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Predicting the Winning Team with Machine Learning
 
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Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 102444 Siraj Raval
Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
 
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Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Views: 566391 MBAbullshitDotCom
Strategies for effective learning outcomes with students new to text mining and text analysis
 
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Kelvin Smith Library 2014 Digital Scholarship Colloquium Strategies for effective learning outcomes with students new to text mining and text analysis Mace Mentch, Consultant, Instructional Design and Technology 11/6/2014 Depending on the source, it has been estimated that 80% of existing data is in the form of unstructured text. The processes and methods used to transform unstructured textual data into structured data through turning the text into numbers and then back into text to discover relationships and create knowledge is complex. This presentation will cover methods derived from instructional systems design that serve to effectively facilitate student learning outcomes for the text mining and text analysis process. Colloquium website: http://library.case.edu/ksl/freedmancenter/colloquium/2014colloquium/
Views: 81 case
Pallab Bhattacharya, Aegis Alumni sharing experience @Aegis School of Data Science
 
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Pallab Bhattacharya, Associate Director Business Excellence at Edelweiss Financial Services sharing experience @Aegis School of Data Science Education Qualification: Bachelor's Degree in Mechanical Engineering, Jadavpur University; Convocation ceremony convened by Aegis School of Business, Data Science and Telecommunication to confer the Post Graduate Program in Data Science, Business analytics and Big Data in association with IBM at The Park Hotel in Navi Mumbai on 24th June 2018. Convocation proceedings were carried by Dr. Abhijit Gangopadhyay, Dean, Aegis School. He congratulated and awarded PGP certification by Aegis and IBM to all the graduating students. Dr. Paul Pallath, Chief Data Scientist & Director - Advanced Analytics at SAP and Dr. Subramani Ramakrishnan, Lead, Resource & Capacity Management at IBM Global Business Services delivered the Convocation address to graduating students. Meet participants of Aegis School of Data Science's PGP/EPGP in Data Science, Business Analytics & Big Data in association with IBM. Get the Best Brains trained and certified jointly by IBM and Aegis having skills and competency in Data Science, Business Analytics, Big Data, Machine Learning, AI, Natural Language Process (NLP), Text Mining, Data Mining, Cognitive Computing, Hadoop, Spark, IBM Watson, IBM Cognos, Infosphere Big Insight, IBM SPSS, SAS, Tableau etc Write to Taranjit Oberai at [email protected] for your talent needs. Check Full Time PGP in Data Science, Business Analytics & Big Data in association with IBM at https://www.muniversity.mobi/PGP-DataScience/ Part Time Executive Weekend program in Mumbai, Pune, Bangalore, https://www.muniversity.mobi/Weekend-EPGP-DataScience/ Online Executive PGP Program worldwide https://www.muniversity.mobi/Online-EPGP-DataScience/ About Aegis Aegis is a leading higher education provider in the field of Telecom, Data Science, Business Analytics, Big Data, Machine Learning, Deep Learning and Cyber Security. Aegis was started in 2002 with the support of Airtel Bharti, among the top five mobile operators to develop the cross functional techno-business leaders. Aegis is the number one school for Data Science and among the top five for business analytics in India. It has campuses in Mumbai, Pune and Banaglore. Aegis & IBM jointly delivers full time and Executive Post Graduate Program/MS in Data Science, Business Analytics, Big Data and Cyber Security. Aegis offers Deep Learning courses in partnership with NVIDIA. Find more about Aegis at www.aegis.edu.in www.mUniversity.mobi/Aegis
Views: 1348 Aegis TV
Data Science in 8 Minutes | Data Science for Beginners | What is Data Science? | Edureka
 
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** Data Scientist Masters Program: https://www.edureka.co/masters-program/data-scientist-certification ** This edureka video on Data Science will introduce you to the concepts of Data Science and how it is used to solve real-world problems. You will learn how Data Science works with an example on UBER. Data is everywhere, and its growing at an exponential rate. So, Data science is the process of using the data to find solutions / to predict outcomes of a problem statement. Below are the topics covered in this video: 0:57 What is Data Science? 1:11 How Data Science works? : Data Science at UBER 2:06 Data Science Process a. Business Requirements b. Data Collection c. Data Cleaning d. Data Exploration and Analysis e. Data Modelling f. Data Validation g. Deployment and Optimization 4:17 Data Science Applications 6:05 Who is a Data Scientist? 6:18 Data Scientist Job Trends 6:51 Data Scientist Skills 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 #Datasciencein8minutes #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: 19828 edureka!
TaStEBoOk
 
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TasteBook is a system that matches the preferences of known dinners to recommend an optimal cooking menu for the desired experience. TasteBook is the outcome of the two weeks Data mining and design module in the USI program at TU/e.
Views: 85 rashtrojos
Applications of Predictive Analytics in Legal | Litigation Analytics, Data Mining & AI | Great Lakes
 
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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
Sentiment Analysis in 4 Minutes
 
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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 105503 Siraj Raval
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Python | Edureka
 
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** NIT Warangal Post Graduate Program on AI and Machine Learning: https://www.edureka.co/nitw-ai-ml-pgp ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial: 1. AI vs Machine Learning vs Deep Learning 2. What is Artificial Intelligence? 3. Example of Artificial Intelligence 4. What is Machine Learning? 5. Example of Machine Learning 6. What is Deep Learning? 7. Example of Deep Learning 8. Machine Learning vs Deep Learning Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: 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 Telegram: https://t.me/edurekaupdates - - - - - - - - - - - - - - - - - #edureka #AIvsMLvsDL #PythonTutorial #PythonMachineLearning #PythonTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: "I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the "customer of Edureka!". Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working."
Views: 514586 edureka!
Complete Data Science Course | What is Data Science? | Data Science for Beginners | Edureka
 
