Home
Search results “Video data mining ppt slides”
Introduction to data mining and architecture  in hindi
 
09:51
#datamining #datawarehouse #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: 262294 Last moment tuitions
How data mining works
 
06:01
In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 238441 Thales Sehn Körting
data mining powerpoint
 
12:01
IASP 520 - Data Mining How Data Mining is used in the Healthcare Field
Views: 1048 Stephanie Hansen
Data Mining
 
03:01
A video presentation on DATA MINING which describes the basics of data mining in a simple slides. I have compiled this from many sources and tried to make it as much as simple as data mining is a ocean to explore. I hope this presentation will be helpful to someone .if u liked and need original ppt send me mail.
Views: 38 guhan r
Data Mining Class Presentation
 
07:01
Data Mining - NHL Data Analysis Rutgers Newark Fall 2009 By, Mariusz Grabowski Matthew Wisner Iyesha Kamara Phalguni Dave
Views: 505 Mariusz Grabowski
Learning From Data (Data Mining) Presentation
 
27:48
This video explain how data processes in machine and how the machine learn from human. Machine Learning Artificial Intelligence Data Mining Information Technology Data Techniques Technology 2017 Machine Learning Human learning Data Process Algorithms Learning Algorithms Machine Learning Algorithms
Views: 591 M Rukhshan Ali
Data Mining Video
 
02:48
Data Mining Video for HIM
Views: 17 Aaryn Curtis
Data Mining and Visualization Paradata Project
 
04:49
This is my final project for my Data mining class. Links to my information, github, and my powerpoint for research purposes: Infographic: https://infogr.am/video_games_and_viewing_them Github: https://github.com/jonlouiscool/Final-Project/tree/master Powerpoint: https://docs.google.com/presentation/d/1daRLP6r0Cw6PPKStIBwucYn2Jv8uBGnYgdWyy2YN8iI/edit?usp=sharing Sorry if the quality is low, this is due to the converter. All sources are found in the powerpoint. Hope you enjoy, and remember gaming is the future.
Views: 228 Jonlou Czajka
How to explain Data Science Using Presentation Diagrams
 
01:48
Download: https://www.infodiagram.com/diagrams/data_science_analytics_icons_ppt_flat.html?cp=camp5 What's Data Science? How it related to Big Data? And Data Mining? Example of simple visual explanation of areas that compose Data Science - A. data sources including Big Data, B. algorithm for processing data e.g. as statistics and machine learning algorithms C. business use. Illustration of data analysis process. See inspiration how you can present these popular data related concepts visually. Using simple charts and symbols. Adapt the presentation to your context. And let me know in comments how you did it :). I'd love to hear your opinion. All this is Do It Yourself graphics using Powerpoint. Read visualization tips on IT technology slide design on my https://blog.infodiagram.com Comments are welcome!
Data Mining Presentation (Customer Segmentation)
 
04:55
None-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 2688 Afiq Zaimi
BIG DATA PRESENTATION
 
11:05
For Info Resources Class
Views: 19221 Carl Peterson
Data Mining
 
02:02
-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 38933 Azis Ikwanto
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
36:36
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience 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). Website: https://www.edureka.co/data-science 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. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 78305 edureka!
Data Mining - Clustering
 
06:52
What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
What is Text Mining?
 
01:49
An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 55988 Elsevier
Web Mining - Tutorial
 
11:02
Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Data Warehousing and Data Mining
 
09:48
This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 5866 SlideTalk
How To... Extract an Embedded Video from a PowerPoint Presentation
 
02:59
Learn how to extract an embedded video from a PowerPoint presentation. You may want to do this if you have lost the original copy of the video and all you have left is a presentation with the video embedded. There is no option within PowerPoint to save an embedded video as a separate file. In this video you will learn how to extract an embedded video by using a ZIP file.
Views: 117291 Eugene O'Loughlin
Data Science In 5 Minutes | Data Science For Beginners | What Is Data Science? | Simplilearn
 
