A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Machine learning & artificial intelligence. Machine learning is playing an increasingly important role in computing and artificial intelligence. Suits any article on AI, algorithms, machine learning, quantum computing, artificial intelligence.
Machine learning training bootcamp is a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI).
Attendees will learn, comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification, and prediction.
Learning Objectives:
Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
List similarities and differences between AI, Machine Learning and Data Mining
Learn how Artificial Intelligence uses data to offer solutions to existing problems
Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn.
Clarify how Data Mining can serve as foundation for AI and machine learning.
List the various applications of machine learning and related algorithms
Learn how to classify the types of learning such as supervised and unsupervised learning
Implement supervised learning techniques such as linear and logistic regression
Use unsupervised learning algorithms including deep learning, clustering , etc.
Learn about classification data and Machine Learning models
Select the best algorithms applied to Machine Learning
Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering
more...
Course Agenda and Topics:
The Basics of Machine Learning
Machine Learning Techniques, Tools and Algorithms
Data and Data Science
Review of Terminology and Principles
Applied Artificial Intelligence (AI) and Machine Learning
Popular Machine Learning Methods
Learning Applied to Machine Learning
Principal Component Analysis
Principles of Supervised Machine Learning Algorithms
Principles of Unsupervised Machine Learning
Regression Applied to Machines Learning
Principles of Neural Networks
Large Scale Machine Learning
Introduction to Deep Learning
Applying Machine Learning
Overview of Algorithms
Overview of Tools and Processes
Call us today at +1-972-665-9786. Learn more about course audience, objectives, outlines, pricing. Visit our website links below.
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Azure Machine Learning and ML on PremisesIvo Andreev
Machine Learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis.Microsoft offers Azure Machine Learning, while Amazon offers Amazon Machine Learning and Google offers the Google Prediction API - now depricated and replaced by Google ML engine based on TensorFlow. Software products such as MATLAB support traditional, non-cloud-based ML modeling.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...Simplilearn
This presentation on "Machine Learning Engineer Salary, Skills & Resume" will help you understand who is a Machine Learning engineer, the salary of a Machine Learning engineer, skills required to become a Machine Learning engineer and what a Machine Learning engineer's resume should look like. Machine Learning is the study of algorithms and data models that computer systems utilize to perform specific tasks without using instructions, relying on previous patterns. To make this possible, a Machine Learning engineer is required. Now, let us get started and understand what the job of a Machine Learning engineer looks like.
Below are the topics that we will be discussing in the presentation:
1. Introduction to Machine Learning
2. Responsibilities of a Machine Learning engineer
3. Salary Trends of a Machine Learning engineer
4. Skills of a Machine Learning engineer
5. Resume of a Machine Learning engineer
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
PREDICT THE FUTURE , MACHINE LEARNING & BIG DATADotNetCampus
Scopri come utilizzare Azure Machine Learning, un servizio cloud che consente alle aziende, università, centri di ricerca e sviluppatori di incorporare e sfrutturare nelle loro applicazioni funzionalità di apprendimento automatico e analisi predittiva su enormi set di dati. Tramite Azure ML Studio possiamo creare, testare, attuare e gestire soluzioni di analisi predittiva e apprendimento automatico nel cloud tramite un qualunque web browser. Durante la sessione si darà un saggio attraverso un esempio di analisi predittiva sul Flight Delay.
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Machine learning & artificial intelligence. Machine learning is playing an increasingly important role in computing and artificial intelligence. Suits any article on AI, algorithms, machine learning, quantum computing, artificial intelligence.
Machine learning training bootcamp is a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI).
Attendees will learn, comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification, and prediction.
Learning Objectives:
Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
List similarities and differences between AI, Machine Learning and Data Mining
Learn how Artificial Intelligence uses data to offer solutions to existing problems
Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn.
Clarify how Data Mining can serve as foundation for AI and machine learning.
