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Description

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The second live conversation for the Data Analysis and Interpretation specialization capstone course

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Views: 4882 Jeff Leek

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Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 143 Audiopedia

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Views: 238 WF NEN

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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: 64290 Simplilearn

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In this video, the Speaker, Jason Geng, talked about the Data Science Project Lifecycle and Data Scientist Skill Set. He introduced the whole process of Data Science Project: Business Requirement, Data Acquisition, Data Preparation, Hypothesis & Modeling, Evaluation& Interpretation, Deployment, Operations, and Optimization. There are more detailed explanation and examples, which can help you to understand these procedural concepts accurately. In addition, the video also introduced some skill sets that are looked for when building data team. More from Data Application Lab Official Reviews: Subscribe on YouTube: https://www.youtube.com/channel/UCa8NLpvi70mHVsW4J_x9OeQ Website: https://www.dataapplab.com

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( Data Science Training - https://www.edureka.co/data-science ) Watch sample class recording: http://www.edureka.co/data-science?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. Data Science is one of the most-sought after professions today. Universities across the world are offering courses in this discipline which stands testimony to this emerging profession. There are a very few professionals with the required skill and the demand for data scientists is racing ahead. The tutorial wil give a brief understanding about Data Science. The topics covered in the video: 1.Problem Statement 2.Variable Desriptions 3.Data EXploration 4.Data Cleaning and Preparation 5.Reading from Other Sources 6.Titanic Data Sets 7.Decision Trees and Random Forests 8.Build a Decision Tree 9.Build a Random Forest 10.Linear Regression 11.Logistic Regression 12.Machine Learning 13.Data Mining 14.Machine Learning and Data Mining Resources 15.Solving a Data Science Problem using R, Hadoop, Mahout Related Posts: http://www.edureka.co/blog/who-can-take-up-a-data-science-tutorial/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/enroll-for-a-data-science-course/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/types-of-data-scientists/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/core-data-scientist-skills/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. ‘Develop a Data Science Project’ have been widely covered in our course ‘Data Science’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 33474 edureka!

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Big Data is a growing, exciting field with lots of career opportunities. In this session, Big Data Analytics Program Manager Nancy McQuigge speaks to the changing role of data management in various industries and how this program can help launch your career in data analytics.

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Introduction Class
Views: 900 UofU Data

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Automated Data Analysis Framework Capstone Project - Sponsored by Gates Foundation
Views: 147 kartikey bharti

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** Data Analytics Masters' Program: https://www.edureka.co/masters-program/data-analyst-certification ** ** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Analyst vs Data Engineer vs Data Scientist" will help you understand the various similarities and differences between them. Also, you will get a complete roadmap along with the skills required to get into a data-related career. Below topics are covered in this video: 1:05 - Who is data analyst, data engineer and data scientist? 2:32 - Roadmap 3:48 - Required skill-sets 5:34 - Roles and Responsibilities 7:16 - Salary Perspective ------------------------------------- Data Science Training Playlist: https://goo.gl/Jg1pJJ Blog Series: https://goo.gl/H2pf8V Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV 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 #dataanalystvsdataengineervsdatascientist #DataScience #DataScienceCertificationTraining ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Data Science Training and Certification, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Views: 33204 edureka!

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#PredictiveAnalytics | Learn the prediction of outcome or treatment of a case by legal courts of Appeals based on historical data using predictive analytics. Watch the video to understand analytics in legal using case study on real-life data set. How litigation analytics can flourish with the use of data mining and AI. Know more about our analytics Program: PGP- Business Analytics: https://goo.gl/V9RzVD PGP- Big Data Analytics: https://goo.gl/rRyjj4 Business Analytics Certification Program: https://goo.gl/7HPoUY #LegalTech #LegalAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence.
Views: 1108 Great Learning

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** Data Science Certification using R: https://www.edureka.co/data-science ** This Edureka "Data Science Projects" video explains how to solve a problem using data science and the basic approach of a Data Scientist to go about a problem. Below are the topics covered in this module: [01:30] Basic approach to solve a problem [02:21] Problem Statement 1 [02:56] Decision Trees and Random Forests [09:42] Problem Statement 2 [10:25] Logistic Regressions [21:44] Summary Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist ------------------------------------- Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV 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 #DataScienceProjects #DataScienceTutorial #MachineLearningProjects -------------------------------------- How it Works? 1. This is a 30-hour Instructor-led Online Course. 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 Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. ------------------------------------- Who should go for this course? The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for: Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'R' professionals who wish to work Big Data Analysts wanting to understand Data Science methodologies ------------------------------------- Why learn Data Science? Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges. ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Views: 4358 edureka!

