Speaker: Mao Ting Description By segmenting customers into groups with distinct patterns, businesses can target them more effectively with customized marketing and product features. I'll dive into a few machine learning and statistical techniques to extract insights from customer data, and demonstrate how to execute them on real data using Python and open-source libraries. Abstract I will go through clustering and decision tree analysis using sciki-learn and two-sample t test using scipy. We will learn the intuition for each technique, the math behind them, and how to implement them and evaluate the results using Python. I will be using open-source data for the demonstration, and show what insights you can extract from actual data using these techniques. Event Page: https://pycon.sg Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/P6SD/
Views: 18473 Engineers.SG
http://www.mindecology.com This video shows you how to use advanced market (customer) segmentation techniques that go beyond using traditional demographic data alone. Video uses data-oriented examples so that you can see how it really works. The result is better-targeted advertising and marketing for better return on investment (ROI). http://www.mindecology.com
Views: 23846 Jed Jones
By using advanced analytics to create your segmentation strategies, you can: - Identify your most proitable customers - Focus your marketing on segments most likely to purchase - Discover potential niche markets - Develop or improve products to meet customer needs For more information visit http://www.angoss.com/predictive-analytics-software/applications/customer-analytics/
Views: 26659 AngossSoftware
Segmentation (Targeting, Profiling, Classification) is the process of dividing a database into distinct groups of individuals who share common characteristics. This is readily accomplished using modern data mining and machine learning techniques. The methods are easily implemented and work well with large datasets containing nonlinearities, interactions in the data and a mix of categorical and numerical variables. In this webinar, you will learn, via step-by-step instruction, how to use modern techniques to: 1) Segment a large database AND 2) Look at an already segmented/clustered database and discover the reasons for the class memberships. Access the data set, slides, and step-by-step guide here: http://info.salford-systems.com/customer-segmentation-webinar http://www.salford-systems.com
Views: 1054 Salford Systems
( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 72439 edureka!
Cette nouvelle session de la série de didacticiels "Le Data Mining en 35 Leçons avec STATISTICA" présente différentes techniques de segmentation, c’est-à-dire des méthodes de classification non-supervisée dont l’objectif consiste à répartir les observations en différents groupes relativement homogènes, possédant des caractéristiques communes. Le processus de construction de la segmentation est expliqué pas-à-pas dans STATISTICA au travers des techniques des k-moyennes, de l’Espérance Maximisation (EM) et des réseaux de neurones (cartes de Kohonen) en couvrant les options d’analyse et l’interprétation des résultats...
Views: 3052 Statistica France
Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 111672 Bharatendra Rai
Websand helps email marketers get better results by making it easy for them to manage their data into customer segments based on profiles or customer behaviour. The video explains how to you can create customer segmentation using the search feature within Websand. So far Websand users are recording increases in their marketing by +50%. For more information on how you can do the same visit Websand.co.uk
Views: 62 Websand
Data science can deliver transformational business insights by bringing together statistics, mathematics, computer science, machine learning, and business strategy. A variety of data science techniques are available which allow marketers to surface insights from large swathes of data, but which technique is right for your business and where do you start? In this on-demand webinar, our experts go over a broad range of data science techniques, and expose how major global brands are using them for valuable business insights including:customer lifetime value for customer segmentation and activation, forecasting and predictive analytics with machine learning, and natural language processing for digital marketing optimization
Views: 4464 Cardinal Path
Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 212133 Augmented Startups
K-means clustering algorithm is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both k-means and Gaussian mixture modeling. Additionally, they both use cluster centers to model the data; however, kmeans clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. ====================================================== watch part 2 here: https://www.youtube.com/watch?v=AukQSbtZ1NQ book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 21755 fun 2 code
Hierarchical Clustering - Fun and Easy Machine Learning with Examples ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hierarchical Clustering Looking at the formal definition of Hierarchical clustering, as the name suggests is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left. The results of hierarchical clustering can be shown using Dendogram as we seen before which can be thought of as binary tree Difference between K Means and Hierarchical clustering Hierarchical clustering can’t handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2). In K Means clustering, since we start with random choice of clusters, the results produced by running the algorithm multiple times might differ. While results are reproducible in Hierarchical clustering. K Means is found to work well when the shape of the clusters is hyper spherical (like circle in 2D, sphere in 3D). K Means clustering requires prior knowledge of K i.e. no. of clusters you want to divide your data into. However with HCA , you can stop at whatever number of clusters you find appropriate in hierarchical clustering by interpreting the Dendogram. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 35462 Augmented Startups
The fourth part of our tutorial on cell segmentation and single cell tracking in microscope phase images. Here we describe how to use the pipeline in combination with the Weka Segmentation Tool in Fiji ImageJ to segment cells. Scripts and datasets are available here: https://osf.io/gdxen/.
