A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.
ML Times: Mainframe Machine Learning Initiative- June newsletter (2018)Leslie McFarlin
I contributed the featured article in the June 2018 newsletter: Structure and Complexity- Algorithms, Data, and User Experience. In it, I untangle the link between data and algorithms, and how that might limit what design options we have.
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...Edureka!
** Machine Learning Master's Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "How to become a Machine Learning Engineer" covers all the basic aspects of becoming a certified Machine Learning Engineer. It establishes the concepts like roles, responsibilities, skills, salaries and even trends to get you up to speed with Machine learning.
Follow us to never miss an update in the future.
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
GRC 2020 - IIA - ISACA Machine Learning Monitoring, Compliance and GovernanceAndrew Clark
With Machine Learning (ML) taking on a more significant role in decision making, ML is becoming a risk management
and compliance issue. In light of increasing regulatory scrutiny, companies deploying ML must ensure that they have a
robust monitoring and compliance program. This presentation will provide context around relevant regulations, outline
critical risks and mitigating controls for ML, and provide an overview of monitoring and governance best practices.
Course 2 Machine Learning Data LifeCycle in Production - Week 1Ajay Taneja
This is the Machine Learning Engineering in Production Course notes. This is the Week 1 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Machine Learning: The First Salvo of the AI Business RevolutionCognizant
Machine learning (ML), a branch of artificial intelligence (AI), is coming into its own as a force in the business landscape, performing a variety of innovative and highly skilled activities that enhance customer experience and offer market advantages. This is a brief guide to getting started with ML, the thinking, tools and frameworks to make it a powerful business tool.
ML Times: Mainframe Machine Learning Initiative- June newsletter (2018)Leslie McFarlin
I contributed the featured article in the June 2018 newsletter: Structure and Complexity- Algorithms, Data, and User Experience. In it, I untangle the link between data and algorithms, and how that might limit what design options we have.
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...Edureka!
** Machine Learning Master's Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "How to become a Machine Learning Engineer" covers all the basic aspects of becoming a certified Machine Learning Engineer. It establishes the concepts like roles, responsibilities, skills, salaries and even trends to get you up to speed with Machine learning.
Follow us to never miss an update in the future.
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
GRC 2020 - IIA - ISACA Machine Learning Monitoring, Compliance and GovernanceAndrew Clark
With Machine Learning (ML) taking on a more significant role in decision making, ML is becoming a risk management
and compliance issue. In light of increasing regulatory scrutiny, companies deploying ML must ensure that they have a
robust monitoring and compliance program. This presentation will provide context around relevant regulations, outline
critical risks and mitigating controls for ML, and provide an overview of monitoring and governance best practices.
Course 2 Machine Learning Data LifeCycle in Production - Week 1Ajay Taneja
This is the Machine Learning Engineering in Production Course notes. This is the Week 1 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Machine Learning: The First Salvo of the AI Business RevolutionCognizant
Machine learning (ML), a branch of artificial intelligence (AI), is coming into its own as a force in the business landscape, performing a variety of innovative and highly skilled activities that enhance customer experience and offer market advantages. This is a brief guide to getting started with ML, the thinking, tools and frameworks to make it a powerful business tool.
The Key Differences Between Rule-Based AI And Machine LearningRobert Smith
While a rules-based system could be considered as having “fixed” intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.
"Industrializing Machine Learning – How to Integrate ML in Existing Businesse...Dataconomy Media
"Industrializing Machine Learning – How to Integrate ML in Existing Businesses", Erik Schmiegelow, CEO at Hivemind Technologies AG
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Since 1996, Erik Schmiegelow has worked as a software architecht and consultant, building large data processing platforms for companies such as NTT DoCoMo, Royal Mail, Siemens, E-Plus, Allianz and T-Mobile; and until 2001 he was CTO at the Cologne-based digital agency denkwerk.
In 2007 he founded the telecommunications consulting agency Itellity, followed by Hivemind Technologies in 2014. Hivemind Technologies is a solutions and services company, focussed on big data analytics and stream processing technologies for web, social data and industrial applications. Erik studied computer sciences in Hamburg.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance. In certain cases, ML operations are solely employed for deploying machine learning models.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
SigOpt's Fay Kallel, Head of Product, and Jim Blomo, Head of Engineering, describe the latest updates to SigOpt, a suite of features that help you manage your modeling process.
Did you know that custom-trained generative AI models can give your business a game-changing edge?
The latest piece from the E42 Blog explores how custom-trained generative AI models offer a competitive edge in today's data-driven landscape. Imagine AI co-workers streamlining your Accounts Payable process, resolving HR queries round-the-clock, and enhancing marketing strategies with data-driven SWOT analyses. Generative AI models can be tailored to your specific needs and the advantages are clear:
1️⃣ 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: A meticulously trained model delivers demonstrably superior accuracy, efficiency, and effectiveness compared to generic models
2️⃣ 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐁𝐢𝐚𝐬 Custom training allows you to control data sources, mitigating potential biases and ensuring your model aligns with your values
3️⃣ 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞: In today's data-driven world, a custom-trained model automates tasks, generates creative ideas, and grants you a distinct edge
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.
