectation-Maximization Algorithm, or EM algorithm for short, is an approach for maximum likelihood estimation in the presence of latent variables. The EM algorithm was explained and given its name in a classic 1977 paper by A. Dempster and D. Rubin in the Journal of the Royal Statistical Society.
F... | ['Machine Learning', 'Coding', 'Gaussian Mixture Model', 'Unsupervised Learning', 'Data Science'] |
5- The Machine Learning pipeline
Once we are dealing with an unsupervised learning problem, it is extremely important to well dimension two points:
Which train of thought we are trying to reproduce. How we will assess if the clusters are good or not.
The answer to those two questions remains on the reproducibility o... | ['Clustering', 'Machine Learning', 'Travel', 'Unsupervised Learning', 'Data Science'] |
Machine Learning is a subset of artificial intelligence that primarily focused on their experience and making predictions based on it. The main concept behind this is the study of how to build applications that exhibit this iterative improvement.
There are many ways to frame this idea, but mainly there are three major... | ['Machine Learning', 'Supervised Learning', 'Category', 'Reinforcement Learning', 'Unsupervised Learning'] |
Centroid Neural Network: An Efficient and Stable Clustering Algorithm
Generally, clustering is grouping multi-dimensional datasets into closely related groups. Classical representatives of clustering algorithms are K-means Clustering and Self-Organizing Map (SOM). You can easily find numerous resources for those algor... | ['Clustering', 'Machine Learning', 'Neural Network Algorithm', 'Neural Networks', 'Unsupervised Learning'] |
Unsupervised Learning: Hierarchical Clustering and DBSCAN
Source: Google Images
There are lots of methods to group our data points in machine learning for further analysis based on similarity. As a data scientist or a data analyst, we know this method called clustering. The most common clustering algorithm that used ... | ['Clustering', 'Hierarchical Clustering', 'Customer Analytics', 'Unsupervised Learning', 'Dbscan'] |
Machine Learning is one of the fastest-growing fields in today’s era. This article will give you an overview of what it is? How it is different from Artificial Intelligence(AI)? And what are its different types?
Photo by Luke Peters on Unsplash
What is Machine Learning?
Just like humans learn from their past experie... | ['Machine Learning', 'Supervised Learning', 'Reinforcement Learning', 'Unsupervised Learning', 'Artificial Intelligence'] |
by Ashish Adhikari
There are different algorithms in Machine used for different types of datasets. To solve a specific problem it is important to identify the problem and choose a suitable mode of learning of the problem.
To select the type of machine learning we must understand the data types by analyzing them. We h... | ['Machine Learning', 'Reinforced Learning', 'Supervised Learning', 'Unsupervised Learning', 'Artificial Intelligence'] |
Let’s quickly compare K-MEANS (Partitioning) vs DBSCAN (Density), by running them on a popular dataset called “multishapes” (embedded in the R-package factoextra) which looks like
We “visually” detect 5 distinct clusters + a set of loose points around (noise). We can validate this number of clusters displaying the clu... | ['Data Visualization', 'Unsupervised Learning', 'Python', 'Data Science', 'Spatial Analysis'] |
The illustration is taken from www.pexels.com
When we are in college, our teacher guides us and explains the information with examples. There is a sense of supervision involved when the teacher is explaining. The target information i.e. the answers are available.
A similar process occurs with supervised machine learn... | ['Machine Learning', 'Supervised Learning', 'Data Science Training', 'Unsupervised Learning', 'Data Science'] |
According to Arthur Samuel (1959),
It is a field of study that gives computer ability to learn without being explicitly programmed.
