This use case showcases how Machine Learning can help you understand your customers to better develop personalized relationships. The lecturer is Arturo Moreno, Associate Professor at ICADE Business School, and a technology entrepreneur, investor, and innovative leader working on the intersection of venture capital and Machine Learning.
*Machine Learning School for Business Schools 2021: Virtual Conference.
BigMLSchool: My First End-to-End Machine Learning ProjectBigML, Inc
A hands-on workshop to create your first End-to-End Machine Learning Project, presented by Dr. Mercè Martín, VP of Applications in BigML since 2013.
*Machine Learning School for Business Schools 2021: Virtual Conference.
BigMLSchool: ML in the Healthcare IndustryBigML, Inc
Text Analysis: Discovering Insights for the Healthcare Industry.
Learn how Machine Learning helps discover insights for the Healthcare industry by analyzing text. The lecturer is Tomáš Kliegr, Associate Professor at the Department of Information and Knowledge Engineering at Prague University of Economics and Business (VSE).
*Machine Learning School for Business Schools 2021: Virtual Conference.
Learn how to automatically summarize academic literature with the BigML platform, presented by Professor Nigel L. Williams, Reader in Project Management and Research Lead in the Organizations and Systems Management Subject Group at the University of Portsmouth, in The United Kingdom.
*Machine Learning School for Business Schools 2021: Virtual Conference.
BigMLSchool: ML Platforms and AutoML in the EnterpriseBigML, Inc
An introductory session on the current situation of Machine Learning, the importance of ML platforms and AutoML, and why ML needs to be properly taught at schools and universities.
The lecturer is Ed Fernández, Board Director at BigML and Arowana International, a Private Equity firm, Faculty at Northeastern University (the Silicon Valley campus), lecturer at Headspring Corporate Learning (the Joint Venture of Financial Times and IE Business School), and mentor at Berkeley Sutardja Center for Entrepreneurship and Technology.
*Machine Learning School for Business Schools 2021: Virtual Conference.
Prediction of company bankruptcy. Learn about how Machine Learning finds insights of the Czech Business Landscape, presented by Lucie Beranová, Ph.D. Student at Prague University of Economics and Business (VSE) and Data Scientist at Vodafone.
*Machine Learning School for Business Schools 2021: Virtual Conference.
What is Machine Learning: A Business Perspective.
A Gentle Introduction to Machine Learning, by Enrique Dans, professor of innovation at IE Business School.
*MLSEV 2020: Virtual Conference.
The more potent AI becomes, the more important it becomes to get it right. Todays most pressing problem is bias in AI. Here you can find an indepth analysis about the current status of bias mitigation algorithms and the exciting new findings that some bias can not be mitigates (impossibility theorem).
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
BigMLSchool: My First End-to-End Machine Learning ProjectBigML, Inc
A hands-on workshop to create your first End-to-End Machine Learning Project, presented by Dr. Mercè Martín, VP of Applications in BigML since 2013.
*Machine Learning School for Business Schools 2021: Virtual Conference.
BigMLSchool: ML in the Healthcare IndustryBigML, Inc
Text Analysis: Discovering Insights for the Healthcare Industry.
Learn how Machine Learning helps discover insights for the Healthcare industry by analyzing text. The lecturer is Tomáš Kliegr, Associate Professor at the Department of Information and Knowledge Engineering at Prague University of Economics and Business (VSE).
*Machine Learning School for Business Schools 2021: Virtual Conference.
Learn how to automatically summarize academic literature with the BigML platform, presented by Professor Nigel L. Williams, Reader in Project Management and Research Lead in the Organizations and Systems Management Subject Group at the University of Portsmouth, in The United Kingdom.
*Machine Learning School for Business Schools 2021: Virtual Conference.
BigMLSchool: ML Platforms and AutoML in the EnterpriseBigML, Inc
An introductory session on the current situation of Machine Learning, the importance of ML platforms and AutoML, and why ML needs to be properly taught at schools and universities.
The lecturer is Ed Fernández, Board Director at BigML and Arowana International, a Private Equity firm, Faculty at Northeastern University (the Silicon Valley campus), lecturer at Headspring Corporate Learning (the Joint Venture of Financial Times and IE Business School), and mentor at Berkeley Sutardja Center for Entrepreneurship and Technology.