04:38
This Data Science tutorial video will give you an idea on the life of a Data Scientist, steps involved in Data science project, roles & salary offered to a Data Scientist. Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data Science is basically dealing with unstructured and structured data. Data Science is a field that comprises of everything that is related to data cleansing, preparation, and analysis. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data. Now, let us get started and understand what is Data Science all about. Below topics are explained in this Data Science tutorial: 1. Life of a Data Scientist 2. Steps in Data Science project - Understanding the business problem - Data acquisition - Data preparation - Exploratory data analysis - Data modeling - Visualization and communication - Deploy & maintenance 3. Roles offered to a Data Scientist 4. Salary of a Data Scientist To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into 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 Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-X3paOmcrTjQ&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 306805 Simplilearn
1 - Introduction to Data warehouse and Data warehousing
 
05:17
Short Introduction Video to understand, What is Data warehouse and Data warehousing? How it is different from Database? It also talks about properties of Data warehouse which are Subject Oriented, Integrated, Time Variant, Non Volatile ETL Tools: Talend Open Studio, Jaspersoft ETL, Ab initio, Informatica, Datastage, Clover ETL, Pentaho ETL, Kettle. #datawarehouse #ETL #DWH Business Intelligence tools: Oracle BI, Microsoft BI suite, Tableau, Qlik, Jaspersoft BI, Pentabo BI, Miscrostrategy, Tibco For more details visit: http://www.vikramtakkar.com/2015/08/what-is-datawarehouse-and.html Datawarehouse Playlist: https://www.youtube.com/playlist?list=PLJ4bGndMaa8FV7nrvKXeHCLRMmIXVCyOG
Views: 120701 Vikram Takkar
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
10:36
#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: 331224 Last moment tuitions
Data Mining in the Retail Industry
 
07:04
This is a powerpoint/video compilation I made for a project in my Systems Engineering class. It is a tutorial of Data Mining in the Retail Industry and includes a trip I took to Harris Teeter to prove the importance of Market Basket Analysis in the real world.
Views: 7710 bgood717
Educational Data Mining (EDM): Turning Big Data into Big Gains for Students
 
01:00:19
From making travel plans, to online purchases, to watching videos, each day we generate vast amounts of data that contribute to the world of big data. We have already seen big data play a significant role in areas like marketing and science. Now, education has joined the big data movement. In the past, education data was sparse and disparate. Collected across individual gradebooks and housed within multiple platforms, data was inaccessible, laborious, and difficult to analyze. Thankfully, this has changed. Now, educators and researchers can access incredibly rich and meaningful logs about student learning behavior on educational software, and by employing EDM (education data mining), discover a great deal about how students learn. By connecting this powerful data and asking the right questions, there is potential to change the future of education. Learn about the ability to leverage meaningful data with EDM and learning analytics, and find out how to turn big data into big gains for students. Attend this webinar to discover how: Learning analytics and EDM are already transforming education EDM advancements can assess students’ knowledge as they are learning Specific EDM methods are proving useful in understanding and predicting which students are likely to succeed in 21st century careers Learning analytics can provide insight into the effectiveness of educational technology programs and the conditions under which these programs have the greatest return on learning
Views: 1243 eschoolnews
What is Business Intelligence (BI)?
 
03:47
There are many definitions for Business Intelligence, or BI. To put it simply, BI is about delivering relevant and reliable information to the right people at the right time with the goal of achieving better decisions faster. If you wanna have efficient access to accurate, understandable and actionable information on demand, then BI might be right for your organization. For more information, contact Hitachi Solutions Canada (canada.hitachi-solutions.com).
Views: 399429 Hitachi Solutions Canada
H1B VISA Project Video Presentation
 
08:28
This video is a part of the final project of Data Mining class at USF The team members are Arundhathi Patil Palak Tater Shruti Sridharan Varsha Sharma
Views: 138 Shruti Sridharan
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
14:17
In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 165361 Well Academy
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn
 