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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: 49277 edureka!
Using Machine Learning for Predicting NFL Games | Data Dialogs 2016
 
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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.
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
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** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ ------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learned content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 57363 edureka!
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
 
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** 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: 76591 edureka!
Lecture 01
 
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introduction to Business analytics and data mining modeling using R studio Discussions on key terms used for data mining finally discuss the course roadmap
13. Classification
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 43901 MIT OpenCourseWare
Business Analytics Course | A Roadmap to Business Analytics - Tools, Techniques & Applications
 
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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: 136616 Great Learning
Lecture 17 - Three Learning Principles
 
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Three Learning Principles - Major pitfalls for machine learning practitioners; Occam's razor, sampling bias, and data snooping. Lecture 17 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 29, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 67976 caltech
Predicting Future Data - Intro to Data Science
 
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This video is part of an online course, Intro to Data Science. Check out the course here: https://www.udacity.com/course/ud359. 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: 2895 Udacity
Great Learning Business Analytics Certificate Program: Build a career in Analytics | Great Lakes
 
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#BusinessAnalytics | 6 Months online Business Analytics Certificate Program with certification provided from Great Lakes Institute of Management. The Business Analytics Certificate Program enables participants to gain an in-depth understanding of analytics techniques and tools that are widely used by companies globally from the convenience of their homes without any location constraints. The BACP is designed to cover essential topics in business analytics that are required to break into a career in analytics with minimal disruption to a professional’s work life. Learning material is provided online followed up with office hours (live instructor-led webinars) for doubt clearing and case study illustrations. Have a look at our curriculum : 1. Program Overview 2. Getting Started with R and SAS 3. Fundamentals of Statistics for Business Analytics 4. Advanced Excel Analysis 5. Data Mining using Decision Trees 6. Data Mining using Cluster Analysis in R and SAS 7. Time Series Forecasting 8. Predictive Modeling - Logistic Regression using SAS 9. Predictive Modeling - Logistic Regression using R 10.Applications of Analytics and Other Topics For detailed curriculum or more details about the program, please visit: https://classroom.greatlearning.in/courses/302/show_case #BusinessAnalyticsCourse #Analytics #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/ 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: 1798 Great Learning
Stephen Hawking, Data Science Insights | PG Diploma in Data Science | Manipal ProLearn
 
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In this video on Stephen Hawking, Data Science Insights, we’ll be discussing about the teachings of Hawking about Data Science Stephen Hawking was Science’s brightest star, who shaped modern cosmology and left behind a legacy of budding data scientists Consider data as a playground and play with it instead of fearing it Big Data is responsible for scientific advancements and innovations in various fields from education to healthcare Data Science is an intriguing field which requires knowledge and a specialised skillset ------------------------------------------------------------------------------------------------------- PG Diploma in Data Science: https://www.manipalprolearn.com/data-science/post-graduate-diploma-in-data-science-full-time-manipal-academy-higher-education?utm_source=YouTube&utm_medium=YouTube&utm_campaign=YouTube&utm_term=YouTube&utm_content=Youtube ------------------------------------------------------------------------------------------------------- Subscribe to our channel to get video updates. Hit the subscribe button above https://www.youtube.com/channel/UCllnb6S5fPzpVYcV8KYzhnA?view_as=subscriber?sub_confirmation=1 Also follow us on other channels Facebook: https://www.facebook.com/ManipalProLearn Twitter: https://twitter.com/manipalprolearn LinkedIn: https://www.linkedin.com/company/10147429 ------------------------------------------------------------------------------------------------------- About the course The PG Diploma in Data Science offered by Manipal ProLearn is a full-time training program that will help you learn data science from the foundation. It includes a 3-month internship program. ------------------------------------------------------------------------------------------------------- Learning outcomes: Learning AI and neural network for the creation of intelligent machines Performing modelling, predictive analysis, Data analysis, and storytelling Learn and apply unstructured data analysis and robotic process Applying R, Excel, Python, SQL, Apache, and Hadoop techniques in real-world problems ------------------------------------------------------------------------------------------------------- Who can attend this course? Anyone who is interested in building a career in Data Science Professionals who wish to switch their careers -------------------------------------------------------------------------------------------------------
Views: 190 Manipal ProLearn
Using Social Media Data for Analytics | Social Media Analytics | Business Analytics Case Study
 