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This Random Forest Algorithm tutorial will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is Classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python. Below are the topics covered in this Machine Learning tutorial: 1. What is Machine Learning? 2. Applications of Random Forest 3. What is Classification? 4. Why Random Forest? 5. Random Forest and Decision Tree 6. Use case - Iris Flower Analysis Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/K8T4tW Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Random-Forest-Tutorial-eM4uJ6XGnSM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Random-Forest-Tutorial-eM4uJ6XGnSM&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning 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. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you 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. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 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 - - - - - - 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: 67827 Simplilearn

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Considering Content Analysis.mp4
Views: 12592 Charles Gasper

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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 399040 APMonitor.com

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In this talk from April of 2017, Sam demonstrated how the data science team at Betterment uses the R analysis and reporting stack to understand and monitor investor behavior. Using both open source and in-house tools, Sam demonstrated their process from raw data extraction to modeling, reproducible reporting, and automatic monitoring. The Data Incubator is a data science education company based in NYC, DC, and SF with both corporate training as well as recruiting services. For data science corporate training, we offer customized, in-house corporate training solutions in data and analytics. For data science hiring, we run a free 8 week fellowship training PhDs to become data scientists. The fellowship selects 2% of its 2000+ quarterly applicants and is free for Fellows. Hiring companies (including EBay, Capital One, Pfizer) pay a recruiting fee only if they successfully hire. You can read about us on Harvard Business Review, VentureBeat, or The Next Web, or read about our alumni at LinkedIn, Palantir or the NYTimes. http://www.thedataincubator.com About the speakers: Sam Swift is the Director of Analytics & Data Scientist at Betterment, a New York city startup helping to modernize personal investing and retirement planning by optimizing returns net of fees, taxes, and behavior. His team covers the product, growth, and finance analytics at Betterment to better understand how Betterment and its customers could be more quickly reaching their respective financial goals. Sam received his PhD in Organizational Behavior from the Tepper School of Business at Carnegie Mellon University in 2012. His academic research focused on negotiation, decision making, and behavioral economics, and his work has been published in top management and psychology journals. Before coming to NYC, Sam was a postdoctoral scholar with affiliations at UC Berkeley, U Penn, and Duke University while running the data science operations of the ‘Good Judgment’ team. The team won a wisdom-of-the-crowds geopolitical forecasting tournament hosted by the US intelligence community. Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners. Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup to focus on what he really loves. Michael lives in New York, where he enjoys the Opera, rock climbing, and attending geeky data science events.
Views: 3759 The Data Incubator

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This is the ninth session in the 2017 Microbiome Summer School: Big Data Analytics for Omics Science organized by the Université Laval Big Data Research Center and the Canadian Bioinformatics Workshops. This lecture is by Mickael Leclercq from Universite Laval. For tutorials and lecture slides for this workshop, please visit bioinformaticsdotca.github.io. How it Begins by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100200 Artist: http://incompetech.com/

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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 262 Udacity

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#BusinessAnalyticsCourse | Business Analytics, the methodical exploration of organization’s data with an emphasis on statistical analysis, is a better career opportunity to earn more and give your career the right direction for success. Great Learning uploads videos that show you – a Roadmap to Business analytic – tools, techniques & applications. Learn a lot more about business analytics courses and its potential. Our videos are uploaded by industry’s experts after their experience and ways of learning more. Subscribe our channel and get videos on business analytics courses. #BusinessAnalytics #BusinessAnalyticsTutorial #GreatLearning #GreatLakes Visit https://greatlearningforlife.com our learning portal for more videos introducing you to business analytics, data science, machine learning and AI as well as full tutorials on advanced topics. A roadmap to Business Analytics. Learn about various tools and techniques in Business Analytics, supervised and unsupervised learning techniques, and which one to use for different variables. Know More about our analytics programs: PGP-Business Analytics: https://goo.gl/QEcWgw PGP-Big Data Analytics: https://goo.gl/Gr6DJR Business Analytics Certificate Program: https://goo.gl/x6MdH1 Dr. P K Viswanathan, Professor at Great Lakes Institute of Management shares a roadmap to Business Analytics. He talks about the supervised and unsupervised learning techniques, and which one to use for different kind of variables. About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence.
Views: 137550 Great Learning

