"Artificial Intelligence & Machine Learning Solutions | Eatech Pvt. Ltd – Leadin

Sep 26, 2025

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Eatech Pvt. Ltd delivers cutting-edge Artificial Intelligence & Machine Learning solutions, helping businesses harness the power of automation, data analytics, and intelligent systems. As one of the Top Machine Learning Companies in India, we specialize in AI-driven applications, predictive modeling, and smart algorithms that drive innovation and efficiency across industries. Our team ensures scalable, secure, and customized AI & ML services tailored to meet diverse business needs.

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"Artificial Intelligence & Machine Learning Solutions | Eatech Pvt. Ltd – Leadin

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Artificial Organs

Artificial Organs. BG. Index. What are artificial organs? Artificial skin Dialysis Artificial pancreas Artificial hearts Artificial kidney Artificial liver Artificial lung. What are artificial organs?. Shortage of donor organs Substitute for natural organs

1.23k views • 27 slides

Newbie Video Cash Machine Review & Newbie Video Cash Machine $16,700 bonuses

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35 MM & 50 MM F MOUNT MACHINE VISION LENSES – MACHINE VISION INDIA

Machine Vision India is the first company in India manufacturing large format F-Mount Machine Vision lens. The company designs, develops & manufacture large format F-Mount. We holds very strong expertise in offering our customer's dedicated one-stop solution for all their optics requirements in building their machine vision imaging systems. "Recognized as best Machine Vision lens Supplier in the Industry" With strong in-house R&D & design efforts, our 35 MM & 50 MM F Mount lens are developed with optimal precision techniques & produce high quality images. Our camera lens are actively implemented in applications like in Machine Vision, color sorter, ITS/Surveillance, Medical Imaging Applications & many others etc. "Machine Vision India owned & maintained by BalaJi MicroTechnologies (BMT), India. For More information Please Visit Product page :- http://machine-vision-india.in/balaji-optics-f-mount-lenses.html http://machine-vision-india.in/balaji-optics-machine-vision-lenses.html Sales Contact: Office Tel:- 91-129-4006203, 91-129-6561300 Office Mobile:- 919643047670 Skype ID(For Product Inquiry): bmt.overseas Whatsapp(For Product Inquiry): 919643047670 Email (product inquiry):- sales.overseas@balaji-microtechnologies.com sales.india@balaji-microtechnologies.com machine vision, f mount lenses, f mount lens machine vision, f mount wide angle lens, f mount camera lens, f mount lens, machine vision lens, machine vision lenses F Mount Lenses, F mount lens, Machine Vision lens, machine vision, 50 mm F Mount Lens, 35mm F Mount Lenses, Machine Vision Lenses, colour sorter, Color sorter, Line scan camera, Line Scan Cameras.

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What is the best artificial grass price in Delhi

Artificial Grass is recognized as the leading manufactured lawn brand and development occurs in Holland. We spread Artificial Grass only through trade companions; our Authorized Retailers. Amongst our lovers are artificial turf specialists, panorama designers, companies, and all inspired experts with a center in landscaping. Frequently growing, we as a groundbreaking Artificial Grass Price in Delhi distributor are always looking for new trade lovers. We want for companions that are as interested in Artificial Grass Price in Delhi even as we are and wouldn't accept significantly less than top-quality products. Synthetic lawn products provided by Artificial Grass will be the most modern-day lawns you can acquire. Our turf materials are V-shaped so they'll remind of the true grass and make sure they are look as just like natural form as you possibly can. We have applied Mint Technology inside our artificial turf products that permit smooth, matte color and minimal possible reflection. Do you imagine a perfect Artificial Grass Price in Delhi with fresh inexperienced coloring? Well your fantasy might just become a reality with Artificial Grass. This unnatural Artificial Grass Price in Delhi product involves three hues of renewable with thatch. Artificial Grass has a properly fresh green coloring which appears very natural. Furthermore it includes lush turf fibers of 36mm rendering it a beautiful abundant green artificial lawn product. Manufactured turf with a fairly neat thickness which is both resilient and smooth.