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Matthieu Garnier, Head of Data & Analytics - Equifax, talks about Social Media Analytics and how social media data can be used to draw insights for taking business decisions. Equifax is Manipal ProLearn's Industry Partner for the PG Diploma in Data Science program http://bit.ly/2W7G5kk About the program Manipal ProLearn’s PG Diploma in Data Science program is designed to provide you with a broad understanding of the basic and advanced concepts of Data Science. The Data Science training will enable you to implement Big Data techniques using tools using R, Excel, Tableau, SQL, NoSQL, Hadoop, Pig, Hive, Apache Spark and Storm. After completing the Data Science diploma, you’ll be considered as a strong and competent data scientist. The course will help you to: Perform data analysis, modelling, predictive analysis, and storytelling through data visualization, which is crucial to business decision-making. Analyze data sets to summarize their main characteristics, often with visual methods with Exploratory Data Analysis Understand and use Big Data technologies as enablers to deploy enterprise information management and solve business problems Learn artificial intelligence and neural network that emphasizes the creation of intelligent machines. Apply the methods, tools and techniques to real-world problems by leveraging technologies such as R, Python, Excel, SQL, NoSQL, Tableau, Hadoop, Pig, Hive, Apache. Spark and Storm, and other open source and proprietary products as well. Communicate analytics problems, methods, and findings effectively verbally, visually, and in writing. Become more accurate in predicting outcomes without Machine Learning. Cleaning and unify messy and complex data set with Data Scrapping and Data Wrangling Help Companies make critical decisions through analysis, modeling, visualization, etc. Learn the emerging data science of unstructured data analysis and robotic process automation by choosing an elective based on your area of interest. Term 1: Programming for Data Science Data Scrapping and Data Wrangling Statistical techniques for Data Science Machine Learning Data Analysis and Visualisation Big Data Technologies Term 2: Artificial Intelligence Elective 1 (Banking Analytics or Marketing Analytics) Elective 2 (Unstructured Data Analysis/Robotic Process Automation) Project/Internship Manipal Placement Guarantee Manipal ProLearn promises that every student successfully completing the academic requirements of the PG Diploma in Data Science from Manipal Academy of Higher Education (MAHE) and conforming to the program’s disciplinary norms will be placed by the end of the program. A student is considered to be placed when he/she receives an offer letter for a paid position from a company. ---------------------------------------------------------------------------------------------------------------------------------- Subscribe to our channel to get video updates. https://www.youtube.com/channel/UCllnb6S5fPzpVYcV8KYzhnA?view_as=subscriber?sub_confirmation=1 Also follow us on other channels: Facebook: https://www.facebook.com/manipalprolearn/ Twitter: https://twitter.com/manipalprolearn LinkedIn: https://www.linkedin.com/company/manipal-prolearn/
Views: 281 Manipal ProLearn
#KnowledgeBytes: Artificial Intelligence - Supervised & Unsupervised Learning
 
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In this video, Shreya Modak, a DSP Student of Imarticus Learning, explains the two aspects of Artificial Intelligence - Supervised and Unsupervised learning, with examples. She explains that supervised learning is the most common data type of Machine Learning that is further divided into regression or classification. Regression defines the relationship variables, in which one is an input variable and one is a response variable. Regression has two or more variables - dependent and independent. In classification, we have a dependent variable and the outcome will be either a yes or no. Shreya also elaborates that in unsupervised learning, there is no target variable. There are input variable and training data set. It is used for finding patterns and structures in the data. She describes unsupervised learning with a detailed example of K-means clustering, which is the most widely used algorithm and one of its key platforms is Facebook. Check our complete #ImarticusPrograms playlist here: http://bit.ly/2JP52hM Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - - - - Why Imarticus? Imarticus Learning offers a comprehensive range of professional Financial Services and Analytics programs that are designed to cater to an aspiring group of professionals who want a tailored program on making them career ready. Our programs are driven by a constant need to be job relevant and stimulating, taking into consideration the dynamic nature of the Financial Services and Analytics market, and are taught by world-class professionals with specific domain expertise. Headquartered in Mumbai, Imarticus has classroom and online delivery capabilities across India with dedicated centres located at Mumbai, Bangalore, Chennai, Pune, Hyderabad, Coimbatore and Delhi. For more information, please write back to us at [email protected] Call us at IN: 1-800-267-7679 (toll free) - - - - - - - - - - - - - - - - - Facebook: https://bit.ly/2y6UjKW Twitter: https://bit.ly/2J11llx LinkedIn: https://bit.ly/2xwSoPM
Views: 190 Imarticus Learning
Saving Lives Using Biomedical Data Science! | Dr. Shameer Khader | TEDxGCEKannur
 
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Dr. Shameer Khader is a biomedical and healthcare data scientist. He uses a combination of big data, machine intelligence and bioinformatics approaches for developing personalized, precision medicine solutions. In this talk, Dr. Khader discusses three examples of how large-scale molecular and clinical data could be integrated and analyzed to develop a new approach for risk stratification, diagnoses, and therapeutic development. First, he illustrates the application of genomic data to stratify patients at risk for myocardial infarction using a genomic risk score; he then shows an example of using imaging data and machine learning algorithms to diagnose heart diseases that typically require several years or experience and diagnostic turnaround of several hours in a few seconds; finally he discusses how computational drug repositioning could an important strategy for discovering rapid therapeutic recommendations in the setting of public health crises like Nipah Virus outbreak in Kerala. Collectively, his work could improve clinical outcomes for various disease modalities and reduce the overall cost of care and optimize healthcare delivery. Computational Biologist This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Views: 736 TEDx Talks
SPSS for questionnaire analysis:  Correlation analysis
 
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Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation. 0:00 Introduction to bivariate correlation 2:20 Why does SPSS provide more than one measure for correlation? 3:26 Example 1: Pearson correlation 7:54 Example 2: Spearman (rhp), Kendall's tau-b 15:26 Example 3: correlation matrix I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation. Watch correlation and regression: https://youtu.be/tDxeR6JT6nM ------------------------- Correlation of 2 rodinal variables, non monotonic This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative. Good luck
Views: 524332 Phil Chan
Meet Julio Cardenas-Rodriguez - Springboard Data Science Alumni Series
 
