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: 534622 MBAbullshitDotCom
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: 187439 Joshua Emmanuel
This video is about DECISION TREE ANALYSIS which will help you to understand the basic concept of decision tree analysis. In this video i have solved one practical question which will help you to get the process of solving any numerical question and example. After watching you will also get to know that how to construct the decision tree. I hope this will help you. Thanks JOLLY Coaching how to solve decision tree problem, Decision tree analysis, How to solve decision tree analysis, Practical solved questios on decision tree analysis. decision threoy decision tree analysis
Views: 100144 JOLLY Coaching
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. We are going to use Weka. Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. You can download Weka from here
Views: 1116 Ayyaz Ahmad
Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUO Before breaking out the big algos on a new dataset, it is a good idea to first explore the simple, intuitive patterns (i.e. heuristics). This will pay off in droves. It not only exposes you to your data, it makes you understand it and gives you that critical 'business knowledge'. People you work with will ask you general questions about the data, and this is how you can get to it. In this post will explore how to find the important values that explain a particular target outcome. We'll use sklearn's DecisionTreeClassifier and graphviz for exporting and visualizing resulting trees. Blog/Code: http://amunategui.github.io/simple-heuristics/index.html Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!
Views: 3557 Manuel Amunategui
Learn how to strategically set up your shelf using a consumer decision tree. This approach will set you up for success, because you're organizing the shelf based on what's most important to the Shopper. Happy learning!
Views: 595 Sue Nicholls
This video is a part of an online course that provides a comprehensive introduction to practial machine learning methods using MATLAB. In addition to short engaging videos, the course contains interactive, in-browser MATLAB projects. Complete course is available here: http://bit.ly/2Djmuc3 Learn more about using MATLAB for machine learning: http://bit.ly/2O9Sujp Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLeSee What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
Views: 716 MATLAB
Support TTT on Patreon - http://www.patreon.com/TheTerrainTutor FB Support Group - https://www.facebook.com/groups/TheTerrainTutorsTerrainiacs Join TTT on Facebook - https://www.facebook.com/TheTerrainTutor
Views: 4942 TheTerrainTutor
ETSU Online Programs - http://www.etsu.edu/online
Views: 87551 East Tennessee State University
Risk and Uncertainty - Decision Trees Part 1 - ACCA Performance Management (PM) *** Complete list of free ACCA lectures is available on OpenTuition.com https://opentuition.com/acca/pm/ *** Free lectures for the ACCA Performance Management (PM) Exam To benefit from this lecture, visit opentuition.com/acca to download the notes used in the lecture and access ALL free resources: ACCA lectures, tests and Ask the ACCA Tutor Forums Please go to opentuition to post questions to ACCA Tutor, we do not provide support on youtube.
Views: 1604 OpenTuition
Sara Robertson, VP, Product Engineering, Xaxis
Views: 872 AppNexus
Copyright 2016 IBM Corporation. All rights reserved. IBM, the IBM logo, Cognos, the Cognos logo and other IBM products and services are trademarks of International Business Machines Corporation in the United States, other countries, or both. Excel, Internet Explorer, PowerPoint, SharePoint and Windows are registered trademarks of Microsoft Corporation. Mozilla, the Mozilla logo, Firefox and the Firefox logo are registered trademarks of Mozilla Corporation. Other company, product or service names may be trademarks or service marks of others. iPad® and Apple® are registered trademarks of Apple Inc. The information contained in this video is provided for informational purposes only, and is provided AS IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, these materials. Nothing contained in these materials is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in these materials to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in these materials may change at any time at IBM's sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Sample data is used in this video to develop sample applications of the IBM Program. These records may contain fictional data manually or machine generated, or factual data compiled from academic or public sources, or data used with permission of the copyright holder. These fictitious records include sample data for sales transactions, product distribution, finance, and human resources. Any resemblance to actual names, addresses, contact numbers, or transaction values is coincidental. Other sample files may contain fictional data manually or machine generated, factual data compiled from academic or public sources, or data used with permission of the copyright holder, for use as sample data to develop sample applications. Product names referenced may be the trademarks of their respective owners. Unauthorized duplication is prohibited.