27:43
This KNN Algorithm tutorial (K-Nearest Neighbor Classification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works. Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial: 1. Why do we need KNN? 2. What is KNN? 3. How do we choose the factor 'K'? 4. When do we use KNN? 5. How does KNN algorithm work? 6. Use case - Predict whether a person will have diabetes or not To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/XP6xcp Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 64508 Simplilearn
Cancer Identification Data Mining Java Project
 
06:37
Cancer Identification System Data Mining Java Project Download Project Code, Report and PPT :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 2228 1000 Projects
data mining in healthcare
 
05:06
how does the data mining technique help in solving healthcare problem-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 6464 Fouz Alaseeri
The best stats you've ever seen | Hans Rosling
 
20:36
http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2917182 TED
Introduction to Data Science with R - Data Analysis Part 1
 
01:21:50
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 1022851 David Langer
OPTICS : Ordering Points To Identify Clustering Algorithm Video | Clustering Analysis - ExcelR
 
20:26
ExcelR: In this video, we will learn about the basic approach of OPTICS is similar to DBSCAN, but instead of maintaining a set of known, but so far unprocessed cluster members, a priority queue (e.g. using an indexed heap) is used. Things you will learn in this video 1)What is OPTICS? 2)What are drawbacks in DBSCAN? 3)Advantages & Disadvantages in OPTICS 4)What is OPTICS-Appendix? To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here https://goo.gl/JTkWXo SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #OPTICS#Differenttypesofclusterings#ClusterAnalytics#AdvantagesanddisadvantagesinOPTICS #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Bitcoin POWER Increasing! May 2019 Price Prediction, News & Trade Analysis
 
37:30
***My fully comprehensive road map to becoming a COMPLETE Trading Boss! + VIP DIscord Access*** https://krown-trading.teachable.com/p/trade-like-a-professional-the-art-and-application-of-technical-analysis/ All my trading strategies revealed in a series of 39 Modules of Video and PowerPoint presentation format over 32 hours long + Bonus Material! Reviews inside! ***Master Your Options*** - https://krown-trading.teachable.com/p/master-your-options/ - Years as a Professional Market Maker Authorized Trader synthesized down into a 42 Module Road Map for the Purpose of Derivative Domination ***Krown Jewel Indicator Mastery*** https://krown-trading.teachable.com/?affcode=236462_6ozjtirq My preferred method of scalping by timing both entries and exits with 5 unique oscillators each suited for specific trading styles Complimentary 10% off trading fees for Deribit ***https://www.deribit.com/reg-1170.7653*** Preferred Forex Broker (BTC Deposits) **https://evolve.markets/r/1010d2dc** ByBit Complementary Direct Signup **https://www.bybit.com/app/register?ref=r2P96** Complimentary 10% off trading fees for BitMEX https://www.bitmex.com/register/4l6ovV KrownStatus.com - The digital hub of all Cave related activities. ***Cave Discord*** https://discord.gg/5Zwuum8 ***Digital Den*** https://discord.gg/CsvbmDQ Please do consider gently tapping the like & subscribe button as I update my thoughts on the market daily! ***Complimentary Portfolio Tracking and Balancing Tool*** https://docs.google.com/spreadsheets/d/1Y8WtgxCZen4yt8UwAmqxQjfDR42-kFQ8iRx_3rJi0CI/edit#gid=1158545460 If you feel so inclined to support my Coffee and Ground Beef diet then here is a link to my BTC address - 3QnMhH3QKjoNMprArJ2PyY2t8MWPCDtmJv Recommended Reading for Learning the Art of Trading - Technical Analysis and Stock Market Profits https://amzn.to/2qQAz7A Reminiscences of a Stock Operater https://amzn.to/2F3WD3G Also checkout my Steemit Account as I do post exclusive content here - https://steemit.com/@realcrowncrypto Twitter for Intra-day Mini Update - https://twitter.com/KrownCryptoCave #Bitcoin #BitcoinNews #BitcoinAnalysis Disclaimer - The content in this video and on this channel are not intended to be financial advice. The content in this video and on this channel are only intended for entertainment purposes only! Bitcoin Analysis, Top bitcoin analysis, price prediction, Bitcoin Trading, Bitcoin 2018, Bitcoin Crash, Bitcoin Moon, Bitcoin News, Bitcoin Today, Best Bitcoin Analysis, Bitcoin price, Bitcoin to 0, Where is Bitcoin Going, Bitcoin bottomed For Business Inquires Please Contact - [email protected]
Views: 6376 Krown's Crypto Cave
Сompany video presentation with 3d-graphics for AVK
 