List the various applications of machine learning and related algorithms
Learn how to classify the types of learning such as supervised and unsupervised learning
Implement supervised learning techniques such as linear and logistic regression
Use unsupervised learning algorithms including deep learning, clustering , etc.
Learn about classification data and Machine Learning models
Select the best algorithms applied to Machine Learning
Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering
more...
Course Agenda and Topics:
The Basics of Machine Learning
Machine Learning Techniques, Tools and Algorithms
Data and Data Science
Review of Terminology and Principles
Applied Artificial Intelligence (AI) and Machine Learning
Popular Machine Learning Methods
Learning Applied to Machine Learning
Principal Component Analysis
Principles of Supervised Machine Learning Algorithms
Principles of Unsupervised Machine Learning
Regression Applied to Machines Learning
Principles of Neural Networks
Large Scale Machine Learning
Introduction to Deep Learning
Applying Machine Learning
Overview of Algorithms
Overview of Tools and Processes
Call us today at +1-972-665-9786. Learn more about course audience, objectives, outlines, pricing. Visit our website links below.
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
Azure Machine Learning and ML on PremisesIvo Andreev
Machine Learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis.Microsoft offers Azure Machine Learning, while Amazon offers Amazon Machine Learning and Google offers the Google Prediction API - now depricated and replaced by Google ML engine based on TensorFlow. Software products such as MATLAB support traditional, non-cloud-based ML modeling.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
Machine Learning Engineer Salary, Roles And Responsibilities, Skills and Resu...Simplilearn
This presentation on "Machine Learning Engineer Salary, Skills & Resume" will help you understand who is a Machine Learning engineer, the salary of a Machine Learning engineer, skills required to become a Machine Learning engineer and what a Machine Learning engineer's resume should look like. Machine Learning is the study of algorithms and data models that computer systems utilize to perform specific tasks without using instructions, relying on previous patterns. To make this possible, a Machine Learning engineer is required. Now, let us get started and understand what the job of a Machine Learning engineer looks like.
Below are the topics that we will be discussing in the presentation:
1. Introduction to Machine Learning
2. Responsibilities of a Machine Learning engineer
3. Salary Trends of a Machine Learning engineer
4. Skills of a Machine Learning engineer
5. Resume of a Machine Learning engineer
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
PREDICT THE FUTURE , MACHINE LEARNING & BIG DATADotNetCampus
Scopri come utilizzare Azure Machine Learning, un servizio cloud che consente alle aziende, università, centri di ricerca e sviluppatori di incorporare e sfrutturare nelle loro applicazioni funzionalità di apprendimento automatico e analisi predittiva su enormi set di dati. Tramite Azure ML Studio possiamo creare, testare, attuare e gestire soluzioni di analisi predittiva e apprendimento automatico nel cloud tramite un qualunque web browser. Durante la sessione si darà un saggio attraverso un esempio di analisi predittiva sul Flight Delay.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
Delivered at Pittsburgh Tech Fest - 6/10/2017
Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate.
It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks.
Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning.
In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data.
You will learn to:
* Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications
* How to Integrate Azure Machine Learning into your existing Applications and Processes
* Create Azure Machine Learning Experiments
* Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes
* Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes
* Learn various concepts for abstracting and managing services and api's.
Delivered @ MusicCityCode 6/2/2017
Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate.
It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks.
Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning.
In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data.
You will learn to:
* Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications
* How to Integrate Azure Machine Learning into your existing Applications and Processes
* Create Azure Machine Learning Experiments
* Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes
* Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes
* Learn various concepts for abstracting and managing services and api's.
Integrating Azure Machine Learning and Predictive Analytics with SharePoint O...Bhakthi Liyanage
Windows Azure Machine Learning and Data Analytics platform offers a streamlined experience, from setting up with only a web browser to using drag-and-drop gestures and simple data-flow graphs to set up experiments. Azure Machine Learning Studio features a library of time-saving sample experiments, R and Python packages, and best-in-class algorithms from Microsoft businesses like Xbox and Bing. Learn how the Azure Machine Learning service in the cloud lets you easily build, deploy, and share advanced analytics solutions into your SharePoint platform. Attendees will also gain knowledge on special considerations that should be taken in to account when creating analytical models. The demo will walk you through creating an analytic model in Azure ML studio and consume the model within SharePoint online.