*Machine Learning School for Business Schools 2021: Virtual Conference.
Prediction of company bankruptcy. Learn about how Machine Learning finds insights of the Czech Business Landscape, presented by Lucie Beranová, Ph.D. Student at Prague University of Economics and Business (VSE) and Data Scientist at Vodafone.
*Machine Learning School for Business Schools 2021: Virtual Conference.
What is Machine Learning: A Business Perspective.
A Gentle Introduction to Machine Learning, by Enrique Dans, professor of innovation at IE Business School.
*MLSEV 2020: Virtual Conference.
The more potent AI becomes, the more important it becomes to get it right. Todays most pressing problem is bias in AI. Here you can find an indepth analysis about the current status of bias mitigation algorithms and the exciting new findings that some bias can not be mitigates (impossibility theorem).
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
Explainability for Natural Language ProcessingYunyao Li
NOTE: Please check out the final version here with small but important updates and links to downloadable version and recording: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249992241
Updated version on our popular tutorial on "Explainability for Natural Language Processing" as a tutorial at KDD'2021.
Title: Explainability for Natural Language Processing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Dhanorkar, Shipi and Li, Yunyao and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Presenter: Marina Danilevsky, Dhanorkar, Shipi and Li, Yunyao and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
A Topic Model of Analytics Job Adverts (Operational Research Society Annual C...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
Explainability for Natural Language ProcessingYunyao Li
Tutorial at AACL'2020 (http://www.aacl2020.org/program/tutorials/#t4-explainability-for-natural-language-processing).
More recent version: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249912819
Title: Explainability for Natural Language Processing
@article{aacl2020xaitutorial,
title={Explainability for Natural Language Processing},
author= {Dhanorkar, Shipi and Li, Yunyao and Popa, Lucian and Qian, Kun and Wolf, Christine T and Xu, Anbang},
journal={AACL-IJCNLP 2020},
year={2020}
Presenter: Shipi Dhanorkar, Christine Wolf, Kun Qian, Anbang Xu, Lucian Popa and Yunyao Li
Video: https://www.youtube.com/watch?v=3tnrGe_JA0s&feature=youtu.be
Abstract:
We propose a cutting-edge tutorial that investigates the issues of transparency and interpretability as they relate to NLP. Both the research community and industry have been developing new techniques to render black-box NLP models more transparent and interpretable. Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP researchers, our tutorial has two components: an introduction to explainable AI (XAI) and a review of the state-of-the-art for explainability research in NLP; and findings from a qualitative interview study of individuals working on real-world NLP projects at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability in NLP. Then, we will discuss explainability for NLP tasks and report on a systematic literature review of the state-of-the-art literature in AI, NLP, and HCI conferences. The second component reports on our qualitative interview study which identifies practical challenges and concerns that arise in real-world development projects which include NLP.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
data scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
Video: https://youtu.be/ky3159dqQ_o?t=30
Advances in Data science, Machine Learning, AI, Optimization and prediction are revolutionizing the way financial professionals are taking decisions. From sifting through large amounts of data to designing strategies to optimizing execution, technology has played a major role in changing the investment game! The 21st Century Financial Professional needs to be cognizant of the tsunami of changes that are changing the industry.
In this webinar, Sri Krishnamurthy, CFA, the President of QuantUniversity shares five key trends every financial professional needs to know about. Sri along with Dr.Gustavo Vicentini and Anish Shah, CFA will be leading a full day workshop on the theme on Feb 6th.
In this presentation, Shanmugam introduces Analytics and devices an innovative model that gives out recommendations to students regarding choosing the right engineering streams. Shanmugam employs data analytics to achieve this.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Get a quick introduction to data science with python. What is Data Science? Where is data science used? How is data Science used? Where is the future of Data Science.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Explainability for Natural Language ProcessingYunyao Li
NOTE: Please check out the final version here with small but important updates and links to downloadable version and recording: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249992241
Updated version on our popular tutorial on "Explainability for Natural Language Processing" as a tutorial at KDD'2021.