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Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation | Edureka

** Machine Learning Training with Python: https://www.edureka.co/python ** This Machine Learning Interview Questions and Answers PPT will help you to prepare yourself for Data Science / Machine Learning interviews. This PPT is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Machine Learning core-concepts, Machine Learning using Python and Machine Learning Scenarios. Below are the topics covered in this tutorial: 1. Machine Learning Core Interview Question 2. Machine Learning using Python Interview Question 3. Machine Learning Scenario based Interview Question Check out our playlist for more videos: http://bit.ly/2taym8X 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

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How can e-learning reform the traditional education system?

What is traditional learning? Traditional learning relates to any type of training in a regular school setting using classic paper and pencil techniques. In brief, traditional relates to any type of teaching involving techniques typically used in any college. This may include typical instruction-style lecture, tasks and tests, and student homework, classical teaching pedagogy and teacher training, use of assigned textbooks and other course content, and an in-person instruction scheme. Traditional learning continues to be known to most learners today. While most of the components outlined above will be present, topics, textbooks, and some learning styles may differ. What is e-learning? Broadly speaking, e-learning relates to any kind of training that is electronically transmitted, generally over the internet. Think of any kind of educational material you might have consumed on platforms such as YouTube or other streaming sites or internet classes through universities or even virtual classrooms where students and teachers sign up remotely u2013all of these would count as examples of e-learning. E-learning is an inevitable evolution of the educational ecosystem with the growing ubiquity and fast spread of technology. It can be something as tiny as using electronic materials or something as enormous as massive open online courses that enable anyone to sign up to learn something new, anywhere and at any moment. Due to the many advantages it transfers to all stakeholders in the academic ecosystem, e-learning is increasingly becoming a feature in classrooms across school and institutes in the world. Importance of e-learning Overall, traditional learning is expensive, takes a long time and the results can vary. The importance of e-learning is now a given fact and it can offer an alternative that is much faster, cheaper and potentially better. In the fast-paced world of e-learning, the available technologies to make a course new and exciting are always changing, and course content can and should be updated quickly to give students the very latest information. This is especially important if the e-learning training is being given to employees in a sector where keeping up-to-date on industry developments is of the utmost importance. This is one of the reasons why many schools are now offering training via e-learning other reasons include low costs and the ability for students to study in their own time and place. What are the benefits of e-learning over the traditional education system? The advantages of including e-learning education system methodologies within the traditional education system or even implementing e-learning options at your school and college are many. Some key benefits of an e-learning education system are discussed in further detail below: One Time Setup Cost Many schools and institutes are hesitating to invest in the e-learning education system as they think that it would be too big to bear the price of such an effort. This is mainly due to infrastructure change. While schools and institutes are used to invest in products such as pens and paper, desks and benches, blackboards and classrooms, it makes sense that the cost of setting up an completely fresh teaching scheme must be closely adjusted against the budget of the school or institute. However, with most techniques or instruments in the e-learning education scheme, the price is mainly in the configuration, which is then mitigated over time because of how much it is used. This implies that the price is a one-time price u2013 an investment in the future where there are plenty of yields on such an investment. Increases Inclusivity for Stakeholders The system of e-learning education may seem very modern and cutting-edge as it uses the recent technology in the educational ecosystem. This does not imply, however, that it is in any manner beyond the reach of the common person or