[db tech showcase Tokyo 2018] #dbts2018 #B27 『Discover Machine Learning and A...Insight Technology, Inc.
[db tech showcase Tokyo 2018] #dbts2018 #B27
『Discover Machine Learning and ADWC - The Perfect Combination』
Data Intensity - Director of Innovation Francisco Munoz Alvarez 氏
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
Building High Available and Scalable Machine Learning ApplicationsYalçın Yenigün
The slide contains some high level information about some machine learning algorithms, cross validation and feature extraction techniques. It also contains high level techniques about high available and scalable ML products.
Using Azure Machine Learning Models describes how to provide end-users with Azure Machine Learning services, and how to share data generated from Azure Machine Learning models.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
Simplifying AI and Machine Learning with Watson StudioDataWorks Summit
Are you seeing benefits from big data, AI and machine learning? Some companies are challenged by the complexity of the tools, access to quality data and the ability to operationalize these technologies. IBM’s Watson Studio addresses the needs of developers, data scientists and business analysts – who need to create, train and deploy machine and deep learning models, analyze and visualize data – all in an easy-to-use platform. Watson Studio supports Apple’s Core ML with Watson Visual Recognition service. It provides a suite of tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy models at scale. When coupled with IBM Watson Knowledge Catalog, it enables companies to create a secure catalog of AI assets including datasets, documents and models. In this session, you will learn how to use these new offerings to solve real world business problems and infuse AI into your business to drive innovation.
Speaker
Sumit Goyal, IBM, Software Engineer
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
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Similar to Azure Machine Learning Dotnet Campus 2015 (20)
Codemotion Session - 2016 Milan.
Session about the development of Chat Bot Application ( The Game Rock / Paper / Scissors ) powered by Machine Learning.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
5. Machine Learning / Predictive Analytics
Vision Analytics
Recommenda-tion
engines
Advertising
analysis
Weather
forecasting for
business planning
Social network
analysis
Legal
discovery and
document
archiving
Pricing analysis
Fraud
detection
Churn
analysis
Equipment
monitoring
Location-based
tracking and
services
Personalized
Insurance
Machine learning & predictive
analytics are core capabilities
that are needed throughout
your business
6. Machine Learning Overview
• Formal definition: “The field of machine learning is concerned with the
question of how to construct computer programs that automatically improve
with experience” - Tom M. Mitchell
• Another definition: “The goal of machine learning is to program computers
to use example data or past experience to solve a given problem.” – Introduction to
Machine Learning, 2nd Edition, MIT Press
• ML often involves two primary techniques:
• Supervised Learning: Finding the mapping between inputs and outputs using correct
values to “train” a model
• Unsupervised Learning: Finding patterns in the input data (similar to Density Estimates in
Statistics)
7. Machine Learning
Data:
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Rules, or Algorithms:
about, Learning, language – Spelling and sounding builds words
Learning about language. – Words build sentences
Learning, or Abstraction:
Any new understanding proceeds from previous knowledge.
8. Traditional programming VS Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program/Algorithms
Traditional Programming
Machine Learning
Program can predict the output!