Title: Explainability for Natural Language Processing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Dhanorkar, Shipi and Li, Yunyao and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Presenter: Marina Danilevsky, Dhanorkar, Shipi and Li, Yunyao and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
A Topic Model of Analytics Job Adverts (Operational Research Society Annual C...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
Explainability for Natural Language ProcessingYunyao Li
Tutorial at AACL'2020 (http://www.aacl2020.org/program/tutorials/#t4-explainability-for-natural-language-processing).
More recent version: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249912819
Title: Explainability for Natural Language Processing
@article{aacl2020xaitutorial,
title={Explainability for Natural Language Processing},
author= {Dhanorkar, Shipi and Li, Yunyao and Popa, Lucian and Qian, Kun and Wolf, Christine T and Xu, Anbang},
journal={AACL-IJCNLP 2020},
year={2020}
Presenter: Shipi Dhanorkar, Christine Wolf, Kun Qian, Anbang Xu, Lucian Popa and Yunyao Li
Video: https://www.youtube.com/watch?v=3tnrGe_JA0s&feature=youtu.be
Abstract:
We propose a cutting-edge tutorial that investigates the issues of transparency and interpretability as they relate to NLP. Both the research community and industry have been developing new techniques to render black-box NLP models more transparent and interpretable. Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP researchers, our tutorial has two components: an introduction to explainable AI (XAI) and a review of the state-of-the-art for explainability research in NLP; and findings from a qualitative interview study of individuals working on real-world NLP projects at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability in NLP. Then, we will discuss explainability for NLP tasks and report on a systematic literature review of the state-of-the-art literature in AI, NLP, and HCI conferences. The second component reports on our qualitative interview study which identifies practical challenges and concerns that arise in real-world development projects which include NLP.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
data scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
Video: https://youtu.be/ky3159dqQ_o?t=30
Advances in Data science, Machine Learning, AI, Optimization and prediction are revolutionizing the way financial professionals are taking decisions. From sifting through large amounts of data to designing strategies to optimizing execution, technology has played a major role in changing the investment game! The 21st Century Financial Professional needs to be cognizant of the tsunami of changes that are changing the industry.
In this webinar, Sri Krishnamurthy, CFA, the President of QuantUniversity shares five key trends every financial professional needs to know about. Sri along with Dr.Gustavo Vicentini and Anish Shah, CFA will be leading a full day workshop on the theme on Feb 6th.
In this presentation, Shanmugam introduces Analytics and devices an innovative model that gives out recommendations to students regarding choosing the right engineering streams. Shanmugam employs data analytics to achieve this.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Get a quick introduction to data science with python. What is Data Science? Where is data science used? How is data Science used? Where is the future of Data Science.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Which institute is best for data science?DIGITALSAI1
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Join us for the Best Selenium certification course at Edux factor and enrich your carrier.
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Data Science Online Training In HA comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.hyderabad Data Science Online Training
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
Eduxfactor is an online data science training institution based in Hyderabad. A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Data science online training in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
Overview of Data Science Courses Online
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
What You'll Learn In Data Science Courses Online
Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
Comprehend the crucial steps required to solve real-world data problems and get familiar with the methodology to think and work like a Data Scientist.
Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
Implement clustering methodology, an unsupervised learning method, and a deep neural network (a supervised learning method).
Build a data analysis pipeline, from collection to analysis to presenting data visually.
#datasciencecoursesonline
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Data science online training in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
data science online training in hyderabadVamsiNihal
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge. Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
Best data science training in HyderabadKumarNaik21
Join us for the Best data science training in Hyderabad at Edux factor and enrich your carrier.
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Eduxfactor is an online data science training institution based in Hyderabad. A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Data science training in hyd ppt converted (1)SayyedYusufali
Data Science Online Training In HA comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.hyderabad Data Science Online Training
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Overview of Data Science Courses Online
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
What You'll Learn In Data Science Courses Online
Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
Comprehend the crucial steps required to solve real-world data problems and get familiar with the methodology to think and work like a Data Scientist.
Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
Implement clustering methodology, an unsupervised learning method, and a deep neural network (a supervised learning method).
Build a data analysis pipeline, from collection to analysis to presenting data visually.