Views: 253 Heather Deter
#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 437057 Last moment tuitions
Cluster Analysis for Market Segmentation in Presentation Format.. Include information about Cluster Analysis and the relation between Cluster Analysis and Market Segmentation .. This is only for EDUCATIONAL & KNOWLEDGE purpose... :).. Hit Like if you Like the video & comment..
Views: 1060 Vishal TecHs
Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this. Code for this video: https://github.com/llSourcell/k_means_clustering Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html http://people.revoledu.com/kardi/tutorial/kMean/ https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html http://mnemstudio.org/clustering-k-means-example-1.htm https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 107214 Siraj Raval
Implementing and Training Predictive Customer Lifetime Value Models in Python by Jean-Rene Gauthier, Ben Van Dyke Customer lifetime value models (CLVs) are powerful predictive models that allow analysts and data scientists to forecast how much customers are worth to a business. CLV models provide crucial inputs to inform marketing acquisition decisions, retention measures, customer care queuing, demand forecasting, etc. They are used and applied in a variety of verticals, including retail, gaming, and telecom. This tutorial is separated into two parts: In the first part, we will provide a brief overview of the ins and outs of probabilistic models, which can be used to quantify the future value of a customer, and demonstrate how e-commerce companies are using the outputs of these models to identify, retain, and target high-value customers. In the second part, we will implement, train, and validate predictive customer lifetime value models in a hands-on Python tutorial. Throughout the tutorial, we will use a real-world retail dataset and go over all the steps necessary to build a reliable customer lifetime value model: data exploration, feature engineering, model implementation, training, and validation. We will also use some of the probabilistic programming language packages available in Python (e.g. Stan, PyMC) to train these models. The resulting Python notebooks will lay out the foundation for more advanced models tailored to the specifics of each business setting. Throughout the tutorial, we will give the audience additional tips on how to tweak the models to fit different business settings. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 15168 PyData
In this Machine Learning & Python video tutorial I demonstrate Hierarchical Clustering method. Hierarchical Clustering is a part of Machine Learning and belongs to Clustering family: - Connectivity-based clustering (hierarchical clustering) - Centroid-based clustering (K-Means Clustering) - https://www.youtube.com/watch?v=iybATqk6LNI - Distribution-based clustering - Density-based clustering In data mining and statistics, Hierarchical Clustering also called hierarchical cluster analysis or HCA is a method of cluster analysis which seeks to build a hierarchy of clusters. In this video I demonstrate how Agglomerative Hierarchical Clustering is working. Must know for Hierarchical Clustering is knowing Dendrograms. Dendrogram helps you to decide the optimal number of clusters for your dataset. For executing task in Python I used: - sklearn library that is for Machine Learning algorithms. - ward method that means Minimum Variance Method. If you are interesting more in Hierarchical Clustering, read my article on LinkedIn where I described my experiment about combining Machine Learning (Hierarchical Clustering) in GIS (Geographical Information System). - https://www.linkedin.com/pulse/machine-learning-gis-hierarchical-clustering-urban-bielinskas Data-set for this example is taken from https://www.kaggle.com. There you can find many dataset for very different Machine Learning tasks. Hierarchicaal Clustering is very usable in solving Data Analysis, Data Mining and Statistics problems. If you have any question or comments please write below. Do not forget to subscribe me if want to follow my new videos about Machine Learning, Data Science, Python programming and relative issues. Follow me on LinkedIn: https://www.linkedin.com/in/bielinskas/
Views: 3835 Dr. Vytautas Bielinskas
Extracting information from remotely sensed imagery is an important step to providing timely information for your GIS. ArcGIS Pro desktop provides a rich environment to process and exploit imagery. A significant aspect of this is the image segmentation and classification tools that process multispectral imagery, resulting in extracted feature data for the GIS. These can be combined with a range of machine learning algorithms for the automated feature extraction and identification, and also used as models that can be scaled up using Raster Analytics running on ArcGIS Image Server
Views: 5038 Esri Events
This session will show you how to create a Customer Segment. Customer segmentation is an enterprise-specific solution that uses data mining to group customers based on customer attributes and customer transactions. The retailer can use this information to describe and predict customer behavior. It provides the retailer with a vehicle to target customers with offers, pricing, assortment, and experience. The video is part of the Oracle Retail Cloud Documentation Video series, developed by the Oracle retail Documentation team to demonstrate, explain, and enhance your experience with the application. Useful links: Send feedback, comments, questions: [email protected] ================================= For more information, see http://www.oracle.com/goto/oll Copyright © 2017 Oracle and/or its affiliates. Oracle is a registered trademark of Oracle and/or its affiliates. All rights reserved. Other names may be registered trademarks of their respective owners. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the “Materials”). The Materials are provided “as is” without any warranty of any kind, either express or implied, including without limitation warranties or merchantability, fitness for a particular purpose, and non-infringement.