9. No, more like gardening
Gardener = You
Seeds = Algorithms
Nutrients = Data
Plants = Programs
10. Sample Application
• Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• Robotics
• Information extraction
• Social networks
• Debugging
• [Your favorite area]
11. Types of Learning
• Supervised (inductive) learning
• Training data includes desired outputs
• Unsupervised learning
• Training data does not include desired outputs
• Semi-supervised learning
• Training data includes a few desired outputs
12. Machine Learning Problem
Classification or
Categorization
Clustering
Regression
Dimensionality
reduction
Supervised Learning Unsupervised Learning
DiscreteContinuous
13. Machine Learning Example
Predict function F(X) for new examples X
Discrete F(X): Classification
Continuous F(X): Regression
F(X) = Probability(X): Probability estimation
Given examples of a function (X, F(X))
14. Machine Learning Example
• Training: given a training set of labeled examples {(x1,y1), …, (xN,yN)}, estimate the prediction
function f by minimizing the prediction error on the training set
• Testing: apply f to a never before seen test example x and output the predicted value y = f(x)
output prediction
function
Image
feature
y = f(x)
Apply a prediction function to a feature representation of the image to get the desired
output:
F( ) = «apple»
F( ) = «tomato»
F( ) = «dog»
15. Supervised Learning
1. Used when you want to predict unknown answers from answers you already
have
2. Data is divided into two parts: the data you will use to “teach” the system (data
set), and the data to test the algorithm (test set)
3. After you select and clean the data, you select data points that show the right
relationships in the data. The answers are “labels”, the
categories/columns/attributes are “features” and the values are…values.
4. Then you select an algorithm to compute the outcome. (Often you choose more
than one)
5. You run the program on the data set, and check to see if you got the right answer
from the test set.
6. Once you perform the experiment, you select the best model. This is the final
output – the model is then used against more data to get the answers you need
17. Unsupervised Learning
1. Used when you want to find unknown answers – mostly groupings -
directly from data
2. No simple way to evaluate accuracy of what you learn
3. Evaluates more vectors, groups into sets or classifications
4. Start with the data
5. Apply algorithm
6. Evaluate groups
18. Unsupervised Learning
Example 1 example A Example 2 example
B Example 3 example C
example A example B example C
Example 1 Example 2 Example 3
The clustering strategies have more tendency to transitively group points even if they are
not nearby in feature space
19. Microsoft + Machine Learning
• 1999: Outlook Included email filers for spam or junk mail in Microsoft Outlook.
• 2004: Search Started incorporating machine learning aspects into Microsoft search engine
technology.
• 2005: SQL Server 2005 Enabled “data mining” processing capabilities over large databases.
• 2008: Bing Maps Incorporated machine learning traffic prediction services.
• 2010: Kinect Incorporated the ability to watch and interpret user gestures
• 2014: Azure Machine Learning (preview) Made years of predictive analytics innovations
available to all via the Azure cloud platform.
• 2014: Microsoft launches “Cortana” Introduced a digital assistant based on the popular Halo
video game series, which heavily leverages machine learning to become the perfect digital
companion for today’s mobile society.
• 2014: Microsoft Prediction Lab Launched a stunning real-world example, the “real-time
prediction lab” at www.prediction.microsoft.com
Microsoft has a long and deep history of using applied predictive analytics and machine
learning
21. Azure Learning Machine Workflow
• Data It’s all about the data. Here’s where you will acquire, compile, and analyze
testing and training data sets for use in creating Azure Machine Learning
predictive models.
• Create the model Use various machine learning algorithms to create new models
that are capable of making predictions based on inferences about the data sets.
• Evaluate the model Examine the accuracy of new predictive models based on
ability to predict the correct outcome, when both the input and output values are
known in advance. Accuracy is measured in terms of confidence factor
approaching the whole number one.
• Refine and evaluate the model Compare, contrast, and combine alternate
predictive models to find the right combination(s) that can consistently produce
the most accurate results.
• Deploy the model Expose the new predictive model as a scalable cloud web
service, one that is easily accessible over the Internet by any web browser or
mobile client.
• Test and use the model Implement the new predictive model web service in a
test or production application scenario.
22. Azure Machine Learning algorithms
• Classification algorithms These are used to classify data into different categories that can
then be used to predict one or more discrete variables, based on the other attributes in the
dataset.
• Regression algorithms These are used to predict one or more continuous variables, such as
profit or loss, based on other attributes in the dataset.
• Clustering algorithms These determine natural groupings and patterns in datasets and are
used to predict grouping classifications for a given variable.