01:41
Need company video presentation? Visit our website https://bit.ly/2FZKGjl Our portfolio https://vimeo.com/1infographics/videos We created this company video presentation for AVK, company which is engaged in the construction of sports facilities. 3d-graphics. 3D graphics allows you to explain in detail all the advantages of cooperation with the company increase its attractiveness in the eyes of customers. Order your own company video presentation If you want to take a worthy place in the market in the era of the undivided domination of video content in the advertising https://www.youtube.com/channel/UCfa0FimmCiezyVQLq_yXyOQ http://eng.videozayac.ru/ https://www.youtube.com/watch?v=AixC3HQh-rw #videomarketing #presentation #videoproduction
Efficient Techniques for Online Record Linkage
 
02:58
Title: Efficient Techniques for Online Record Linkage Domain: Data Mining Key Features: 1. In recent years, heterogeneous data sources information merging is extensively required one. To achieve this goal, different types of heterogeneity problems should be resolved by an organization particularly the entity heterogeneity problem occurs when similar real-world entity type is signified using distinct identifier in various data sources. 2. To tackle this problem, statistical record linkage technique is used. But it fails when this statistical record linkage technique is used for online record linkage. It causes remarkable communication blockage in a disseminated environment (heterogeneity problems also arises). 3. To overcome this problem, a technique is proposed using the matching tree which resembles the same as decision tree .The proposed technique deduces the communication overhead drastically. The matching decision will be similar as result of conventional linkage technique. 4. The databases exhibiting entity heterogeneity are distributed, and it is not possible to create and maintain a central data repository or warehouse where precomputed linkage results can be stored. 5. An important issue associated with record linkage in distributed environments is that of schema integration. For record linkage techniques to work well, one should be able to identify the common nonkey attributes between two databases. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 2000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Views: 2520 InnovationAdsOfIndia
How to Create an Awesome Slide Presentation (for Keynote or Powerpoint)
 
17:22
In this episode of SPI TV, I'm going to show you how to create an awesome slide deck for your next presentation, one that captivates your audience and supports your talk, not bores people to death and puts people to sleep. I've performed dozens of presentations myself, and I take great pride in how I approach my slide deck. I'm always getting complimented on my slides, and I want the same to happen to you. Creating great slides doesn't have to be difficult, and with a few simple rules and some guidelines to follow, you'll stand out as a top presenter the next time you're on stage or presenting in front of a group. Please note that I do talk briefly about why this is important, however if you'd like -~-~~-~~~-~~-~- Building an email list? Watch my latest video: "How to Get More Email Subscribers (17 Lead Magnet Ideas)": https://www.youtube.com/watch?v=6te1AlLUA10 -~-~~-~~~-~~-~-
Views: 1046296 Pat Flynn
Cambridge Analytica - The Power of Big Data and Psychographics
 
11:01
Description: In a presentation at the 2016 Concordia Annual Summit in New York, Mr. Alexander Nix discusses the power of big data in global elections. Cambridge Analytica’s revolutionary approach to audience targeting, data modeling, and psychographic profiling has made them a leader in behavioral microtargeting for election processes around the world. Speaker: Mr. Alexander Nix CEO, Cambridge Analytica
Views: 495407 Concordia
Data Mining : Data Visualization Techniques
 