#datasciencecoursesonline
#datascience
#datasciencecourses
Similar to BigMLSchool: Customer Segmentation (20)
Digital Transformation and Process Optimization in ManufacturingBigML, Inc
Keyanoush Razavidinani, Digital Services Consultant at A1 Digital, a BigML Partner, highlights why it is important to identify and reduce human bottlenecks that optimize processes and let you focus on important activities. Additionally, Guillem Vidal, Machine Learning Engineer at BigML completes the session by showcasing how Machine Learning is put to use in the manufacturing industry with a use case to detect factory failures.
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML for AML ComplianceBigML, Inc
Machine Learning for Anti Money Laundering Compliance, by Kevin Nagel, Consultant and Data Scientist at INFORM.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - My First Anomaly Detector BigML, Inc
My First Anomaly Detector: Practical Workshop, by Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
Introduction to End-to-End Machine Learning: Classification and Regression - Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - A Data-Driven CompanyBigML, Inc
A Data-Driven Company: 21 Lessons for Large Organizations to Create Value from AI, by Richard Benjamins, Chief AI and Data Strategist at Telefónica.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML in the Legal SectorBigML, Inc
How Machine Learning Transforms and Automates Legal Services, by Arnoud Engelfriet, Co-Founder at Lynn Legal.
*Machine Learning School in The Netherlands 2022.
Machine Learning for Public Safety: Reducing Violence and Discrimination in Stadiums.
Speakers: Ramon van Ingen, Co-Founder at Siip, Entrepreneur, Researcher, and Pablo González, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Anomaly Detection at ScaleBigML, Inc
Lessons Learned Applying Anomaly Detection at Scale, by Álvaro Clemente, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Citizen Development in AIBigML, Inc
Citizen Development in AI, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
This new feature is a continuation of and improvement on our previous Image Processing release. Now, Object Detection lets you go a step further with your image data and allows you to locate objects and annotate regions in your images. Once your image regions are defined, you can train and evaluate Object Detection models, make predictions with them, and automate end-to-end Machine Learning workflows on a single platform. To make that possible, BigML enables Object Detection by introducing the regions optype.
As with any other BigML feature, Object Detection is available from the BigML Dashboard, API, and WhizzML for automation. Object Detection is extremely helpful to tackle a wide range of computer vision use cases such as medical image analysis, quality control in manufacturing, license plate recognition in transportation, people detection in security surveillance, among many others.
This new release brings Image Processing to the BigML platform, a feature that enhances our offering to solve image data-driven business problems with remarkable ease of use. Because BigML treats images as any other data type, this unique implementation allows you to easily use image data alongside text, categorical, numeric, date-time, and items data types as input to create any Machine Learning model available in our platform, both supervised and unsupervised.
Now, it is easier than ever to solve a wide variety of computer vision and image classification use cases in a single platform: label your image data, train and evaluate your models, make predictions, and automate your end-to-end Machine Learning workflows. As with any other BigML feature, Image Processing is available from the BigML Dashboard, API, and WhizzML, and it can be applied to solve use cases such as medical image analysis, visual product search, security surveillance, and vehicle damage detection, among others.
Machine Learning in Retail: Know Your Customers' Customer. See Your FutureBigML, Inc
This session presents a quite common situation for those working in food and beverage retail (FnB) and highlights interesting insights to fight waste reduction.
Speaker: Stephen Kinns, CEO and Co-Founder at catsAi.
*ML in Retail 2021: Webinar.
Machine Learning in Retail: ML in the Retail SectorBigML, Inc
This is an introductory session about the role that Machine Learning is playing in the retail sector and how it is being deployed across the different areas of this industry.
Speaker: Atakan Cetinsoy, VP of Predictive Applications at BigML.
*ML in Retail 2021: Webinar.
ML in GRC: Machine Learning in Legal Automation, How to Trust a LawyerbotBigML, Inc
This presentation analyzes the role that Machine Learning plays in legal automation with a real-world Machine Learning application.
Speaker: Arnoud Engelfriet, Co-Founder at Lynn Legal.
*ML in GRC 2021: Virtual Conference.
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...BigML, Inc
This is a real-life Machine Learning use case about integrated risk.