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In the newest version of Treeplan, you need to use ctrl+shift+T to launch Treeplan, not ctrl+T mentioned in the video.
Views: 149774 CQ

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Business Analytics/Data Mining
Views: 54 Cory Lueth

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When data people join forces, we can answer any question, solve any problem, and rise to any challenge—together. This is why data.world has built the social network for data people. Sign up today: https://data.world
Views: 5844 datadotworld

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You are a HUGE football fan. Every week you pick winners in an NFL pick-em' league. Somehow, all that fan experience doesn't translate into consistently winning your league. Perhaps you need a more systematic approach that takes some of the emotion out of it. Where to start? Betting spreads provide a consistent and robust mechanism for encapsulating the variables and predicting outcomes of NFL games. In a weekly confidence pool, spreads also perform very well as opposed to intuition-based guessing and "knowledge" from years of being a fan. Can we do better? In this talk, we will discuss an approach to use machine learning algorithms to make improvements on the spread method of ranking winners on a weekly basis as an exercise in winning your friendly neighborhood confidence pool. https://datadialogs.ischool.berkeley.edu/2016/schedule/using-machine-learning-predicting-nfl-games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Bhattacharyya Senior Data Scientist Teachers Pay Teachers Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources. At TpT, Amit works on developing both technical and modeling infrastructure to analyze customer behavior and ways to more effectively connect buyers and sellers. Amit also teaches in the MIDS program at the UC Berkeley School of Information. He received a Ph.D. in physics from Indiana Universtiy. Previously, he did a two-year stint in advertising, and worked as a quantitative analyst at various banks and hedge funds for twelve years. In his spare time, he likes to plan skiing and backpacking trips, and dabble with machine learning algorithms for fantasy football.

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** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 50171 edureka!

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How can one create a fair compensation system and also predict the right compensation structure for a new hire? Marina and Geetanjali, participants of three months weekend HR Analytics program created a predictive wage parity model.

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As technology becomes more ubiquitous in our daily lives through laptops, phones and even wearable computers, the amount of data that can be collected about a person has grown exponentially. Most Importantly, this information can now be captured with little to no user involvement. The new problem facing computer scientists is how to analyze and interpret these ever growing data sets in a meaningful and efficient manner. The problem that we are attempting to solve is how to use these new progressions in technology to benefit both consumers and businesses by answering the question of "where should I eat?" The purpose of our project is two-fold. The first is to recommend restaurants to users with minimal interaction. While there are many restaurant finding applications on the market, they require users to input details about the user’s tastes and preferences. Our project will attempt to gather its data through sensors, such as GPS, and only require user-interaction when it is deemed absolutely necessary. The second purpose is to provide valuable metrics to restaurant owners about their clientele. By mining the same data that we are already collecting to make our recommendations, we can inform owners about their average customer: how often they visit, how long they stay, where else they frequent, etc.
Views: 65 muitprogram

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This K Means clustering algorithm tutorial video will take you through machine learning basics, types of clustering algorithms, what is K Means clustering, how does K Means clustering work with examples along with a demo in python on K-Means clustering - color compression. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K Means clustering work. Below topics are covered in this K-Means Clustering Algorithm Tutorial: 1. Types of Machine Learning? ( 07:08 ) 2. What is K Means Clustering? ( 00:10 ) 3. Applications of K Means Clustering ( 09:27 ) 4. Common distance measure ( 10:20 ) 5. How does K Means Clustering work? ( 12:27 ) 6. K Means Clustering Algorithm ( 20:08 ) 7. Demo In Python: K Means Clustering ( 26:20 ) 8. Use case: Color compression In Python ( 38:38 ) What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/B6k4R6 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Kmeans-Clustering-Algorithm-Xvwt7y2jf5E&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Kmeans-Clustering-Algorithm-Xvwt7y2jf5E&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning 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. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you 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. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 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 - - - - - - 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: 30122 Simplilearn