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A senior data scientist at Banner Healthcare, Julio Cardenas-Rodriguez shares why he chose Springboard, how he stayed motivated during the course, and the value his mentor provided in accelerating his learning process. Springboard is an online school for learning 21st-century skills in fields like data science, UX design, and cybersecurity. We've served thousands of students around the world through a combination of expert-curated curricula and one-on-one mentorship. In all of our self-paced courses, Springboard students develop job-ready skills that they display in industry-worthy capstone projects. And throughout the entire process, students receive support from their industry mentor, as well as Springboard's resident advisors and the larger student/alumni community. Our Data Science Career Track comes with a job guarantee—land a job within six months of graduation or get a refund. And through our deferred tuition program, Data Science Career Track students can enroll for a small deposit and finish paying after starting a job. More here: https://learn.springboard.com/deferred-tuition/ Data Science Career Track graduates who accepted new jobs reported an average annual salary increase of $19,919. And 70 percent of those students received their offer before graduating from the course. Find out more: https://workshops.springboard.com/student-outcomes/ Stay connected to the Springboard community: Facebook - https://www.facebook.com/springboard Twitter - https://twitter.com/springboard Instagram - https://www.instagram.com/springboardlife/ LinkedIn - https://www.linkedin.com/school/springboard/ Visit Springboard’s blog for more alumni and mentor profiles: https://www.springboard.com/blog/ Go to https://www.springboard.com/ for more on all of our courses.
Views: 290 Springboard
R tutorial: Data splitting and confusion matrices
 
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Learn more about credit risk modeling in R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r We have seen several techniques for preprocessing the data. When the data is fully preprocessed, you can go ahead and start your analysis. You can run the model on the entire data set, and use the same data set for evaluating the result, but this will most likely lead to a result that is too optimistic. One alternative is to split the data into two pieces. The first part of the data, the so-called training set, can be used for building the model and the second part of the data, the test set, can be used to test the results. One common way of doing this is to use two-thirds of the data for a training set and one-third of the data for the test set. Of course there can be a lot of variation in the performance estimate depending which two-thirds of the data you select for the training set. One way to reduce this variation is by using cross validation. For the two-thirds training set and one-third test set example, a cross validation variant would look like this. The data would be split in three equal parts, and each time, two of these parts would act as a training set, and one part would act as a test set. Of course, we could use as many parts as we want, but we would have to run the model many times if using many parts. This may become computationally heavy. In this course, we will just use one training set and one test set containing two-thirds versus one-third of the data, respectively. Imagine we have just run a model, and now we apply the model to our test set to see how good the results are. Evaluating the model for credit risk means comparing the observed outcomes of default versus non-default--stored in the loan_status variable of the test set--with the predicted outcomes according to the model. If we are dealing with a large number of predictions, a popular method for summarizing the results uses something called a confusion matrix. Here, we use just 14 values to demonstrate the concept. A confusion matrix is a contingency table of correct and incorrect classifications. Correct classifications are on the diagonal of the confusion matrix. We see, for example, that 8 non-defaulters were correctly classified as non-default, and 3 defaulters were correctly classified as defaulters. However, we see that 2 non-defaulters where wrongly classified as defaulters, and 1 defaulter was wrongly classified as a non-defaulter. The items on the diagonals are also called the true positives and true negatives. The off-diagonals are called the false positives versus the false negatives. Several measures can be derived from the confusion matrix. We will discuss the classification accuracy, the sensitivity and the specificity. The classification accuracy is the percentage of correctly classified instances, which is equal to 78.57% in this example. The sensitivity is the percentage of good customers that are classified correctly, or 75% in this example. The specificity is the percentage of bad costomers that are classified correctly, or 0.80 in this example. Let's practice splitting the data and constructing confusion matrices.
Views: 15586 DataCamp
K-Means Clustering - The Math of Intelligence (Week 3)
 
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Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this. Code for this video: https://github.com/llSourcell/k_means_clustering Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html http://people.revoledu.com/kardi/tutorial/kMean/ https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html http://mnemstudio.org/clustering-k-means-example-1.htm https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 107417 Siraj Raval
Delete Facebook Campaign | Misuse of Facebook data | Manipal ProLearn
 
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In this video on #DeleteFacebook Campaign , we’ll be discussing about the misuse of Facebook data to influence the 2016 US Elections and how data scientists need to be responsible while using data for analysis: 1. With great power comes great responsibility 2. Set clear data mining boundaries 3. Always have a Plan B --------------------------------------------------------------------------------------------------- PG Diploma in Data Science: https://www.manipalprolearn.com/data-science/post-graduate-diploma-in-data-science-full-time-manipal-academy-higher-education?utm_source=YouTube&utm_medium=YouTube&utm_campaign=YouTube&utm_term=YouTube&utm_content=Youtube --------------------------------------------------------------------------------------------------- Subscribe to our channel to get video updates. Hit the subscribe button above https://www.youtube.com/channel/UCllnb6S5fPzpVYcV8KYzhnA?view_as=subscriber?sub_confirmation=1 Also follow us on other channels Facebook: https://www.facebook.com/ManipalProLearn Twitter: https://twitter.com/manipalprolearn LinkedIn: https://www.linkedin.com/company/10147429 --------------------------------------------------------------------------------------------------- #DeleteFacebookCampaign #DataScienceTutorial #PGDDSCourse About PG Diploma in Data Science This PG Diploma in Data Science is a full-time analytics course for all graduates with a 50% aggregate and/or with 0 to 3 years of work experience. Students will learn from experts with nearly a decade of experience in data science and analytics. --------------------------------------------------------------------------------------------------- Learning objectives of the PG Diploma in Data Science • Perform data analysis, predictive analysis, storytelling and modelling through data visualization • Make use of Big Data technologies for information management and solving business problems • Learn artificial intelligence and neural network • Orally, visually and verbally communicate analytics problems, methods, and findings • Accurately predict outcomes without using Machine Learning --------------------------------------------------------------------------------------------------- Why Learn Data Science at Manipal ProLearn? • Get trained in implementing Big data techniques with a 3-month internship • Guaranteed placement or 30,000 per month financial support for 3 months • Get a certification from Manipal Academy of Higher Education, an institution of eminence • Get placed with salary packages as high as 9.35 lakhs ---------------------------------------------------------------------------------------------------
Views: 142 Manipal ProLearn
Lecture 02 - Is Learning Feasible?
 