the exclusionary. Indeed, with the development of low-cost technology and the proliferation of internet connectivity and internet-enabled phones in even the most remote parts of the world, e-learning education system is becoming increasingly feasible in today's era. This is particularly crucial for marginalized communities that do not usually have access, infrastructure or the funding needed to construct and maintain traditional education systems. E-learning enables anyone with an internet connection and an internet-enabled device to sign up for a classroom that is revolutionary for learners who are often ignored or excluded from traditional learning systems. Easy to Scale Up Technology for the e-learning education system is built in such a way that there are continual improvement and development being done on the tools that support it. Whether it is a new version of an app with extra features or just advancing hardware, e-learning education system makes it easy for users to keep up with the times through easy patches or increasingly low-cost devices. This means that whether the school or college is small or large, it does make it easier to scale up to meet the present needs of the school when the e-learning education system has been implemented. Being able to easily update via a patch or software upgrade or having the rare hardware update also end up being cost-effective ensures that schools can scale up easily and stay up to date. Technology Embracing In the modern world, the use of technology is becoming ubiquitous. Entire jobs rely on it and there is no industry that has not been affected and embraced technology in some form of the other. It is important for schools and colleges to also prepare their children for this modern world by embracing the use of technology at the very earliest. The easiest and most beneficial way to do this would be through the implementation of e-learning education system, which ensures that students familiarise themselves with the use of various electronic or internet-enabled tools and platforms in their day to day lives, to perform their day to day tasks. Not only do students then become comfortable using technology, but they also understand the systems of thinking that underlie it making them natural learners in an increasingly technology embracing world. Lowered Overheads and Labor Cost Think of the endless overheads of running a school or college u2013 from simple things like pens and papers to storage space for all the physical records that need to be maintained. A traditional education system comes with massive overhead costs. E-learning on the other hand naturally eliminates many of these costs, since so many of the tasks and processes take place electronically. From classroom assignments being submitted using school management systems like Paatham to even the elimination of the need for a physical space to call a classroom, e-learning education system drastically reduces overhead costs for all stakeholders in the educational ecosystem. The same also applies to labor costs with a traditional education system. Many personnel is required to manage the logistical and temporal demands of a traditional classroom setting u2013 from study materials that are physical to the natural restrictions in the number of students that can be fit in a physical classroom. With e-learning education system such restrictions do not apply. Instead, since many tasks and processes are automated, the need for a human agent to perform them is eliminated resulting in downsizing and reduction in labor costs. This kind of reduction in costs is vital for budget-conscious industries such as education since valuable funds can then be redirected towards more worthy causes that would benefit all stakeholders in the educational ecosystem. Conclusion: Can e-learning replace traditional education systems? As we move into the future, it is likely that educators and management will continue to take advantage of the many benefits of e-learning systems. However, some classic traditional education system practices will remain. Rather than creating a false dichotomy where one is forced to choose between a traditional education system and e-learning it is more likely that a new form of systemic learning will emerge that is a blend of the two. We are already seeing this in practice u2013 with virtual classrooms becoming a reality thanks to tools like Paatham learning management system that allows for a seamless experience, for teachers to reach students in remote parts of the world, through massive online open courses through services. So while it is unlikely that the traditional education system will ever be completely eradicated, it will continue to evolve to be inclusive of new forms of technology and learning, via the e-learning education system.