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Views: 11201 sandeep sharma

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Faculty candidates from 10 countries self-organize in teams and offer project ideas to discuss with expert audience. Select episode from the 3-day session at the School of Advanced Studies, University of Tyumen. See TIMELINE below. More on research at SAS here: https://sas.utmn.ru/en/research-en/ Video Timeline: 0:06 — Project Design Session Goals 3:20 — What Is Multidisciplinarity? 6:03 — Session Participants 6:52 — Brave New World: Co-Evolution of Technology and Humanity /technological vulnerabilities, Anthropocene, biohacking/ 22:50 — Rethinking Citizenship /existential belonging, alternatives, refugees/ 38:39 — Naked Soldier /violence, vulnerability, automated warfare/ 56:33 — Inner Life /loneliness, the public and the private, education/ 1:17:18 — Reimagining Citizenship /existential belonging, antiquity, concept and institutions/ 1:37:53 — First Contact /bacteria, whales, natives, aliens/ 2:04:35 — Communities /social disintegration, generations, gaps/ 2:16:08 — First Contact - 2 /rapport, empirical experiment, smell, sex, multidisciplinarity/ 2:34:45 — Reimagining Citizenship: Towards an Existential Belonging /big data, power structures, non-belonging, animals and humans/ 2:58:38 — Winning Projects 2:58:48 — New SAS Faculty 3:00:01 — Bonus: Duskin’s workout https://www.facebook.com/sas.utmn.en/ https://vk.com/sas_tmn https://www.instagram.com/sas_utmn/ #sas_research #sas_utmn #sas_tmn #utmn

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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 1341 Udacity

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Data Wrangling in R Course - Experfy.com R is an extraordinarily powerful language with a vast community of great resources, but where should you start when all you want to do is get your data into a usable format? How do you know your data might be ready? What are the pitfalls you should watch for so that you don’t perform an analysis on bad data? This course will teach you from start to finish how to get your data into R efficiently and polish it up so that it is as good as it can be. This course will give you real experience in the art and science of data preparation that you can take to your next real project forward with confidence Instructor is the founder of Analytics Incubation Center at Cisco and has 15 years of analytics development experience. Supported with office hours and a capstone project reviewed by the instructor Follow us on: https://www.facebook.com/experfy https://twitter.com/experfy https://experfy.com
Views: 189 Experfy

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Excel: Solver Participation Project for the Housing Problem
Views: 298 WVU CS101

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Get our Newsletter: UofLIPR.com/join Social media, along with its "event" organization and planning tools, plays an important role in connecting and engaging individuals and groups. These online spaces thrive with multi-faceted activities and interests which give rise to rich content and user interaction that often crossover to the world of events. For these reasons, the data trails associated with "events" in the virtual world, can be complex and challenging to understand and predict. This talk makes the case for building a data science pipeline to analyze event data and recommend relevant events to users with different preferences. The datasets for this challenge were provided by a competition on Kaggle. We conduct an extensive data analysis and exploration to help gain a better understanding of the data. We then proceed to the next most critical phase, which is feature engineering, storytelling and then on to fuzzy approximate reasoning-based modeling for computing event recommendations.  One particularly desirable property of fuzzy sets is their rich linguistic approximate reasoning ability which allows crunching through numbers and Big Data, while maintaining human interpretability of the built models and predictions. This interpretability is critical in the data science enterprise because data science often require team collaboration and yields results that need to be consumed by people of diverse technical and non-technical backgrounds, who therefore question the meaning of models and emphasize the importance of telling stories from the data. We have evaluated our proposed framework on a real world dataset with more than one million events and 38 thousand users. The proposed approach achieved accuracy of 70% which outperforms other existing event recommendation algorithms. Speaker Bio Mahsa  Badami is  Ph.D. candidate  in computer  science  at  the  department  of Computer  Engineering  and  Computer Science  (CECS),  University  of Louisville,  Louisville,  KY,  USA.  She  is currently  a  member  of  the  Knowledge Discovery  and  Web  mining  lab  and  her research  interests  include  recommender  systems,  text/data mining  and  data  science,  machine  learning.  She  has participated in several recommender systems and data science competitions.  In  addition,  she  has  published  several  research papers in various data mining and data science conferences. This talk was sponsored by the Department of Computer Engineering and Computer Science.
Views: 91 UofL Innovation

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Mark Davenport, Georgia Institute of Technology Real-Time Decision Making https://simons.berkeley.edu/talks/mark-davenport-2016-07-01
Views: 774 Simons Institute

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Dr. Catherine Stewart, Research Associate (University of Glasgow) Dr. Ruth Dundas, Senior Investigator Scientist (University of Glasgow) - Designing the study and applying for data - The data itself - UBDC projects: The health of looked after children in Scotland - Opportunities for publication Slides from presentation available to download here: http://ubdc.ac.uk/media/1452/section-2-stewart-and-dundas.pdf