Views: 7028 IBM Analytics Learning Services
''' Python for Machine Learnion Session # 88 Topic to be covered - How to install Graphviz, Pydotplus and execute it to generate the Decision Tree Graph in Anaconda/Spyder ''' from sklearn.tree import DecisionTreeClassifier from sklearn import datasets from IPython.display import Image from sklearn import tree import pydotplus # Load data iris = datasets.load_iris() X = iris.data y = iris.target # Create decision tree classifer object #clf = DecisionTreeClassifier(random_state=0) clf_decisiontree = DecisionTreeClassifier(criterion = "entropy",random_state=0) #clf = DecisionTreeClassifier(criterion = "gini",random_state=0) # Train model model = clf_decisiontree.fit(X, y) # Create DOT data dot_data = tree.export_graphviz(clf_decisiontree, out_file=None, feature_names=iris.feature_names, class_names=iris.target_names) # Draw graph graph = pydotplus.graph_from_dot_data(dot_data) # Show graph Image(graph.create_png()) # Create PDF graph.write_pdf("iris2.pdf") # Create PNG graph.write_png("iris2.png")
Views: 1296 MachineLearning with Python
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Views: 68412 Derek Kane
Copyright 2015 IBM Corporation. All rights reserved. IBM, the IBM logo, Cognos, the Cognos logo and other IBM products and services are trademarks of International Business Machines Corporation in the United States, other countries, or both. Excel, Internet Explorer, PowerPoint, SharePoint and Windows are registered trademarks of Microsoft Corporation. Mozilla, the Mozilla logo, Firefox and the Firefox logo are registered trademarks of Mozilla Corporation. Other company, product or service names may be trademarks or service marks of others. iPad® and Apple® are registered trademarks of Apple Inc. The information contained in this video is provided for informational purposes only, and is provided AS IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, these materials. Nothing contained in these materials is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in these materials to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in these materials may change at any time at IBM's sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Sample data is used in this video to develop sample applications of the IBM Program. These records may contain fictional data manually or machine generated, or factual data compiled from academic or public sources, or data used with permission of the copyright holder. These fictitious records include sample data for sales transactions, product distribution, finance, and human resources. Any resemblance to actual names, addresses, contact numbers, or transaction values is coincidental. Other sample files may contain fictional data manually or machine generated, factual data compiled from academic or public sources, or data used with permission of the copyright holder, for use as sample data to develop sample applications. Product names referenced may be the trademarks of their respective owners. Unauthorized duplication is prohibited.
Views: 1630 IBM Analytics Learning Services
This Decision Tree algorithm in Machine Learning tutorial video will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms. Below topics are covered in this Decision Tree Algorithm Tutorial: 1. What is Machine Learning? ( 02:25 ) 2. Types of Machine Learning? ( 03:27 ) 3. Problems in Machine Learning ( 04:43 ) 4. What is Decision Tree? ( 06:29 ) 5. What are the problems a Decision Tree Solves? ( 07:11 ) 6. Advantages of Decision Tree ( 07:54 ) 7. How does Decision Tree Work? ( 10:55 ) 8. Use Case - Loan Repayment Prediction ( 14:32 ) What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 27566 Simplilearn
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: 32729 MIT OpenCourseWare
Learn more about Frontline Systems Risk Solver Platform software which includes forecasting, data mining, simulation/risk analysis, decision trees, as well as conventional and stochastic optimization. You can find the full playlist here: http://youtu.be/-T-mY_M2Cx0
Views: 718 FrontlineSolvers
Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 181157 Google Developers
Part 4 of 6. In this Video you will learn how to use a Decision Tree to develop an optimal strategy when you are faced with multiple stage decisions and uncertain outcomes at each stage. You can find the full playlist here: http://youtu.be/-T-mY_M2Cx0
Views: 5272 FrontlineSolvers
Learn how to identify what your consumers want in a product and the best distribution and sales channels to engage with your chosen markets. ↓ More info below. ↓ Take this course free on edX: https://www.edx.org/course/marketing-analytics-products-uc-berkeleyx-busadm466-4x#! ABOUT THIS COURSE In this marketing course, you will learn how to apply advanced concepts such as conjoint analysis and decision tree methodologies to product decisions, as well as learn the best ways to distribute and sell your offerings to consumers. You will also learn how to apply conjoint analysis to identify the product features that your consumers want most. This course is taught by Stephan Sorger who has held leadership roles in marketing and product development at companies such as Oracle, 3Com and NASA. He has also taught for over a decade at UC Berkeley Extension and is the author of two widely adopted marketing textbooks. This course will equip you with the knowledge and skills necessary to immediately see practical benefits in the workplace. Analytics-based marketing is increasingly important in determining a company’s spending and ROI. Many entry-level positions in marketing now require some basic level of knowledge in this rapidly growing field. WHAT YOU'LL LEARN - Product: Conjoint analysis - Product: Decision trees - Distribution: Store location decision model - Sales: Consumer sales process for brick and mortar and ecommerce channels
Views: 1002 edX
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Views: 12026 Sonu Singh - PPT wale
Q.Shelf harnesses data science to power better range and space decisions for retailers and suppliers. Q.Shelf currently comprises two key modules: Customer Decision Tree and Diagnose Range. https://www.quantium.com/q-shelf/
Views: 185 Quantium