Supervised learning
• Classification algorithms
• Regression algorithms
Unsupervised learning
• Clustering algorithms
24. Deploying a prediction model
The deployment of a new prediction model takes the form of exposing a web service
which can then be invoked via the Representational State Transfer (REST) protocol.
• Azure Machine Learning web services can be called via two different exposed interfaces:
• Single, request/response-style calls.
• “Batch” style calls, where multiple input records are passed into the web service in a single call and the corresponding
response contains an output list of predictions for each input record.
When a new machine learning prediction model is exposed on the Web, it performs the
following operations:
• New input data is passed into the web service in the form of a JavaScript Object Notation (JSON) payload.
• The web service then passes the incoming data as inputs into the Azure Machine Learning prediction model engine.
• The Azure Machine Learning model then generates a new prediction based on the input data and returns the new
prediction results to the caller via a JSON payload.
25. AzureML
• Why AzureML?
• Setting up a Microsoft Azure Account
• Setting up a Storage Account
• Loading Data
• Setting up an AzureML Workspace
• Accessing AzureML Studio
• AzureML Studio Tour
26. Azure ML Getting Started
Option 1 Take advantage of a free Azure trial offer at
http://azure.microsoft.com/en-us/pricing/free-trial
Option 2 Take advantage of the (free) Azure Machine Learning
trial offer at https://studio.azureml.net/Home
How to get started ?
27. Azure Machine Learning workspaces
• Workspace
• Experiments
• Azure ML Studio
• Web Services
• Datasets
• Modules
29. Create your first Azure Machine Learning experiment
• Defining the problem you want to solve
• e.g. figure out if you like certain movie what’s another movie you should watch? (movie
recommendation )
• Designing a Solution Using AzureML
1. Creating an experiment
2. Load a Data Set
3. Create the Experiment
4. Add Transformations and Filters
5. Create the Experiment Path and apply Algorithms
6. Save and Run the Model
7. Publish the Model
8. Use the Model
• Saving and Running
• Publishing and Accessing
30. Create your first Azure Machine Learning Experiment
Problem :
Let’s start with a simple, real-world scenario such as predicting the next move to watch based on
your movie history.
Steps :
• Download dataset from a Azure Machine repository
• Upload data into an Azure Machine Learning experiment
• Create a new Azure Machine Learning experiment
• Clean Dataset, Train & Score The Model
• Create Recommender experiment
• Expose the model as a web service
• Testing web service
31. Demo
Azure Machine Learning Demo
Predict ratings for a given user and movie
Recommend movie to a given user
Find users related to a given user
Find items related to a given item
33. API examples
• Difference in Proportions Test
• Lexicon Based Sentiment Analysis
• Forecasting-Exponential Smoothing
• Forecasting - ETS+STL
• Forecasting-AutoRegressive Integrated
Moving Average (ARIMA)
• Normal Distribution Quantile Calculator
• Normal Distribution Probability Calculator
• Normal Distribution Generator
• Binomial Distribution Probability Calculator
• Binomial Distribution Quantile Calculator
• Binomial Distribution Generator
• Multivariate Linear Regression
• Survival Analysis
• Binary Classifier
• Cluster Model
datamarket.azure.com
35. Sell your Azure Machine Learnig Service
Publish Azure Machine Learning Web Service to the Azure Marketplace
• http://azure.microsoft.com/en-us/documentation/articles/machine-learning-publish-web-service-to-
azure-marketplace
• http://datamarket.azure.com
Azure Marketplace
36. Check out the Machine Learning marketplace
at datamarket.azure.com
Learn how to on-board to the
marketplace off azure.com at
Machine Learning Center
Visit the Microsoft booth to talk
to the team
Next Steps
Machine learning can be described as computing systems that improve with experience. It can also be described as a method of turning data into software. Whatever term is used, the results remain the same; data scientists have successfully developed methods of creating software “models” that are trained from huge volumes of data and then used to predict certain patterns, trends, and outcomes.