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Crowd Emotion is a data science team solving an emotional problem. Crowd Emotion captures body language through smart devices to understand the link between emotions and choice. It offers a freemium, cloud-based solution that enables companies to emotionally understand and connect their brands, products and key stakeholders. Crowd Emotion supports the software with a thin services layer of data science and psychology expertise to help interpret emotional results relevant to client objectives. The ecosystem is then delivered directly to end clients or through a partner network. To find out more about Crowd Emotion, visit the Digital Catapult website: http://bit.ly/1z6hVFC
Views: 1311 Digital Catapult

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Views: 641 w wb

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SOLUTION LINK: http://libraay.com/downloads/custom_business_intelligence_capstone_project/ All tasks 1 - 10 SOLVED DISCOUNT CODE: WELCOME10 Task 1: (10 Points) Formulate a linear programming (LP) model that may be solved to identify the optimal production plan for the company in each time period. Specifically, you must define the decision variables, objective function, and constraints in your LP model using the following parameters: In each time period, for each product i∈(1,2,3): D_i is the demand (number of units required) for product i. C_i^R is the cost (in dollars) for producing each unit of product i in a regular run. C_i^S is the cost (in dollars) for producing each unit of product i in a special run. t_i^m is the machining time (in minutes) required to produce each unit of product i. t_i^a is the assembly time (in minutes) required to produce each unit of product i. t_i^f is the finishing time (in minutes) required to produce each unit of product i. Further, assume that: 300 hours of machining time is available for regular run. 240 hours of assembly time is available for regular run. 240 hours of finishing time is available for regular run. .........ALL TASKS SOLVED!!! Optimal LP Solution: Task 7: (10 Points) Solve the LP formulated in Task 1 using the parameters estimated in Tasks 2, 4, and 6 to determine the optimal production plan for period 53. Report the minimum production cost achievable, number of units of each product type to be produced under the regular and special production runs, and the resources used during regular run in the following format: Minimum cost attainable: Number of units produced S1 S2 S3 Regular Run Special Run Resources in regular run Minutes used MACHINE TIME ASSEMBLY TIME FINISH TIME Sensitivity Analysis: Task 8. (3+12 = 15 Points). Perform sensitivity analysis by changing one parameter at a time (leaving all other parameters fixed at the values used in Task 7) and answer the following questions. (a) By how much does the total production cost change as the demand for each product type changes by 1 unit? (b) At most how much should the company be willing to pay to (i) Increase the availability of machining time by one hour during regular run? (ii) Increase the availability of finishing time by one hour during regular run? (iii) Increase the availability of assembly time by one hour during regular run? Quality Control The text file “defective.csv” contains 2 columns. The first column DefectiveID is an identifier, and the second column SerialNo specifies the serial number of a defective product. Create a table DEFECTIVE with DefectiveID as its primary key and insert all 591 records from defective.csv into the table. Note that SerialNo in the DEFECTIVE table is a foreign key that references the primary key in the PRODUCTION table. The text file “quality.csv” contains 5 columns containing data from quality control tests run on 1500 batches of items produced. Create a table QUALITY with BatchNo as its primary key and Test1, Test2, Test3, and Test4 as its other 4 attributes. Insert all 1500 records from quality.csv into the table. Note that BatchNo in the PRODUCTION table is a foreign key that references the primary key BatchNo in the Quality table. Any batch that contains more than one defective items is deemed to be of poor quality; a batch with at most one defective item is considered to be of good quality. Task 9: (10 Points) Formulate an SQL query that lists all 5 columns from the QUALITY table and adds a derived column BatchQuality that contains “Poor” if the batch is of poor quality (contains at least 2 defective items) and “Good” otherwise. In your report, include: 1. The SQL query for task 9 2. The results of the query in a file batchQuality.csv. Task 10: (10 Points) Use the data obtained from Task 9 to train and test a Classification Tree that predicts BatchQuality based on values of the features Test1, Test2, Test3, and Test4. In your report: 1. Specify the rules that you obtained in Task 10 in the canonical form: IF …. THEN … 2. Present the classification accuracy of this set of rules in the form: Number of batches Actually Poor Quality Actually Good Quality Predicted Poor Quality Predicted Good Quality If you wish, you may also use other prediction and classification methods (such as Logistic Regression, Neural Nets, and Discriminant Analysis) to classify BatchQuality based on values of the features Test1, Test2, Test3, and Test4, and comment on the classification accuracy of these methods. Summary of deliverables: Deliverable Tasks Weight Due Date Project selection – 05% August 28, 2016 Mid-Term Report 1, 2, 3, 4, 5, & 6 50% October 8, 2016 Final Report 7, 8, 9, & 10 45% November 25, 2016
Views: 101 Libraay Downloads