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Perceptual Learning Styles of Students and its Effect to Their Academic Performance

This study was entitled Perceptual Learning Styles of Students and Its Effect to their Academic Performance. The study aimed to know the Perceptual Learning Styles of Grade 12 GAS Students and its effect to their Academic Performance. It also aimed to answer the following objectives What are the Learning Styles of the students What are the Academic Performances of the students in terms of learning style and Is there a significant difference between Academic Performance of the students in terms of their learning style This was a non experimental study and descriptive comparative since the researchers wanted to know the Perceptual Learning Styles of the Grade 12 General Academic Strand Students and compare their Academic Performance when grouped according to their Learning Styles. This was done by giving them survey questionnaires. The students that were included in the study comes from the five Grade 12 General Academic Strand sections that has a total population of 232. The total calculated sample size of respondents in this study using Slovin's formula is 147 with 5 percent margin of error. Names of all the students were taken and systematic sampling technique was used to get 147 random respondents. The instrument used in gathering data is in the form of self assessment questionnaires by Victoria Chislett MSc and Alan Chapman of 2005. The questionnaire was composed of 30 statements. 10 items corresponds to each of the Learning Styles. These were given to know the Perceptual Learning Styles of Grade 12 GAS Students in Agusan Sur National High School based on Fleming's VAK Learning Style Model. Data were analysed to identify, describe and explore the different learning style preference of Grade 12 GAS students of Agusan Sur National High School and determine its effect to their academic performance. The questionnaire includes the respondent's profile such as name and their average grade during the first semester. A total of 147 self assessment questionnaires by Victoria Chislett MSc and Alan Chapman of 2005 were distributed to the selected respondents. It was composed of 30 statements where 10 of which corresponds to each of the learning style identified by Neil Fleming in his VAK Learning Style Model. The following findings were obtained based on the analysis and interpretation of data. The Perceptual Learning Style of the Grade 12 General Academic Strand are arranged in descending order as follows Visual Learning 48 , Auditory Learning 30 , Kinesthetic Learning 17 , Visual Auditory 5 . The Academic Performance of the students in terms of their Perceptual Learning Style reveals that students who performs excellently in class are mostly Visual and Auditory learners. Both Kinesthetic and Visual Auditory learners respond desirably to class. Since the calculated value 0.002 is lesser than the probability value 0.005, there is a significant difference. Therefore, the students' Perceptual Learning Style affects their Academic Performance. It is therefore concluded that Visual and Auditory are the commonly used perceptual learning style of the students. Also, only few are inclined in Kinesthetic and the combination of Visual and Auditory. In terms of Perceptual Learning Style, the students who performs outstandingly are mostly Visual and Auditory Learners. Both Kinesthetic and Visual Auditory learners respond desirably to class. The Perceptual Learning Style of the students affects their Academic Performances. As synthesized from the findings and conclusions, the following recommendations were made The Visual aids that represent ideas using methods other than words, such as graphs, charts, diagrams, symbols, etc. will be very useful for Visual learners. Lectures, discussions and playing of audio tapes best suits the Auditory learners Fleming and Mills, 1992 . Kinesthetic students learn best by being active, and they often rely on physical interaction in order to master a concept Sarasin, 1998 therefore, role playing and other physical activities will be beneficial for them. On the other hand, the identified bimodal learners Visual Auditory can benefit from the combination of the teaching strategies that fits to both mentioned learning style. Rona A. Banas "Perceptual Learning Styles of Students and its Effect to Their Academic Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18992.pdf Paper URL: http://www.ijtsrd.com/humanities-and-the-arts/education/18992/perceptual-learning-styles-of-students-and-its-effect-to-their-academic-performance/rona-a-banas

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Machine Learning And its Applications in different Sectors

Machine Learning And its Applications in different Sectors Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being programmed. Machine Learning is used in situations where perfection is not expected, the goal is not to achieve predictions that are good enough to be useful. The value of machine learning technology has been recognized by companies across several industries that deal with huge volumes of data. Letu2019s have a look how some sectors / domains are implementing machine learning: Financial Services Companies in the financial sector are able to identify key insights in financial data as well as prevent any occurrences of financial fraud, with the help of machine learning technology. Usage of cyber surveillance helps in identifying those individuals or institutions which are prone to financial risk, and take necessary actions in time to prevent fraud. Usage of cyber surveillance helps in identifying those individuals or institutions which are prone to financial risk, and take necessary actions in time to prevent fraud. Marketing and Sales Companies are using machine learning technology to analyze the purchase history of their customers and make personalized product recommendations for their next purchase. ML helps to capture, analyze, and use customer data which helps to provide a personalized shopping experience and this is the future of sales and marketing. Government Government agencies like utilities and public safety have a specific need for ML, as they have multiple data sources, which can be mined for identifying useful patterns and insights. For example, sensor data can be analyzed to identify ways to minimize costs and increase efficiency. ML can also be used to minimize identity thefts and detect fraud. Healthcare ML is becoming a fast-growing trend in healthcare. Sensors in wearable provide real-time patient information, such as overall health condition, heartbeat, blood pressure and other vital parameters. The technology also empowers medical experts to analyze data to identify trends that facilitate better diagnoses and treatment. Transportation Transportation firms and delivery organizations are increasingly using machine learning technology to carry out data analysis and data modeling to make informed decisions and help their customers make smart decisions when they travel. Based on the travel history and pattern of traveling across various routes, machine learning can help transportation companies predict potential problems that could arise on certain routes, and accordingly advise their customers to opt for a different route. Oil and Gas Right from analyzing underground minerals and finding new energy sources to streaming oil distribution, Machine Learning applications for this industry are vast and are still expanding. This is perhaps the industry that needs the application of machine learning the most. By leveraging insights obtained from the data, companies are able work in an efficient manner to control costs as well as get an edge over their competitors. Visit http://gnexttechllc.com/ for Digital Marketing Company, Digital Advertising Company, SEO Service Provider, SEO Company, Social Media Marketing Company, Web Design Development Company, Web Design Company, Graphics Design Company, Software Company, Software Development Company, Mobile Apps Development Company.