Views: 697 Oracle Learning Library
In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com
Views: 206267 Influxity
** Python Training for Data Science: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session: 1. What is Clustering? 2. Types of Clustering 3. What is K-Means Clustering? 4. How does a K-Means Algorithm works? 5. K-Means Clustering Using Python Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 46949 edureka!
We aim to answer all of your Machine Learning questions and queries. Get in touch if you have a specific question about Machine Learning you would like us to cover! Email: [email protected] In this episode we discuss a common conversation we have with large companies who thing they are already getting the most from segmenting. Of course segmenting is a very good step and greatly improves conversion rates. However, there is a similarly great improvement possible by leveraging machine learning for targeting. We discuss why. Data Revenue is the umbrella company for: https://scoring.ai/ - User Lifecycle Scoring for Real Estate Portals, and http://mvp.ai/ - Building AIs for data driven businesses
Views: 3136 Data Revenue
https://www.udemy.com/business-analytics-from-beginners-perspective-using-sas/?couponCode=51OFFSY Confused on what is Clustering technique all about ? How is Cluster Analysis different from standard Regression or Segmentation techniques ? Here is Introductory video for basics of Clustering Technique This video is part of the overall online course on Business Analytics for Beginners using SAS The course is a detailed course on Business Analytics specifically designed for Beginners and uses SAS and Excel to run Live Analytics projects Check out the course on link below: https://www.udemy.com/business-analytics-from-beginners-perspective-using-sas/?couponCode=51OFFSY Go ahead and invest in yourself !
Views: 21170 Analytics17
Blockspring has +1000 functions that can all be used in Google Sheets. Check out the blog post here: https://api.blockspring.com/blog/blockspring-for-google-sheets In this example, we'll go through how easy it is to segment your customer list and visualize multi-dimensional data.
Views: 3395 Blockspring
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 93771 MIT OpenCourseWare
In this tutorial, we shift gears and introduce the concept of clustering. Clustering is form of unsupervised machine learning, where the machine automatically determines the grouping for data. There are two major forms of clustering: Flat and Hierarchical. Flat clustering allows the scientist to tell the machine how many clusters to come up with, where hierarchical clustering allows the machine to determine the groupings. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 59178 sentdex
By Dorit Simona Hochbaum. The dominant algorithms for machine learning tasks fall most often in the realm of AI or continuous optimization of intractable problems. This tutorial presents combinatorial algorithms for machine learning, data mining, and image segmentation that, unlike the majority of existing machine learning methods, utilize pairwise similarities. These algorithms are efficient and reduce the classification problem to a network flow problem on a graph. One of these algorithms addresses the problem of finding a cluster that is as dissimilar as possible from the complement, while having as much similarity as possible within the cluster. These two objectives are combined either as a ratio or with linear weights. This problem is a variant of normalized cut, which is intractable. The problem and the polynomial-time algorithm solving it are called HNC. It is demonstrated here, via an extensive empirical study, that incorporating the use of pairwise similarities improves accuracy of classification and clustering. However, a drawback of the use of similarities is the quadratic rate of growth in the size of the data. A methodology called “sparse computation” has been devised to address and eliminate this quadratic growth. It is demonstrated that the technique of “sparse computation” enables the scalability of similarity-based algorithms to very large-scale data sets while maintaining high levels of accuracy. We demonstrate several applications of variants of HNC for data mining, medical imaging, and image segmentation tasks, including a recent one in which HNC is among the top performing methods in a benchmark for cell identification in calcium imaging movies for neuroscience brain research.