Speakers: Thomas Rengersen, Product Owner of the Governance Risk and Compliance Tool for Rabobank, and Thomas Alderse Baas, Co-Founder and Director of The Bowmen Group.
*ML in GRC 2021: Virtual Conference.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
BigMLSchool: Customer Segmentation
1. Arturo Moreno #BigMLSchool ICADE
February 17, 2021
Customer Segmentation: Understanding Your
Customers to Better Develop Personalized
Relationships
BigML Machine Learning School
2. Arturo Moreno #BigMLSchool ICADE
Welcome
print ("Welcome to BigML Machine
Learning School!")
Output:
Welcome to BigML
Machine Learning School!
3. Arturo Moreno #BigMLSchool ICADE
About me
Associate Professor Business Analytics at ICADE Business School
Twitter:
@r2_moreno
Clubhouse:
@r2moreno
4. Arturo Moreno #BigMLSchool ICADE
About me
Disclaimer
I think the BigML team are great experts and that they have built a top-class
machine learning platform.
I have worked with BigML in the past and love them.
Now, everything that I share with you is my honest assessment of the value of
BigML as a tool to teach machine learning at business schools.
5. Arturo Moreno #BigMLSchool ICADE
Agenda
My perspective on teaching ML
• Setting (the right) expectations for the course
• Theoretical background + hands-on experience (for managers)
• BigML at the core of my teaching experience
• Case Study – Clustering for better understanding your customers
• Questions? Google Meet and Slack channel
6. Arturo Moreno #BigMLSchool ICADE
1. Approach business problems data-analytically
• Think carefully & systematically about whether & how data can improve
decision performance
2. Be able to interact competently on the topic of data mining for business
analytics
• Know the basics of data mining processes, techniques, & concepts well
enough
3. Live hands-on experience applying ML models
• You should be able to follow up on ideas or opportunities that present
themselves
6
Setting (the right) expectations for the course
The goal for the class is three-fold
7. Arturo Moreno #BigMLSchool ICADE
7
Setting (the right) expectations for the course
Data-related jobs are in explosive demand
Source: Indeed
8. Arturo Moreno #BigMLSchool ICADE
8
Setting (the right) expectations for the course
And Schools have already / are in the process of adapting
Source: Indeed
9. Arturo Moreno #BigMLSchool ICADE
Setting (the right) expectations for the course
Importance of Business Analytics
Source: McKinsey&Co.
10. Arturo Moreno #BigMLSchool ICADE
Setting (the right) expectations for the course
Importance of Business Analytics
Assortment
optimization
Next-product-
to-buy mailing
Promotion
optimization
Inventory
management
Real-time
advertising
Shopper
segmentation
Shopper
loyalty/
Churn
Source: McKinsey&Co.
11. Arturo Moreno #BigMLSchool ICADE
1. Not a computer science class. I don’t expect students to program, so
implementation won’t be covered in the class.
2. Not a Math class either. We explore just enough in order for you to
understand the concept. (i.e.: entropy of a dataset or “distance” between two
elements,…)
3. Not a course on big data technologies (data engineering and data
processing)
But also, ML education must stay away from an existing ML-charlatan trend.
Above all, it’s our duty to teach to use data with responsibility. (i.e.: stay away
from low-quality processes around data, models or evaluation)
11
Setting (the right) expectations for the course
The NO-goal for this class is three-fold
12. Arturo Moreno #BigMLSchool ICADE
2
Setting (the right) expectations for the course
What should be the goal for business students?
• Improve decision making?
• Build well “performant” models?
• Deploy models of all kinds supervised or unsupervised?
• Make sure there is as much data as possible?
• Ok… Just throw millions and hire thousands of developers and let data for
the data scientists!?
• … Other?
13. Arturo Moreno #BigMLSchool ICADE
Setting (the right) expectations for the course
Cross Industry Standard Process for Data Mining (CRISP-DM; Shearer, 2000)
Industry average is that ~
80% of the time of any data
science project is spent
here.
There is a lot of domain
expertise required but
basic data transformation
skills.