Predictive analytics is the underlying technology behind Azure Machine Learning, and it can be simply defined as a way to scientifically use the past to predict the future to help drive desired outcomes.
Machine learning can be described as computing systems that improve with experience. It can also be described as a method of turning data into software. Whatever term is used, the results remain the same; data scientists have successfully developed methods of creating software “models” that are trained from huge volumes of data and then used to predict certain patterns, trends, and outcomes.
Apprendimento Supervisionato
Apprendimento Non Supervisionato
I Dati + Rule = Machine Learning
Under traditional programming models, programs and data are processed by the computer to produce a desired output, such as using programs to process data and produce a report
When working with machine learning, the processing paradigm is altered dramatically. The data and the desired output are reverse-engineered by the computer to produce a new program
The power of this new program is that it can effectively “predict” the output, based on the supplied input data. The primary benefit of this approach is that the resulting “program” that is developed has been trained (via massive quantities of learning data) and finely tuned (via feedback data about the desired output) and is now capable of predicting the likelihood of a desired output based on the provided data.
A classic example of predictive analytics can be found everyday on Amazon.com; there, every time you search for an item, you will be presented with an upsell section on the webpage that offers you additional catalog items because “customers who bought this item also bought” those items. This is a great example of using predictive analytics and the psychology of human buying patterns to create a highly effective marketing strategy
Many examples of predictive analytics can be found literally everywhere today in our society:
Spam/junk email filters These are based on the content, headers, origins, and even user behaviors (for example, always delete emails from this sender).
Mortgage applications Typically, your mortgage loan and credit worthiness is determined by advanced predictive analytic algorithm engines.
Various forms of pattern recognition These include optical character recognition (OCR) for routing your daily postal mail, speech recognition on your smart phone, and even facial recognition for advanced security systems.
Life insurance Examples include calculating mortality rates, life expectancy, premiums, and payouts.
Medical insurance Insurers attempt to determine future medical expenses based on historical medical claims and similar patient backgrounds.
Liability/property insurance Companies can analyze coverage risks for automobile and home owners based on demographics.
Credit card fraud detection This process is based on usage and activity patterns. In the past year, the number of credit card transactions has topped 1 billion. The popularity of contactless payments via near-field communications (NFC) has also increased dramatically over the past year due to smart phone integration.
Airline flights Airlines calculate fees, schedules, and revenues based on prior air travel patterns and flight data.
Web search page results Predictive analytics help determine which ads, recommendations, and display sequences to render on the page.
Predictive maintenance This is used with almost everything we can monitor: planes, trains, elevators, cars, and yes, even data centers.
Health care Predictive analytics are in widespread use to help determine patient outcomes and future care based on historical data and pattern matching across similar patient data sets.
Supervised learning is a type of machine learning algorithm that uses known datasets to create a model that can then make predictions. The known data sets are called and include input data elements along with known response values
In the case of unsupervised machine learning, the task of making predictions becomes much harder. In this scenario, the machine learning algorithms are not provided with any kind of known data inputs or known outputs to generate a new predictive model.
In the case of unsupervised machine learning, the success of the new predictive model depends entirely on the ability to infer and identify patterns, structures, and relationships in the incoming data set.
Classification algorithms These are used to classify data into different categories that can then be used to predict one or more discrete variables, based on the other attributes in the dataset.
Regression algorithms These are used to predict one or more continuous variables, such as profit or loss, based on other attributes in the dataset.
Clustering algorithms These determine natural groupings and patterns in datasets and are used to predict grouping classifications for a given variable.
One of the most common unsupervised learning algorithms is known as which is used to find hidden patterns or groupings within data sets.
Some common examples of cluster analysis classifications would include the following:
Socioeconomic tiers Income, education, profession, age, number of children, size of city or residence, and so on.
Psychographic data Personal interests, lifestyle, motivation, values, involvement.
Social network graphs Groups of people related to you by family, friends, work, schools, professional associations, and so on.