Views: 164 INFORMS
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-674518790 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 81705 Udacity
#SubScribeOurChannel k means clustering example #KMeanscClustering #ImagesSegmentation Subscribe Our Channel:https://www.youtube.com/c/ProgrammingTech676 HI welcome to programming tech in this tutorial we learn how to image segmentation using k-mean. computer vision tools Detect a tumor in brain using k-mean. Image segmentation is the classification of an image into different groups. Many researches have been done in the area of image segmentation using clustering. ... K -means clustering algorithm is an unsupervised algorithm and it is used to segment the interest area from the background. Source Code:https://programmingtech6.blogspot.com/p/k-mean-segmenation.html 3.Matlab Basic Tutorial Command Window Base Coding and Function. https://youtu.be/YHPULfu2ai0 4.Matlab Basic Tutorial video About Vector function and how use Matrix operation. https://youtu.be/i5sSbfgI3ow 5.Matlab Basic Tutorial video About Matrix Function and Operation. https://youtu.be/4XZG2RNhrcA 6.How to Connect Mobile Camera And Webcam with MATLAB/Laptop https://youtu.be/7th54GDufuY #7.How to plot a Graph in Matlab and Read Image show using Subploting Concept. https://youtu.be/IVFWeWzZjEw #8.How to Browse Images From Drive & HOW to apply Histogram/Equalize Histogram on Image In Matlab https://youtu.be/ZO6LVdoF4M8 #10. [Point Processing #2].How Power Law Transformation Implement On Image Using Matlab. https://youtu.be/3lw3snlDIoY #11 [POINT PROCESSING#3] How to Convert An Image To Negative Image Using For Loop in Matlab https://youtu.be/MDlKCh_e-WU #12:Extraction of Bit Planes and Merging of Bit Plane Slicing in Matlab code https://youtu.be/TSWYzxZX8EE #13.How to Install Toolboxes in Matlab and Why some Toolboxes are Not install. https://youtu.be/VYGHArawk5s 14.How to Detect Edges Using Sobel and Canny Edge Filters in Matlab. And Comparison Between Two. https://youtu.be/L6F8DgmV8Io #15 How to Detect Edges of an Image using Laplacian Filters in Matlab https://youtu.be/7aNi1m1uXxc #16 How Image Sharpening using Laplacian Filter | Matlab Code https://youtu.be/2t54KkjnV90 18. Matlab code For Smoothing filter in Digital image processing using Neighborhood https://youtu.be/1uXazxD-NaI FOR MORE Matlab Tutorial click on below link : Subscribe Our Channel:https://www.youtube.com/c/ProgrammingTech676 IF YOU like VIDEO PLEASE share video comment and SUBSCRIBE CHANEL to get latest video TUTORIAL notification. Thank you Tags: Matlab Tutorials Matlab Basic Tutorial Matlab advanced Tutorials Matlab Beginner Tutorial Digital image Processing Tutorial Filters Tutorials Mathworks Tutorials Programming Tutorial Thanks
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Tableau - Do it Yourself(DIY) Tutorial - Market Basket Analysis -DIY# 41 of 50 https://drive.google.com/drive/folders/0B1BHXHiSfdg_OGFQVGhKQmFVbHc?usp=sharing by Bharati DW Consultancy cell: +1-562-646-6746 - Cell & Whatsapp email: [email protected] [email protected] website: http://bharaticonsultancy.in/ Tableau Do it Yourself - Market Basket Analysis -DIY# 41 of 50 High Level Steps: #1- Use OrderDetails.xlsx #2- Create a Parameter - Base Product Type #3- Create a calculation to count Products in an order. - Product Type Count if([Product Type]=[Base Product Type]) then 1 else 0 end #4- Create a calculation to find other Products in the same order. - Other Product Types if([Product Type] *;not-equal;*[Base Product Type]) then [Product Type] else 'N/A' end replace *;not-equal;* #5- Create a Set for Order ID, to find the orders having more than one product. #6- Create a Layout.
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Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Python for Data Science Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=SC&utm_source=youtube The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants. Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization. Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS. As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis. Who should take this course? There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. Analytics professionals who want to work with Python 2. Software professionals looking for a career switch in the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in Analytics and Data Science 5. Experienced professionals who would like to harness data science in their fields 6. Anyone with a genuine interest in the field of Data Science For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
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