05:22
This video explains various visualization techniques in data mining. Video Lecture by Anisha Lalwani.
Views: 4253 topNotch Tutorials
Decision Tree Building based on Impurity for KDD or Machine Learning
 
14:36
In this video, I create a decision tree using Gini Impurity to determine the splitting attributes. I originally created this video (and the others in my series) to be used with a specific KDD class which is taught at my home university. I first encountered this algorithm in class there. If you would like to look into this topic in more detail, or read a bit about some similar algorithms, I am including the link to one of the presentations that I used as a reference. coitweb.uncc.edu/~ras/KBS-Class/1-Decision-Trees.ppt Thank you for watching!
Views: 45716 Laurel Powell
What Is Data Science? Data Science Course - Data Science Tutorial For Beginners | Edureka
 
01:03:05
( Data Science Training - https://www.edureka.co/data-science ) This Edureka Data Science course video (Data Science Blog Series: https://goo.gl/yGjZfs) will take you through the need of data science, what is data science, data science use cases for business, BI vs data science, data analytics tools, data science lifecycle along with a demo. This Data Science tutorial video is ideal for beginners to learn data science and machine learning basics. You can read the blog here: https://goo.gl/lYb5Lb Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience 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: 191782 edureka!
How To Create an MP4 Video From a PowerPoint Presentation
 
01:27
In a previous post of mine, I had mentioned the capability of creating a video file from a PowerPoint Presentation in Office 2010 (Greek Version). The only lack then, was the fact that we could only create a Windows Media Video (.wmv) file. Fortunately, Microsoft Office 13 is here, and especially PowerPoint 13, which gives us the capability to create a MP4 video file from our presentation. The video describes how it can be done. You can check out the video below or follow the link below to read the post. Looking For Tips & Tricks on how to work with Microsoft Office Applications? Subscribe To This Channel: https://www.youtube.com/user/philippospan https://officesmart.wordpress.com/
Data Mining  Association Rule - Basic Concepts
 
06:53
short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Presentation Data Mining & Decision-making: Case of Amazon.com
 
01:36
Week 2 assignment for MooreFMIS7003 course at NCU. Prepared by FahmeenaOdetta Moore.
Qualitative analysis of interview data: A step-by-step guide
 
06:51
The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Text and video (including audio) © Kent Löfgren, Sweden
Views: 769741 Kent Löfgren
Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial
 
05:59
Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial ►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 Limited Time - Discount Coupon Hey guys and welcome to another fun and easy machine tutorial on Eclat. Today we are going to be analyzing what video games get sold more frequently using an associated rule algorithm called Eclat. The Eclat algorithm which is an acronym for Equivalence CLAss Transformation is used to perform itemset mining. Itemset mining let us find frequent patterns in data like if a consumer buys Halo, he also buys Gears of War. This type of pattern is called association rules and is used in many application domains such as recommender systems. In the previous lecture we discussed the Apriori Algorithm. Eclat is one of the algorithms which is meant to improve the Efficiency of Apriori. Eclat is a depth-first search algorithm using set intersection. It is a naturally elegant algorithm suitable for both sequential as well as parallel execution with locality-enhancing properties. It was first introduced by Zaki, Parthasarathy, Li and Ogihara in a series of papers written in 1997. ------------------------------------------------------------ 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: 6815 Augmented Startups
Lecture - 30 Introduction to Data Warehousing and OLAP
 
57:50
Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 212633 nptelhrd
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
 
46:38
** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial: 1. What is Classification? 2. Types of Classification 3. Classification Use Case 4. What is Decision Tree? 5. Decision Tree Terminology 6. Visualizing a Decision Tree 7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm #decisiontree #decisiontreepython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. 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
Views: 77368 edureka!
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
07:28
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: 214217 Augmented Startups