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Is Learning Feasible? - Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 5, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 344070 caltech
The Path To Self-Service Analytics: A Success Story by Somesh Saxena, GE
 
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This talk was part of Dataiku's EGG NYC 2018 Conference. The Path To Self-Service Analytics: A Success Story by Somesh Saxena, Technical Product Manager at GE Aviation: The Self-Service Data program at General Electric Aviation has truly enabled the democratization of data and empowered business users to transform and analyze data through the implementation of data cataloging, workflow and visualization tools to drive horizontal outcomes and build data products for the digital industrial company. The program started in late 2016 when the Self-Service Data team from GE Aviation’s Digital Technology group rolled out the Self-Service Data tools. The team partnered with other organizations within the business, such as: engineering, supply chain, sales and marketing, and others, to identify and execute on projects within each group’s domain. Initial training sessions, and open office hours provided by the Self-Service Data team, helped user adoption and provided a sense of ease for non-technical users to work in the shared eco-system of GE’s data lake. Digital Data Analyst, an intensive week-long course teaching digital tools, data science and process excellence, was introduced in 2018. The training program was met with instant success, with over 700 graduates from multiple areas across the business. With a community of over 1,400 Self-Service developers building digital products to make data-driven decisions, the program is front and center of the digital cultural transformation at General Electric Aviation. Somesh Saxena is the Product Owner of Alation and Dataiku at General Electric Aviation. He manages a team of full-stack data engineers and helps lead the Self-Service Data program. Somesh is front and center of the digital cultural transformation at General Electric Aviation, training employees through the Digital Data Analyst training. He began his career with General Electric’s Digital Technology Leadership Program, where he led projects for the company’s customer portal, did full-stack web development in Cyber Security, and data ingestion, engineering and visualization in the data analytics space. Somesh is a Certified Scrum Product Owner from the Scrum Alliance. He holds a degree in Business Administration with a concentration in Information Systems from the University of Cincinnati.
Views: 127 Dataiku
Learning analytics and scientific simulations
 
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Transforming Assessment Webinar 10 December 2014 "Learning analytics to understand student learning strategies and outcomes: a study involving scientific simulations" Presenter: Barney Dalgarno (Charles Sturt University, Australia) An ongoing challenge in an online or blended learning context is the provision of formative feedback to students when they undertake online learning or assessment tasks independently. Often we are limited to providing feedback on their learning outcomes through an assessment of the products created during their learning or of performance on exams. Feedback on learning strategies is much more difficult when the learning is undertaken independently online or away from the campus. The emerging area of learning analytics has great potential in addressing this through techniques that allow us to draw upon and analyse data collected during online activities. This seminar reported on a study in which initial conclusions about the relative merits of two online learning designs were thrown into question once student learning strategies, visible through learning analytic techniques, were analysed. The study compared learning outcomes from exploration and manipulation of computer-based scientific simulations with the outcomes from the presentation of simulation output. A key implication of the study is that in order to understand the learning resources and support our students need when undertaking online learning activities we need a deeper understanding of the strategies they adopt. As well as describing the learning resources, experimental results and findings from this study, the seminar discussed the broader question of how we can scrutinise student learning strategies in these kinds of online tasks. Various alternative approaches to analysing student online learning log file data was also discussed along with the potential for the use of such methods to underpin the provision of dynamic support for students based on an automated characterisation of their learning strategies. This session was hosted by Professor Geoff Crisp, RMIT University and Dr Mathew Hillier, University of Queensland, Australia starting 07:00AM UST/GMT. Duration 58 minutes. Further information and resources for this session: http://www.transformingassessment.com/events_10_december_2014.php See more about e-assessment and webinars at Transforming Assessment: http://transformingassessment.com
Predictive Analytics, Machine Learning, and Recommendation Systems on Hadoop
 
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Originally recorded January 30, 2014. In the world of ever growing data volumes, how do you extract insight, trends and meaning from all that data in Hadoop? Do you need help transforming your big data into big knowledge? Organizations know that the key to competitive advantage is in using advanced analytics to discover trends and use them to your advantage faster than the competition. Getting relevant information from big data requires a different approach. Churning out a couple of analytical models a week isn't going to cut it. If you're using big data to identify trends, spot weaknesses and predict outcomes, you need proven analytical software that's a lot faster, more efficient, accurate, and easy to use. Learn more about how to reveal insights in your Big data and redefine how your organization solves complex problems. You will learn how to: Use sophisticated analytics in both a visual interface and a coding interface. Prepare, explore and model multiple scenarios using data volumes never before possible to generate accurate and rapid insights. Interact with the data to add or drop variables into the model and instantly see how their influence provides increased predictive power. Easily perform modeling tasks interactively and on-the-fly Quickly understand your model fit with model diagnostics - interactively and in real time (typically in seconds instead of hours or days). Ask what-if questions on all the data. Use a scalable recommendation system to help improve customer experience through profiling users and items and finding how to relate them About Wayne Thompson Wayne Thompson is the Manager of SAS Predictive Analytics Product Management at SAS.Over the course of his 20-year tenure at SAS he has been credited with bringing to market landmark SAS analytics technologies (SAS Text Miner, SAS Credit Scoring for Enterprise Miner, SAS Model Manager, SAS Rapid Predictive Modeler, SAS Scoring Accelerator for Teradata, and SAS Analytics Accelerator for Teradata). Current focus initiatives include easy to use self-service data mining tools for business analysts, decision management and massively parallel high performance analytics. Wayne received his Ph.D. and M.S from the University of Tennessee in 1992 and 1987, respectively. During his PhD program, he was also a visiting scientist at the Institut Superieur d'Agriculture de Lille, Lille, France. Georgia Mariani is Principal Product Marketing Manager for Statistics at SAS. She drives marketing direction for SAS' statistics software initiatives. Georgia began her career at SAS as a systems engineer, consulting with sales prospects in the government and education industries regarding their analytical business questions and implementing SAS software and solutions. Georgia received her M.S. degree in Mathematics with a concentration in Statistics in 1996 and her B.S. degree in Mathematics in 1992 from the University of New Orleans. During her Master's program she was awarded a fellowship with NASA. Produced by: Yasmina Greco Don't miss an upload! Subscribe! http://goo.gl/szEauh - Stay Connected to O'Reilly Media. Visit http://oreillymedia.com Sign up to one of our newsletters - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 3406 O'Reilly
Decision Analysis 3: Decision Trees
 