Purchasing patterns Price range, type of media used, intensity of use, choice of retail outlet, fidelity, buyer or nonbuyer, buying intensity.
The other type of approach to unsupervised machine learning is to use a reward system, rather than any kind of teaching aids, as are commonly used in supervised learning. Positive and negative rewards are used to provide feedback to the predictive model when it has been successful.
Features or vectors Known data that is used as an input element for making a prediction.
Labels or supervisory signal Represents the known outcomes for the corresponding features for the input record.
Not used (default) Not used by predictive algorithms for inferring a new predictive model
Watson is the first cognitive system in the world
Offer a lot of service like face, voice , image recognition , question & answer
But in this moment don’t offer any type of tool to costumize the power of these services.
The basic process of creating Azure Machine Learning solutions is composed of a repeatable pattern of workflow steps that are designed to help you create a new predictive analytics solution in no time. The basic steps in the process are summarized in Figure
Data It’s all about the data. Here’s where you will acquire, compile, and analyze testing and training data sets for use in creating Azure Machine Learning predictive models.
Create the model Use various machine learning algorithms to create new models that are capable of making predictions based on inferences about the data sets.
Evaluate the model Examine the accuracy of new predictive models based on ability to predict the correct outcome, when both the input and output values are known in advance. Accuracy is measured in terms of confidence factor approaching the whole number one.
Refine and evaluate the model Compare, contrast, and combine alternate predictive models to find the right combination(s) that can consistently produce the most accurate results.
Deploy the model Expose the new predictive model as a scalable cloud web service, one that is easily accessible over the Internet by any web browser or mobile client.
Test and use the model Implement the new predictive model web service in a test or production application scenario. Add manual or automatic feedback loops for continuous
The formula for producing a supervised learning model is expressed in Figure illustrates the general flow of creating new prediction models based on the use of supervised learning along with known input data elements and known outcomes to create an entirely new prediction model. A supervised learning algorithm analyzes the known inputs and known outcomes from training data
Predictive models can generally achieve better accuracy results when provided with new (and more recent) datasets. The prediction evaluation process can be expressed as shown in Figure The evaluation process for new prediction models that use supervised learning primarily consists of determining the accuracy of the new generated model.
Once a new predictive model has been generated from good training datasets and carefully evaluated for accuracy, then it can be deployed for use in testing or production usage scenarios. The new production prediction process can be expressed as shown in Figur
Azure Machine Learning workspaces Represent a discrete “slice” of the Azure Machine Learning tool set that can be partitioned by Workspace name and Workspace owner
Azure Machine Learning experiments Experiments are created within Azure Machine Learning workspaces and represent the primary method of enabling an iterative approach to rapidly developing Azure Machine Learning solutions.
Azure ML Studio This is the primary interactive predictive analytics workbench that is accessed from within an Azure Machine Learning workspace to allow a data scientist to create Azure Machine Learning experiments via a drag-and-drop visual designer interface. Access to a unique Azure ML Studio environment is governed from within an Azure Machine Learning workspace.
Azure Machine Learning web services These represent Azure Machine Learning experiments that have been exposed as public APIs over the Internet in the form of the Azure Machine Learning REST API. These services are generally exposed as a simple web service, or as an OData endpoint.
Datasets This is data that has been uploaded to Azure ML Studio so that it can be used in the prediction modeling process.
Modules These are algorithms that you can apply to your data. Azure ML Studio has a number of modules ranging from data ingress functions to training, scoring, and validation processes. Here are some examples of included modules:
Convert to ARFF Converts a .NET serialized dataset to ARFF format. ARFF is a common machine learning construct and stands for Attribute-Relation File Format. It is commonly defined as an ASCII text file that describes a list of instances sharing a set of attributes.
Elementary Statistics Calculates elementary statistics such as mean, standard deviation, and so on.
Linear Regression Creates an online gradient, descent-based, linear regression model.
Score Model Scores a trained classification or regression model