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This Decision Tree in R tutorial video will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree-shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work. Below topics are explained in this Decision tree in R tutorial : 1. What is Decision tree? 2. What problems can be solved using Decision Trees? 3. How does a Decision Tree work? 4. Use case: Survival prediction in R Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/WsM21R Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning 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. What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you 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. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems We recommend this Machine Learning training course for the following professionals in particular: 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=Decision-Tree-in-R-HmEPCEXn-ZM&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: 6627 Simplilearn

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Learn more about how Rensselaer M.S. in Supply Chain Management students are succeeding in their capstone projects.
Views: 451 RPI Lally

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In this webinar, learn how Pivotal's Regunathan Radhakrishnan and Srivatsan Ramanujam helped a large financial services company evolve one of its customer-facing applications to meet the changing behavior and needs of users. Speakers: Regunathan Radhakrishnan, Srivatsan Ramanujam; Pivotal

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The Batten College of Engineering and Technology at ODU is the only engineering program in the country that offers an undergraduate degree in modeling, simulation and visualization engineering. From object-oriented programming, artificial intelligence, computer communications and computer graphics, to probability and statistics, data analysis and modeling human behavior, modeling and simulation is one of the fastest growing and most exciting fields in engineering. Learn more in this great video at: http://www.odu.edu/msve

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One of the best ways to learn data science is building a project. Pick a problem or question of interest to you and starting building something. However, what is the overall goal of this technique. 1. Experience 2. Education 3. Enjoyment Thanks for watching. If you have another question you would like me to address, please leave a comment.
Views: 323 Learn Data Science

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This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes video are as follows: 1. What is Naive Bayes? ( 01:06 ) 2. Naive Bayes and Machine Learning ( 05:45 ) 3. Why do we need Naive Bayes? ( 05:46 ) 4. Understanding Naive Bayes Classifier ( 06:30 ) 5. Advantages of Naive Bayes Classifier ( 20:17 ) 6. Demo - Text Classification using Naive Bayes ( 22:36 ) 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/Cw9wqy #NaiveBayes #MachineLearningAlgorithms #DataScienceCourse #DataScience #SimplilearnMachineLearning - - - - - - - - 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=Naive-Bayes-Classifier-l3dZ6ZNFjo0&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: 45758 Simplilearn

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**ORDER our new book: http://WeHaveNoIdea.com Adam Crymble describes his thesis of using Computer Science methods for historical analysis of Irish Immigrants in 19th century London, England. Subscribe to our channel: http://youtube.com/subscription_center?add_user=phdcomics More videos at PHDtv: http://www.phdcomics.com/tv Credits: Animation by: Jorge Cham and Meg Rosenburg Series Producer: Meg Rosenburg Adam Crymble, Kings College London

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Advances in data analytics and machine learning now allow the data silos to be analyzed, helping departments pinpoint not only where crime is likely to occur, but when and under what circumstances. Where do people gather after sporting events? What happens when the weather turns warm? How do patterns change when school is out? By leveraging computer models that take into consideration historical crime trends, demographics, climatology, geospatial information, and other data sets, law enforcement agencies can better plan where to deploy their resources. https://bismart.com/en/business-intelligence-solutions/crime-prediction/
Views: 223 Bismart

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Geography 565 Capstone Project at the University of Wisconsin-Madison Alissa Wilson, Brenna O'Halloran, Kirby Wright, Martin Brubaker December 2015
Views: 71 Alissa Wilson

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Daniel Bruckner is Co-Founder at Tamr. Held at the Haas School of Business, University of California, Berkeley, the Data Science & Strategy Lecture Series examines the evolving role of "big data" and analytics in managerial decision-making. In this playlist, lecture series host Prof. Greg La Blanc interviews industry executives and practitioners on key topics in data science, including data mining, machine learning, visualization, advanced statistics and more. For more information please visit: http://businessinnovation.berkeley.edu/data-science-strategy/lecture-series/.
Views: 435 Berkeley Haas