But, how is a computer supposed to learn? Tom Mitchell says that
A computer program is said to learn from experience E with respect to task T and some performance measure P, if its perf... | ['Machine Learning', 'Supervised Learning', 'Reinforcement Learning', 'Unsupervised Learning', 'Technical'] |
Customer Segmentation Part
This part contains data understanding, preprocessing of demographics of general population data(cleaning, feature scaling and building proper cleaning functions so that used for any similar dataset) and make them ready for fitting into model. Let’s look into the datasets and features informa... | ['Unsupervised Learning', 'Machine Learning', 'Data Science', 'Clustering'] |
In this article I’m going to explain and trying to implement an approach proposed in A Novel Ensemble Learning Approach to Unsupervised Record Linkage Anna Jurek, Jun Hong, Yuan Chi, Weiru Liu paper for using semi supervised learning in matching databases with automatic seed selection.
Record linkage is a process of i... | ['Unsupervised Learning', 'Machine Learning', 'Semi Supervised Learning', 'Record Linkage'] |
Thus far, we’ve covered a very simplistic example; however, auto-encoders in practice are not far off in intuition. Recall that the blue and green arrows are simply functions that convert a large set of features into a smaller set of features and vice versa. Since neural networks are essentially complicated functions (... | ['Machine Learning', 'Deep Learning', 'Introduction', 'Unsupervised Learning', 'Autoencoder'] |
Definition of Classification: “The action or process of classifying something according to shared qualities or characteristics.”
“It’s all about finding patterns and understanding what differentiates them.”
The answer to the given question largely depends on what a person understands by the term ‘classification’. One... | ['Machine Learning', 'Supervised Learning', 'Classification', 'Unsupervised Learning', 'Artificial Intelligence'] |
Deep Convolutional Networks on Image tasks take in Image Matrices of the form (height x width x channels) and process them into low-dimensional features through a series of parametric functions. Supervised and Unsupervised Learning tasks both aim to learn a semantically meaningful representation of features from raw da... | ['Machine Learning', 'Deep Learning', 'Data Science', 'Unsupervised Learning', 'Artificial Intelligence'] |
A few days ago, OpenAI announced that they have created a very sophisticated AI model called GPT-2, it has been kind of famous cause they have refused to release the full model due to its potential dark uses (Fake news generation, fake content generation, etc.) But they have released a small part of it, called 117M.
I... | ['Machine Learning', 'Deep Learning', 'OpenAI', 'Unsupervised Learning', 'Artificial Intelligence'] |
Machine Learning Meets Biology (once again) or Single Cell RNA Sequencing As a Field for Unsupervised Learning Sergey Kolchenko Feb 18, 2019·9 min read
In a past decade amount of data in biology skyrocketed, and there seems to be no limit for that, as we have more and more sequenced genomes, deeper RNA expression and ... | ['Clustering', 'Machine Learning', 'Biology', 'Bioinformatics', 'Unsupervised Learning'] |
A continuously updating k-means algorithm
Photo by JESHOOTS.COM on Unsplash
Overview
We have all heard about the popular k-means algorithm used to find clusters in our data which is basically grouping of points that are similar based on the features present. Anyway, I will not bore you with the details about the alg... | ['Machine Learning', 'K Means', 'Algorithms', 'Unsupervised Learning', 'Data Science'] |
Mean Shift算法會沿著 KDE 的梯度方向尋找機率最大值，因此考慮
令 g(s) = -k’(s)，則
前一項為核函數，後一項則為 mean shift vector
利用迭代的方式更新中心點：
計算當前的 mean shift vector，也就是 m_h(center_old) 中心點沿著平均偏移向量移動做為新的中心點，意即 center_new = center_old + mean shift。
直到收斂便會找到核密度估計最大值的地方。
－ 演算法 －
輸入：資料集 D，以及帶寬 bandwidth
輸出：目標分群集合 Clusters
從未被分群的資料點中選擇一起始點做為中心。
2. 將距離中心... | ['Clustering', 'Machine Learning', 'Clustering Algorithm', 'Unsupervised Learning', 'Mean Shift'] |
An applied introduction to using Principal Component Analysis in machine learning
Principal Component Analysis (PCA) is an unsupervised, non-parametric statistical technique primarily used for dimensionality reduction in machine learning.