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This brief video explains *the components of the decision tree *how to construct a decision tree *how to solve (fold back) a decision tree. Other videos: Decision Analysis 1: Maximax, Maximin, Minimax Regret https://youtu.be/NQ-mYn9fPag Decision Analysis 1.1 (Costs): Maximax, Maximin, Minimax Regret https://youtu.be/ajkXzvVegBk Decision Analysis 2.1: Equally Likely (Laplace) and Realism (Hurwicz) https://www.youtube.com/watch?v=zlblUq9Dd14 Decision Analysis 2: EMV & EVPI - Expected Value & Perfect Information https://www.youtube.com/watch?v=tbv9E9D2BRQ Decision Analysis 4: EVSI - Expected Value of Sample Information https://www.youtube.com/watch?v=FUY07dvaUuE Decision Analysis 5: Posterior Probability Calculations https://youtu.be/FpKiHpYnY_I
Views: 220420 Joshua Emmanuel
Data Analytics in Health – From Basics to Business | KULeuvenX on edX | Course About Video
 
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Improve diagnostics, care and curing by effectively applying data analytics in healthcare and spot entrepreneurial opportunities. Take this course free on edX: https://www.edx.org/course/data-analytics-health-basics-business-kuleuvenx-dahx#! ABOUT THIS COURSE Many people talk about the promise of “big data” to health care. But how can the application of data analytics to big data actually improve health and health care? We will show that novel data analytics based solutions can result in better diagnosis, better care and better curing. This provides fertile ground for entrepreneurship and the development of new businesses. In our course we’ll start from the very basics of data analytics, look at different real world approaches and help you to see entrepreneurial opportunities and develop a business plan. We will cover three important fields: - Health care expertise: We will present medical approaches to data and give an overview of challenges where big data based solutions have been developed to improve the efficiency and effectiveness in medicine. - Data analytics: We’ll explain the basics of data mining within the context of a wide variety of health care settings, and the types of data and data analysis challenges that you will likely encounter in each. We’ll start with gathering the data, move on to classifying, analyzing and finally visualizing it. - Entrepreneurship: You will learn how to assess when data sciences based improvements in health care represent entrepreneurial opportunities. The development of a rigorous business plan is used to help you make that assessment. Participants with prior experience in the medical field will learn how novel data science applications can improve healthcare, create societal value and how to spot entrepreneurial opportunities. Participants with experience in data science or mathematics will learn about medical approaches to data and why healthcare is an exciting area to apply and develop data analytics. Participants interested in launching their startup will learn how big data solutions in health care can provide a solid basis to build great ventures. Whatever your motivation to enrol in this course, we care about your project and your success - that’s why we will guide you through all parts of this learning journey step by step! Enter now to see how you can engage in data driven innovation and make an impact on improving care, outcomes and the quality of life. WHAT YOU'LL LEARN - How healthcare data analysis can be used to improve diagnosis, curing and caring - How to acquire, transform, classify, mine and visualize data - How to identify data analytics based entrepreneurial opportunities in healthcare and quantify it’s economic value - How to improve entrepreneurial opportunities and to create a rigorous business plan for your start up
Views: 1984 edX
How NLP text mining works: find knowledge hidden in unstructured data
 
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Connect with us: http://www.linguamatics.com/contact What use is big data if you can't find what you're looking for? Follow: @Linguamatics https://twitter.com/Linguamatics https://www.linkedin.com/company/linguamatics https://www.facebook.com/Linguamatics https://plus.google.com/+Linguamatics https://www.youtube.com/user/Linguamatics/videos In knowledge driven industries such as the life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80% of available information exists as unstructured text, and conventional keyword searches only retrieve documents, which still have to be read. This is very time consuming, unreliable, and, when important decisions rest on it, costly. Linguamatics’ text mining solution, I2E, uses Natural Language Processing to identify and extract relevant knowledge at least 10 times faster than conventional search, often uncovering insights that would otherwise remain unknown. I2E analyses the meaning of the text using powerful linguistic algorithms, enabling you to ask open questions, find the relevant facts and identify valuable connections. Going beyond simple keywords, I2E can recognise concepts and the different ways the same thing can be expressed, increasing the recall of relevant information. I2E then presents high quality results as structured, actionable knowledge, enabling fast review and analysis, and providing dramatically improved speed to insight. Our market leading software is supported by highly qualified domain experts who work with our customers to ensure successful project outcomes. Text mining for beginners: https://www.youtube.com/watch?v=40QIW9Sr6Io
Views: 17016 Linguamatics
TLT Symposium 2013 Lunch Keynote (edited)
 