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Machine Learning (ML) company in UK

Machine Learning (ML) company in USA Techsolvo is top machine learning company in USA. Techsolvo is listed in world top machine learning companies in 2020. Our well knowledge team is handled over the project of Machine Learning and deals with high accuracy and proficiency of our clients. Techsolvo expert in developing Web and Mobile app, Blockchain development, Machine Learning, Internet of Things and Artificial Intelligence. For more information visit www.techsolvo.com #machine learning company in USA #machine learning companies in 2020

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machine learning institute in Hyderabad

Machine Learning is Fun. Do you want to learn Machine Learning? Hey there! Welcome here in the zone of Machine Learning. Without wasting the time letu2019s start. Machine learning (ML) is used in various aspects of our lives today. It helps us get from point A to point B, suggests what to do with pressing issues, and is getting better at holding conversations. Your Artificial Intelligence career with the twin engines of Python and R programming. Join our Machine Learning using Python and R program and learn to script winning Machine Learning algorithms in Python and R. Use Python and R to enable regression analysis and to build predictive models. https://360digitmg.com/machine-learning-course-training-in-hyderabad machine learning institute in Hyderabad

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Top 10 Applications Of Artificial Intelligence | Artificial Intelligence Applications | Simplilearn

Artificial intelligence (AI) is currently one of the hottest buzzwords in tech and with good reason. The last few years have seen several techniques that have previously been in the realm of science fiction slowly transform into reality. In this YouTube live session, let us look at some of the popular applications of Artificial Intelligence. Simplilearnu2019s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming. Why learn Artificial Intelligence? The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills. You can gain in-depth knowledge of Artificial Intelligence by taking our Artificial Intelligence certification training course. Those who complete the course will be able to: 1. Master the concepts of supervised and unsupervised learning 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. Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning. 6. Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems ud83dudc49Learn more at: https://bit.ly/2AlrLiB

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4 Inquiries To Ask Before Buying A Can Manufacturing Machine