High dimensionality means that the dataset has a large number of features. The ... | ['Machine Learning', 'Pca', 'Neural Networks', 'Unsupervised Learning', 'Dimensionality Reduction'] |
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About me
After my Ph.D. in pure mathematics from Heidelberg University, I joined Accenture Technology as a Software Engineer in September 2018. During my studies, I chose computer science and economics as minors. Hence, I fulfilled quite well Udacity’s requiremen... | ['Machine Learning', 'Supervised Learning', 'Udacity', 'Unsupervised Learning', 'Artificial Intelligence'] |
INTRODUCTION
It was sometimes confusing and frustrating when you were trying to start up a new restaurant business in Toronto but had not a clue where should your new restaurant locate at that could maximize your possibility to success and make a profit from it. In this capstone project, a closer analysis of the best ... | ['Machine Learning', 'K Means', 'Data Visualization', 'Unsupervised Learning', 'Data Science'] |
“The unsupervised learning is the way most people will learn in the future. You have this model of how the world works in your head and you’re refining it to predict what you think is going to happen in the future.” — Mark Zuckerberg
Unsupervised learning forms a very niche part of Machine Learning, simply because mos... | ['Unsupervised Learning', 'Machine Learning', 'Artificial Intelligence', 'Data Science'] |
Unsupervised Representation Learning
Unsupervised representation learning is concerned to address the following issue:
How can we learn good representations from unlabeled data?
Besides the question of what a good representation is, learning from unlabeled data has great potential. It can unlock a number of applicat... | ['Deep Learning', 'Towards Data Science', 'Self Supervised Learning', 'Representation Learning', 'Unsupervised Learning'] |
Artificial Intelligence in Finance
DBSCAN Clustering for Trading
Image credit: Sharpshot
Pioneered in the 80’s by quantitative analysts at Morgan Stanley, pairs trading is a trading strategy that allows traders to profit in almost any market conditions. This strategy involves monitoring two historically correlated s... | ['Finance', 'Unsupervised Learning', 'Stocks', 'Ai In Finance', 'Artificial Intelligence'] |
An Informal Introduction to RL
Reinforcement learning provides a set of tools to train an agent to take optimal actions within an environment (a real or simulated world) by trial and error (i.e. executing an action and then experiencing its effects), guided only by rewards. A reward is a scalar feedback signal that te... | ['Graph Neural Networks', 'Deep Learning', 'Reinforcement Learning', 'Unsupervised Learning', 'Computer Vision'] |
With data overflowing in every direction, businesses are smartly rushing to utilize them to make data-driven decisions that create business value.
For this reason, Starbucks has published sets of simulated data that mimics customer behavior on the Starbucks Rewards mobile app. Starbucks operates more than 30,000 retai... | ['Clustering', 'Machine Learning', 'K Means', 'Segmentation', 'Unsupervised Learning'] |
Overview
What are autoencoders? How do autoencoders work? This article will answer your questions
We will explore the concept of autoencoders using a case study of how to improve the resolution of a blurry image
Introduction
Do you remember the pre-digital camera era? It was a mystical process that only photographe... | ['Unsupervised Learning', 'Computer Vision', 'Deep Learning', 'Data Science'] |
How To Use Unsupervised Machine Learning To Prevent Promotion Abuse
Conducting business in the modern, digital economy is a complicated process that involves, among other challenges, addressing ongoing tensions between powering growth and incurring risk. Online promotional campaigns present an excellent example of thi... | ['Machine Learning', 'Fraud', 'Fraud Detection', 'Unsupervised Learning', 'Cybersecurity'] |
Travel Segmentation by User Search Behaviors
In the travel and tourism industry, segmentation is an important strategy for developing itineraries and marketing materials targeted towards different groups with varying travel intents and motivations. It helps the businesses to understand the subgroups that make up the a... | ['Clustering', 'Segmentation', 'Data Science Tutorial', 'Ml For Travel', 'Unsupervised Learning'] |
Unsupervised & Manifold Learning in Human Brain
Functional MRI data are very high-dimensional if one considers all the voxels or surface coordinates acquired with standard imaging parameters. As in our dataset, with the structure of 4D time-series image data, we have a curve of dimensionality problem. Hence, dimension... | ['Machine Learning', 'Neuroscience', 'Visualization', 'Unsupervised Learning', 'Dimensionality Reduction'] |
Image source: Towards AI
Clustering is the process of dividing the entire data into groups (known as clusters) based on the patterns in the data. It is an unsupervised machine learning problem because here we do not have a target variable and we only group the data points based on their similarities.