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Interest in big data, data mining, and analytics is strong and growing in business and government. Recent reports by McKinsey, HBR, and Deloitte indicate that big data and analytics are just beginning beginning to make their impact in many sectors. The tools and methods of analytics are developing rapidly and are increasingly easier to use. In education, the adoption of analytics has been slow, and when initiated, often focused on improving organizational processes or identifying at-risk-learners. Analytics hold significant value in improving the spectrum of the teaching and learning process, not only for targeting a particular variable. This presentation will review the context that's driving popularity of analytics, provide cases and examples of use in education, and argue for the use of proactive models that emphasizes improving the learning experience, instead of only reacting to warning signs.
Views: 3895 Penn State TLT
CodeX | Professor Harry Surden Discusses Machine Learning within Law
 
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On October 9, 2014, CodeX: The Stanford Center for Legal Informatics and the Stanford Program in Law, Science & Technology welcomed University of Colorado Law School Associate Professor Harry Surden to campus for a CodeX Speaker Series discussion on Machine Learning within Law. Surden spoke on current and future applications of machine-learning within law, and explored automation in the context of legal tasks currently performed by attorneys, including predicting the outcomes of legal cases, finding hidden relationships in legal documents and data, electronic discovery, and the automated organization of documents.
Views: 4171 stanfordlawschool
R programming for beginners – statistic with R (t-test and linear regression) and dplyr and ggplot
 
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R programming for beginners - This video is an introduction to R programming. I have another channel dedicated to R teaching: https://www.youtube.com/c/rprogramming101 In this video I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.
17. Learning: Boosting
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston Can multiple weak classifiers be used to make a strong one? We examine the boosting algorithm, which adjusts the weight of each classifier, and work through the math. We end with how boosting doesn't seem to overfit, and mention some applications. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 181229 MIT OpenCourseWare
Transforming Data - Data Analysis with R
 
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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: 57923 Udacity
From cells to drug responses - machine learning in cancer research - Julian de Ruiter, Nanne Aben
 
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PyData Amsterdam 2018 Machine learning plays an important role in cancer research. In this talk, we’ll tackle the challenge of predicting which patients are likely to respond to given anti-cancer treatments. In doing so, we’ll show how tools such as Snakemake/Bioconda can be used to create reproducible workflows and illustrate the challenges of interpreting predictive models in large, highly-correlated feature spaces. Slides: https://www.slideshare.net/PyData/from-cells-to-drug-responses-machine-learning-in-cancer-research-julian-de-ruiter-nanne-aben -- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 318 PyData
Learning Analytics and the Academic Library 10 11 2017
 
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Higher education institutions are increasingly looking to mine student data in order to gain new, actionable insights into student behaviors using learning analytics technologies. Purportedly, these insights can help institutions improve student learning outcomes, increase student engagement, decrease time-to-degree measures, and ameliorate graduation rates. While on the face of it these aims are worthy of the resource expenditures necessary to build capacity for and implement learning analytics practices, there are serious serious threats to long-held values. Student privacy, academic and intellectual freedom, and the trustworthy relationships necessary for successful teaching and learning experiences are all affected by data mining practices that dig into and expose intellectual and social behaviors represented in a wide variety of data. This webinar will discuss the on-the-ground practices of learning analytics, how learning analytics specifically threatens these values, and why institutional actors–such as faculty, librarians, and advisors–should take notice. Special emphasis will be placed on particular concerns that arise when libraries participate in learning analytics with relationship to ALA’s Code of Ethics. Presenter: Kyle Jones / Indiana University – Purdue University Indianapolis School of Informatics and Computing 1 LEU
Linear Regression - Least Squares Criterion  Part 1
 
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Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Linear Regression - Least Squares Criterion. In this video I just give a quick overview of linear regression and what the 'least square criterion' actually means. In the second video, I will actually use my data points to find the linear regression / model.
Views: 457947 patrickJMT
Real-World Python Deep Learning Projects
 
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Learn how to use Deep Learning in practice by going through real-world examples. Full Course available for $10 (limited period offer) - http://bit.ly/2r3yoOt Udemy link - http://bit.ly/2Qtcenj Keras Deep Learning Projects: Packt | Udemy http://bit.ly/2PlC6g8 | http://bit.ly/2zPoMeZ Python Deep Learning Solutions: Packt | Udemy http://bit.ly/2QGJFCS | http://bit.ly/2BWzwJY Hands-on Deep Learning with TensorFlow: Packt | Udemy http://bit.ly/2UnAm9F | http://bit.ly/2E5Vdsr This video was done in collaboration with Packt and ProgrammingKnowledge. Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few. This course will teach you Deep Learning using easy-to-understand, practical, and clear examples. This video is a mixture of multiple videos present in the course. We will go through a short overview of what the course covers, then move to downloading and preparing our airline data to work without a neural network. We will then ready our project's goals and steps for labeling a given tweet. Further we we will be preparing our input images to work with CNN, by converting each image into a two-dimensional array and optimize them for best results. Finally we will learn how to get the free stock prices data and prepare it for forecasting using LSTM. By the end of this course, you will have a solid understanding of Deep Learning and the ability to build your own Deep Learning models. #BusinessIntelligence #BI #BigData
Views: 7144 ProgrammingKnowledge
The Health Data Revolution: Improving Outcomes, Protecting Privacy
 
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Will the next great medical insight come from a clinical trial, a laboratory study — or a database search? Today, health systems and insurers have access to a mountain of data about millions of Americans: what medications they take, their health history, even, in some cases, their genetics—plus an emerging body of mobile health data. Using “big data” techniques, doctors and researchers are already mining this data to deliver better care and to gain insights into the kind of hyper-specific questions that clinical trials and observational studies struggle to answer. The approach promises major, rapid-fire, highly-personalized discoveries. At the same time, with the specter of cyberattacks and hacks looming, the need for rugged privacy protection has never been greater. In this Forum, experts in healthcare data and privacy will discuss the potential for future discovery, practical steps to enable progress, and how information can be kept secure. Part of the The Dr. Lawrence H. and Roberta Cohn Forums, this event was presented jointly with HuffPost on Tuesday, December 5, 2017. Watch the entire series at ForumHSPH.org.
kNN Machine Learning Algorithm - Excel
 
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kNN, k Nearest Neighbors Machine Learning Algorithm tutorial. Follow this link for an entire Intro course on Machine Learning using R, did I mention it's FREE: https://www.youtube.com/playlist?list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn Also, be sure to check out my channel for over 300 tutorials on Excel, R, Statistics, basic Math, and more.
Views: 71308 Jalayer Academy
[LAK'17] May 16: Keynote - Timothy McKay: Can a University become a Learning Laboratory?
 