http://food-safety-training.net Stages in the implementation of HACCP (The HACCP study) 1 Assemble and train the HACCP team. (define the terms of reference and the scope) 2 Describe the product/recipe/process. 3 Identify the intended use. 4 Construct a flow diagram and food-room layout showing product, personnel, equipment and waste flows 5 On-site verification of flow diagram and food room layout 6 Identify hazard/risk/severity and control measures. (Hazard analysis) 7 Determine critical control points using the decision tree. 8 Determine critical limits, target levels and tolerances for each critical control point. 9 Establish monitoring system for each CCP. 10 Establish actions to be taken when a CCP is moving out of control, and the corrective actions to be taken if a deviation occurs 11 Establish verification procedures (includes validation and review) 12 Establish record keeping and documentation. The twelve logical sequence steps for the implementation of HACCP (The HACCP study) Successful implementation of HACCP must be built on good hygiene practice and depends on: Culture of the organization Management commitment Effective management/supervision (including leadership of the HACCP team) Adequate resources (budget and time) Adequate training Access to scientific information Access to necessary expertise HACCP implementation must be carefully planned Full implementation - throughout all departments/products. Gradual implementation -department/product at a time. 1. Assemble the HACCP team (multidisciplinary expertise - external expertise may be required, especially for smaller businesses). Training will probably be required. The terms of reference (which product or groups of products/process) and the scope of the HACCP study (which hazards) should be determined. 2. Describe the product/recipe/process. Relevant safety information will include composition, aw, and pH. Processes such as heat-treatment, freezing, brining, and irradiation. Packaging, shelf life, storage conditions and method of distribution. 3. Identify intended use of the product i.e. how will the end user/consumer use the product, e.g. microwave reheating. Awareness of potential abuse, likely time out of temperature control. Will the product be consumed by vulnerable groups e.g. babies, immunocompromised or the elderly? 4. Construct a flow diagram. (It’s also usual to describe the process). The flow diagram must cover all steps in the operation. 5. On-site validation of the flow diagram. - 'walk the line' 6. Identify all potential hazards/risk/severity associated with each step, conduct a hazard analysis and consider any measures to control identified hazards (Principle 1). 7. Determine critical control points using the decision tree (Principle 2). External expertise may be required to support the HACCP team. However, a HACCP plan produced solely by an external consultant may not result in the essential sense of ownership by managers and operatives. 8 Determine critical limits, target levels and tolerances for each critical control point. (Principle 3) 9 Establish monitoring system for each CCP. (Principle 4) 10 Establish corrective actions to be taken if a deviation occurs i.e. a CCP is out of control. (Principle 5) 11 Establish verification procedures (includes validation and review). (Principle 6) 12 Establish documentation and record keeping. (Principle 7) http://food-safety-training.net
Views: 3057 Dave Summers
How do you know if you are making the right decisions on your product or project? How do you know that you're measuring the correct metrics to determine success? In this talk, two Cloud Foundry Product Managers will describe how to design the correct measurable indicators to build an understanding of your product, how to monitor those metrics over time, and how to build feedback loops to course-correct and determine the success of short-term initiatives and features. Speakers: Zoe Vance Senior Product Manager, Pivotal Denise Yu Software Engineer, Pivotal Filmed at SpringOne Platform 2018
Views: 264 Pivotal Software, Inc.
Risk and Uncertainty - Decision Trees Part 2 - ACCA Performance Management (PM) *** Complete list of free ACCA lectures is available on OpenTuition.com https://opentuition.com/acca/pm/ *** Free lectures for the ACCA Performance Management (PM) Exam To benefit from this lecture, visit opentuition.com/acca to download the notes used in the lecture and access ALL free resources: ACCA lectures, tests and Ask the ACCA Tutor Forums Please go to opentuition to post questions to ACCA Tutor, we do not provide support on youtube.
Views: 1380 OpenTuition
2015 Fall 6893 Big Data Analytics.
Views: 174 Roger Shen
What is the customer decision tree. How to plot your customer decision making process. Understanding your customer. Sales-Genius unique transformational training mixing neuroscience with cutting edge training techniques to get better results in your business.
Views: 144 Sales Genius
-- Created using PowToon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 52 Bilal Vohra
Smith Industries case study for choosing between manufacturing a new product or buying it from a supplier using Decision Tree Analysis
Views: 44 Mohab Mohamed
The ABC B2B database consists of 28 classifications with 240.000 products and services. Performing effective searches for new sales leads in the ABC B2B database is easy with the ABC Marketing tool: a powerful, online tool allowing you to compose simple or complex searches, to decide which information is relevant for you and to download results according to your needs.
Views: 295 ABC Business Directories
Making a decision to change a product's design or it's packaging isn't easy. Solutions are clearly not one-intervention-fits-all. Could these tricky decisions be simplified by assessing plastic products or packaging in a new way? Resource futures have developed a framework that takes a fresh look at plastic items in terms of their ‘use phase’ i.e. the length of time a plastic item is used for its intended purpose. This session will focus on a useful infographic and set of decision trees. We will take you through the decision trees using a range of everyday plastic products that have short, medium and long use phases. This will enable you to ask simple questions to help navigate through the sea of choices around minimising the impact of plastic.
Views: 140 DIF Live