This is the ideal territory for
business students to
participate
Source: Data Science for Business – Foster, Provost
14. Arturo Moreno #BigMLSchool ICADE
Setting (the right) expectations for the course
Develop the habit of data understanding
15. Arturo Moreno #BigMLSchool ICADE
Theoretical background + hands-on experience
• Gather data. Plot data. Hypothesize with data. Infer (if possible) from data.
REPEAT!
• Introduce the data mining process
o The concept
o The role
o The process
o The techniques
• Do that through business examples (real problems, real datasets) to
illustrate type of algorithms and the data mining process
Class structure
16. Arturo Moreno #BigMLSchool ICADE
Theoretical background + hands-on experience
Class project (30%): your first data-mining project
• Groups of 2
• Pick a theme that you are interested in or CHOOSE out of my suggestions
• Final Report delivery and class presentation following the CRISP-DM
methodology
o Business Understanding
o Data Understanding (& Preparation)
o Modeling
o Evaluation
o Deployment
How?: My first data science project
17. Arturo Moreno #BigMLSchool ICADE
Theoretical background + hands-on experience
• Select a problem that you want to work on (your business objective) and
explain why this is relevant for you and who else is interested
• Find the data to work on that problem from the data repositories
• Presentation 1: Evaluation and inspection of the data using BigML, Tableau
and/or Jupyter Notebooks.
• Presentation 2: Determining the objective function. Feature generation,
feature selection.
• Presentation 3: Choosing and building the model(s)
• Presentation 4: Evaluation of the model(s). REPEAT!
• Final Presentation: Data Science Project Presentation
My first data science project
21. Arturo Moreno #BigMLSchool ICADE
Theoretical background + hands-on experience
• “If you can’t explain it simply, you don’t understand it well enough” — Albert
Einstein
• We go at a level of detail that students can later explain and question.
• We don't get into technical details, yet deep enough so that students
understand the main questions to be asked related to the why of the
outcomes of models.
1
Focus on understanding what we are doing
31. Arturo Moreno #BigMLSchool ICADE
The Data Mining Process
Cross Industry Standard Process for Data Mining (CRISP-DM; Shearer, 2000)
32. Arturo Moreno #BigMLSchool ICADE
BigML at the core of my teaching experience
Sound theoretical background with business practice
33. Arturo Moreno #BigMLSchool ICADE
BigML at the core of my teaching experience
33
33
1
ACCESS TO DATA: READY-TO-USE DATASETS, SIMPLICITY TO
CONNECT TO EXTERNAL FILES OR DATABASES
2
MODEL CATALOGUE: SUPERVISED AND UNSUPERVISED
MODELS AVAILABLE BOTH 1-CLICK AND CUSTOMIZABLE
3
KEY ML ELEMENTS AT THE CORE: TRAINING-TEST SPLIT,
EXPLAINABILITY FEATURES, CONFUSSION MATRIX
BigML allows me to focus on what matters most for Business students
34. Arturo Moreno #BigMLSchool ICADE
/Case Study – Clustering & Understand customers
34
34
Why are we doing this? Setting the business objective
BUSINESS CASE:
CAN MY CUSTOMER DATA BE USED TO
DEVELOP PERSONALIZED
RELATIONSHIPS WITH MY CUSTOMERS?
35. Arturo Moreno #BigMLSchool ICADE
/Case Study – Clustering & Understand customers
35
35
Let’s see the data. Does it make sense from a business perspective?
36. Arturo Moreno #BigMLSchool ICADE
/Case Study – Clustering & Understand customers
36
36
Analysis of correlations
37. Arturo Moreno #BigMLSchool ICADE
/Case Study – Clustering & Understand customers
37
37
On to clustering… [REPEAT]
38. Arturo Moreno #BigMLSchool ICADE
/Case Study – Clustering & Understand customers
38
38
Applications and Conclusions
39. Arturo Moreno #BigMLSchool ICADE
/Case Study – Clustering & Understand customers
39
39
Applications and Conclusions
ANY BUSINESS OUTCOME?
DO NOT ACCEPT THE FIRST NICE
SOLUTION, KEEP ITERATING AND
QUESTIONING WHETHER THIS MAKES
ANY SENSE FOR THE BUSINESS.
TEST.