Properties of cl... | ['Machine Learning', 'K Means', 'Data Science', 'Unsupervised Learning', 'Artificial Intelligence'] |
Clustering is an unsupervised machine learning technique. It can be widely used in many sectors such as IT, health care, automobile etc. It gathers and identifies the data based on the similarities , shape, size, behavior etc. is usually used to classify data in to structures that are easily understand and manipulated.... | ['Clustering', 'Machine Learning', 'Clustering Algorithm', 'Cluster Analysis', 'Unsupervised Learning'] |
Today we will be learning together a very interesting algorithm that uses similarity among items to group them. The idea seems pretty similar to that of KNN algorithm but the algorithm is not. Let’s get started with the k-means algorithm
What is clustering and what are its types?
Clustering is a process of grouping u... | ['K Means Clustering', 'Machine Learning', 'Beginner', 'Unsupervised Learning'] |
Use Gaussian Mixture Models to Transform User-Item Embedding and Generate Better User Clusters
Improve clustering of user-item embedding by using Gaussian mixture model to generate new and tighter user features Alon Agmon Apr 16·14 min read
Photo by Markus Spiske on Unsplash
I. Intro
A large company that sells musi... | ['Clustering', 'Machine Learning', 'Deep Learning', 'Reccomendations', 'Unsupervised Learning'] |
Undoubtedly, Scikit-learn is one of the best machine learning libraries available today. There are several reasons for that. The consistency among Scikit-learn estimators is one reason. You cannot find such consistency in any other machine learning library. The .fit()/.predict() paradigm best describes the consistency.... | ['Machine Learning', 'Supervised Learning', 'Scikit Learn', 'Unsupervised Learning', 'Data Science'] |
After you learn how to cluster the samples of unlabelled data points using the simplest clustering algorithm of k-means, we begin to see several shortcomings of k-means upon the application of this technique on a real dataset. The next step an ML engineer will take is to apply more sophisticated algorithms to understan... | ['Clustering', 'Machine Learning', 'Data Science', 'Unsupervised Learning', 'Artificial Intelligence'] |
Once the clustering is run one has to evaluate qualitatively the goodness of the output. First of all, to verify that groups are actually meaningful, and moreover having a score can be the proxy to evaluate different models and choose the best one.
There are many different scores out there, each one with its pros and ... | ['Clustering', 'Metrics', 'Unsupervised Learning', 'Mutual Information', 'Entropy'] |
The Task
The evaluation metric used for this competition was the AUC score, which tells us how well a model can distinguish between two diagnostic groups, in our case would be the individuals that are prospects of becoming new members to the ones that are not.
The dataset involved is composed of 42,962 observations (... | ['Unsupervised Learning', 'Machine Learning', 'Supervised Learning', 'Customer Segmentation'] |
Part 1 Unsupervised Learning
As mentioned, regarding the unsupervised model part, I used two datasets for the task:
azdias: Contains demographic information about the general population with shape of 891,221 rows and 366 columns
Contains demographic information about the general population with shape of 891,221 rows... | ['Unsupervised Learning', 'Machine Learning', 'Supervised Learning', 'Customer Segmentation'] |
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