A can manufacturing machine is equipment as far as packaging is concerned. The machine has profit popularity recently due to enlarged industry of tin cans. Tin cans play an important function in food industries together with storing items for distribution international. These machines arrive in various sizes and create different cans of shapes. In that instance, there are distinct questions that you need to take into consideration before selecting the right can production machine that satisfies you personally. Here are some of them: What exactly will you pack? The can generating machine you're selecting will be able to make cans that are ideally suited for your products. This really is actually in regard to contour content, and size. Products such as beverages require tin cans with a great style and layout. The contour should be mobile and lightweight. Some services and products require care and are corrosive. Knowing everything you may end up packing will probably likely be worth addressing when picking your tin can making machine. The length of time will you utilize this machine? Do you necessitate a machine for a solution or you also need touse for a small span? Being aware of this can permit one to find out the can creating machine together with attributes and the longevity that come in keeping with your expectations. You will find machines that just do the job with some small period among the others require care. Simply take under consideration that factor to steer clear of unnecessary waste of the your money. Exactly what distance is available? Space is also an significant factor in every production. Some machines need a space while some are tiny enough to become used within our homesteads. If you need a can machine that can produce cans in every single circle, you then should have enough distance to the same. In addition, you need to take in to consideration the number of employees if moving around along with the distance they will use. Take under consideration the servicing and the way it's going to be done. How rapidly would be your can manufacturing machine? Many machines just perfect for home usage and really are still slow. In the event you prefer to buy one, then take in to account your generation lineup, go you would like cans? If so then choose a machine which will produce cans in most group. Though machines have the additional spending budget, it provides added benefits. A few requires labor compared to others and are automated. We can help you construct your own personal can generating machine manufacturing line. We fabricate and offer good quality and user-friendly turn key can making traces. Our tin can making machines really are cheap but of good quality. They are created at a minor labour cost for mass creation. We're additionally very adaptable in can producing machine generation to ensure our needs are met. Whether you're on the lookout for rectangular can line, around can lineup , or irregular can lines, we have all your requirements. Jiujiang Yixin Machinery Co., Ltd fabriquants des magnu00e9tic balls, Yuzhong Xian, Lanzhou Shi, China Telephone: 0086-792-8352066

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Demand for Compost Windrow Turning Machine Set for Stupendous Growth in and Post 2020, Buoyed by the Global COVID-19 Pan