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Can a University become a Learning Laboratory? by Dr. Timothy McKay Thursday May 16, 2017 Dr McKayDr. McKay is a data scientist, drawing inference from large data sets. McKay’s research has been in two main areas: observational cosmology and higher education. He has also been an academic administrator, leading the 1800 student Honors Program in the UM College of Literature Science and the Arts from 2008-2016. In astrophysics, McKay’s main research tools have been the Sloan Digital Sky Survey, the Dark Energy Survey, and the simulations which support them both. His team uses these tools to probe the growth and nature of cosmic structure as well as the expansion history of the Universe, especially through studies of galaxy clusters and gravitational lensing. He has also studied astrophysical transients, including gamma-ray bursts, as part of the Robotic Optical Transient Search Experiment. In higher education, McKay does learning analytics: using the rich, extensive, and complex data produced by digitally mediated education to better understand and improve student outcomes. In 2011, his team created the ECoach computer tailored student support system. In 2014, he launched the REBUILD project, an effort to increase the use of evidence-based methods in large foundational courses. From 2012-2015 he chaired the University of Michigan’s Learning Analytics Task Force, which helped to create several new systems and structures supporting the use of data to improve teaching and learning. This group’s Learning Analytics Fellows Program provided the basis for McKay’s edX MOOC on Practical Learning Analytics. In 2015, McKay founded the Digital Innovation Greenhouse, an education technology accelerator within the UM Office of Academic Innovation. As Faculty Director of DIG, he works with a team of software developers, user experience designers, and behavioral scientists to grow good ideas from innovation to infrastructure. He also co-chairs the UM Institutional Learning Analytics committee. This faculty group is charged with conducting research aimed at understanding the student experience at Michigan, especially in areas most likely to impact the decision making of campus leaders.
Introduction to Big Data Analytics | RDBMS Program with Oracle, SQL | Manipal ProLearn
 
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In this video on Introduction to Big Data Analytics, we’ll be discussing about the Big Data, Hadoop, and the course outlines. - Big Data- a collection of large and complex data sets of any organization - Apache Hadoop is a solution to the Big Data and is supported by intensive distributed application - Hadoop is written in Java and applies concepts of functional and object-oriented programming - Hands-on experience in Core Java and good analytical skills are the pre-requisites to learn Hadoop Relevant Playlist : https://www.youtube.com/watch?v=xIFkUSFHBhw&list=PLdQdl32Vjw4FCgWCIjMzMxaDtTCtzKnU- ------------------------------------------------------------------------------------------------------- RDBMS Program with Oracle - Enroll Now ! https://www.manipalprolearn.com/technology/rdbms-program-with-oracle-certification-training?utm_source=YouTube&utm_medium=YouTube&utm_campaign=YouTube&utm_term=YouTube&utm_content=YouTube ------------------------------------------------------------------------------------------------------- Use the coupon code YOUTUBE30 to avail exclusive discounts (YouTube Learners) ------------------------------------------------------------------------------------------------------- Subscribe to our channel to get video updates. Hit the subscribe button above https://www.youtube.com/channel/UCllnb6S5fPzpVYcV8KYzhnA?view_as=subscriber?sub_c onfirmation=1 Also follow us on other channels Facebook: https://www.facebook.com/ManipalProLearn Twitter: https://twitter.com/manipalprolearn LinkedIn: https://www.linkedin.com/company/10147429 ------------------------------------------------------------------------------------------------------- #BigDataAnalytics #RDBMS #ManipalProlearn About the course Oracle DBA certification course will strengthen your core concepts of Oracle Database Administrator and introduce you to PL/SQL. The course is designed to give you a hands-on experience in implementing Database Normalization to enhance the design and performance of the schema. ------------------------------------------------------------------------------------------------------- Learning outcomes:  Core concepts of Hadoop Distributed File System and MapReduce framework  Setting up Hadoop clusters and Data Loading Techniques using Sqoop and Flume ecosystem projects  Programming in MapReduce MR version 1, writing complex MapReduce programs  Using Pig and Hive for Data Analytics and implementing HBase, MapReduce Integration. ------------------------------------------------------------------------------------------------------- Who can attend this course?  Software Professionals  Analytics Professionals  ETL developers  Project Managers  Testing professionals ------------------------------------------------------------------------------------------------------- Curriculum of the course:  Overview of RDBMS concepts  Oracle tools  Data Retrieval, Transformation and Grouping  Joins and Subqueries  DDL, DML TCL  DB Objects  PL/SQL Programming ------------------------------------------------------------------------------------------------------- Register Now at Manipal Prolearn : https://www.manipalprolearn.com/technology/rdbms-program-with-oracle-certification-training?utm_source=YouTube&utm_medium=YouTube&utm_campaign=YouTube&utm_term=YouTube&utm_content=YouTube
Views: 302 Manipal ProLearn