How Does Data Create Economic Value_ Foundations For Valuation Models.pdfDr. Anke Nestler
In order to clarify how data creates economic value, the measurement of data value and the process of valuation of data contribution to economic benefits is being discussed in this article leading to the identification of ways to operationalize data valuation methodologies. The four independent authors André Gorius, Véronique Blum, Andreas Liebl, and Anke Nestler are leading European IP and licensing specialists of the International Licensing Executives Society (LESI).
Um zu klären, wie Daten einen wirtschaftlichen Wert schaffen, werden in diesem Artikel die Messung von Datenwerten und der Prozess der Bewertung des Beitrags von Daten zum wirtschaftlichen Nutzen erörtert, um Wege zur Operationalisierung von Datenbewertungsmethoden zu finden. Die vier unabhängigen Autoren André Gorius, Véronique Blum, Andreas Liebl und Anke Nestler sind führende europäische IP- und Lizenzierungsspezialisten der International Licensing Executives Society (LESI).
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
These are some general ideas to get one started with "Machine Learning".Machine learning is a vast subject in the field of computer science & needs intense research to master.
Environmental Monitoring System using IoT, AI and MLRobert Smith
AI, ML, IoT, and Emerging Tech. Cognitive technologies like machine learning and AI (artificial intelligence) certainly have proven to be an important part of the IoT (Internet of Things) sector because they can help make products and services smarter and, therefore, more valuable.
Top 10 Skills You Need For A High-Paying Machine Learning CareerRobert Smith
There are vast applications of Machine Learning in computer science including different types of learning such as supervised learning, unsupervised learning, and reinforcement learning. Machine Learning can be a rewarding career for students who are good in mathematics and statistics and have sharp programming skills.
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The Key Differences Between Rule-Based AI And Machine LearningRobert Smith
While a rules-based system could be considered as having “fixed” intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.
"Industrializing Machine Learning – How to Integrate ML in Existing Businesse...Dataconomy Media
"Industrializing Machine Learning – How to Integrate ML in Existing Businesses", Erik Schmiegelow, CEO at Hivemind Technologies AG
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Since 1996, Erik Schmiegelow has worked as a software architecht and consultant, building large data processing platforms for companies such as NTT DoCoMo, Royal Mail, Siemens, E-Plus, Allianz and T-Mobile; and until 2001 he was CTO at the Cologne-based digital agency denkwerk.
In 2007 he founded the telecommunications consulting agency Itellity, followed by Hivemind Technologies in 2014. Hivemind Technologies is a solutions and services company, focussed on big data analytics and stream processing technologies for web, social data and industrial applications. Erik studied computer sciences in Hamburg.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance. In certain cases, ML operations are solely employed for deploying machine learning models.
ML operations comprise a set of practices and methods specifically crafted for streamlined management of the complete lifecycle of machine learning models in production environments. It encompasses the iterative process of model development, deployment, monitoring, maintenance and integrating the model into operational systems, ensuring reliability, scalability, and performance.
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
SigOpt's Fay Kallel, Head of Product, and Jim Blomo, Head of Engineering, describe the latest updates to SigOpt, a suite of features that help you manage your modeling process.
Did you know that custom-trained generative AI models can give your business a game-changing edge?
The latest piece from the E42 Blog explores how custom-trained generative AI models offer a competitive edge in today's data-driven landscape. Imagine AI co-workers streamlining your Accounts Payable process, resolving HR queries round-the-clock, and enhancing marketing strategies with data-driven SWOT analyses. Generative AI models can be tailored to your specific needs and the advantages are clear:
1️⃣ 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: A meticulously trained model delivers demonstrably superior accuracy, efficiency, and effectiveness compared to generic models
2️⃣ 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐁𝐢𝐚𝐬 Custom training allows you to control data sources, mitigating potential biases and ensuring your model aligns with your values
3️⃣ 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞: In today's data-driven world, a custom-trained model automates tasks, generates creative ideas, and grants you a distinct edge
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.
How Does Data Create Economic Value_ Foundations For Valuation Models.pdfDr. Anke Nestler
In order to clarify how data creates economic value, the measurement of data value and the process of valuation of data contribution to economic benefits is being discussed in this article leading to the identification of ways to operationalize data valuation methodologies. The four independent authors André Gorius, Véronique Blum, Andreas Liebl, and Anke Nestler are leading European IP and licensing specialists of the International Licensing Executives Society (LESI).
Um zu klären, wie Daten einen wirtschaftlichen Wert schaffen, werden in diesem Artikel die Messung von Datenwerten und der Prozess der Bewertung des Beitrags von Daten zum wirtschaftlichen Nutzen erörtert, um Wege zur Operationalisierung von Datenbewertungsmethoden zu finden. Die vier unabhängigen Autoren André Gorius, Véronique Blum, Andreas Liebl und Anke Nestler sind führende europäische IP- und Lizenzierungsspezialisten der International Licensing Executives Society (LESI).
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
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An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?