Future Market Insightsu2019 recently published research study on the Compost Windrow Turning Machine market, titled u201cCompost Windrow Turning Machine: Global Industry Analysis, 2013-2017, and Forecast, 2018-2028u201d, offers a detailed value-volume analysis of the Compost Windrow Turning Machine market along with insights via. BPS analysis in (US$ Mn), on the basis of product type, output capacity and end-use industry. The volume analysis, for the Compost Windrow Turning Machine market, has been done in units (i.e. the number of Compost Windrow Turning Machines), covering data for the historical period ranging from 2013-2017 and forecasted up to 2028 with 2018 as the base year. In addition to the quantitative analyses provided by market size estimates and growth trends, the report on the global Compost Windrow Turning Machine market also provides qualitative analysis in the form of market dynamics, such as drivers, restraints, trends and opportunities, forecast factors, macroeconomic factors, porter's analysis, market definition and other factors, that will influence and impact the global Compost Windrow Turning Machine market. Sample of Research Report @ https://www.futuremarketinsights.com/reports/sample/rep-gb-4061 The report on the global Compost Windrow Turning Machine aims to provide insights with regards to market size and associated developments for the interested stakeholders. Marketing and business intelligence has been facilitated through information based on competition landscape, business strategies, technology roadmap and list of market participants, with relevant information on Compost Windrow Turning Machines. FMI has not only presented the research findings but also has added suitable recommendations and understanding of market dynamics which can be helpful for market entry, business development and sustenance in the market space. Compost turner Machine Market: Report Description For a complete understanding of the readers of the Compost Windrow Turning Machines in the best way possible, the report has been categorically divided into a multitude of categories on the basis of product type, output capacity and end use segment, on consolidated and by individual segments. The market estimates have been provided on global as well as regional levels. The first section of the Compost Windrow Turning Machine report includes market introduction/definition wherein product definition, taxonomy and market definition by segments have been analyzed and discussed. The market description also provides an outline of research limitations/ assumptions binding on the research study on the basis of which the entire framework has been devised. The next section of the global Compost Windrow Turning Machine market report discusses macroeconomic factors along with drivers, restraints, trends, value chain analysis and opportunities. This information has been backed up with suitable examples and facts as obtained from official and authentic data. In the subsequent section, the Compost Windrow Turning Machine market viewpoint has been covered, which includes the global value and volume analysis. Compost Windrow Turning Machine Market Segmentation: By Product Type Drum (Compost Windrow Turning Machine) Elevating Face (Compost Windrow Turning Machine) By Output Capacity < 500 cubic meters per hour 500 u2013 1000 cubic meter per hour >1000 cubic meter per hour By End Use Construction Industry (Compost Windrow Turning Machine) Agriculture Industry (Compost Windrow Turning Machine) Manufacturing Industry (Compost Windrow Turning Machine) By Region North America Latin America Western Europe Eastern Europe South East Asia & Pacific China India Japan Middle East and Africa The report provides significant information pertaining to the above-mentioned segments in the global Compost Windrow Turning Machine market. The last part in the market background includes a study of those factors that are expected to have an impact on the Compost Windrow Turning Machine market, such as technological growth, investments and other key insights pertaining to the market. A section dedicated to pricing analysis of Compost Windrow Turning Machines has been provided on the basis of product type on regional fronts. Weighted average selling price has been computed to arrive at the global average prices. The pricing analysis provides pricing by each configuration for the present scenario. Assumption and key considerations for the same have also been elucidated. The sections that follow consist of the global Compost Windrow Turning Machine market analysis by product type, output capacity, end use segment and region/country. The overall analysis of the Compost Windrow Turning Machine market begins with overall global market assessment, followed by analysis for numerous regions and a discussion on the macroeconomic environment in each particular region. Each regional section of the report contains qualitative data and quantitative aspects of the global Compost Windrow Turning Machine market. In the final section of the report, we have provided a competition analysis with company market share analysis pertaining to the Compost Windrow Turning Machines and performance of manufactures by tier down structure in the global Compost Windrow Turning Machine market. In the competition dashboard section of the global Compost Windrow Turning Machine market, we have provided a dashboard view of major players along with their market share and key business strategies. This would enable readers to evaluate strategies deployed by market leaders and help them develop effective strategies consequently. Sample of Research Methodology Report @ https://www.futuremarketinsights.com/askus/rep-gb-4061 Research Methodology The first stage of the research includes the formulation of a preliminary theory, which was considered from secondary as well as primary approaches. The subsequent steps involved triangulation of data collected using two different approaches. To determine market trends and opportunities, the global Compost Windrow Turning Machine market report has been segmented on the basis of four criteria: product type, output capacity, end use segment and region/country. For the final data analysis of the Compost Windrow Turning Machine market, we have considered 2018 as the base year and basic data was collected from public sources as well as other sources, such as the company annual reports, investor presentations and press releases, World Bank Statistics on revenue sales, newsletters, published reports on public domain, industry associationu2019s reports and paid databases on finance and corporate information. The collated data from the aforementioned sources was further validated from Compost Windrow Turning Machine manufacturers, end-user procurement agencies, distributors and regional representatives. ABOUT US: Future Market Insights is the premier provider of market intelligence and consulting services, serving clients in over 150 countries. FMI is headquartered in London, the global financial capital, and has delivery centers in the U.S. and India. FMIu2019s research and consulting services help businesses around the globe navigate the challenges in a rapidly evolving marketplace with confidence and clarity. Our customized and syndicated market research reports deliver actionable insights that drive sustainable growth. We continuously track emerging trends and events in a broad range of end industries to ensure our clients prepare for the evolving needs of their consumers. CONTACT US: Future Market Insights U.S. Office 616 Corporate Way, Suite 2-9018, Valley Cottage, NY 10989, United States T: 1-347-918-3531 F: 1-845-579-5705 Web: https://www.futuremarketinsights.com

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Mathematics For Machine Learning | Essential Mathematics - Machine Learning Tutorial | Simplilearn

Mathematics forms the core of Machine Learning and is one of the prerequisites. This presentation on Mathematics for Machine Learning will help you understand linear algebra, vectors, and matrices. Then, you will learn about integral calculus, followed by different topics in Statistics for machine learning. Finally, we'll look at Probability for Machine Learning. All these topics contain hands-on demo in Python. So, let's get started with Mathematics for Machine Learning. Data and its types Linear algebra and its concepts Calculus Statistics for machine learning Probability for machine learning About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all peopleu2019s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. 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 ud83dudc49Learn more at: https://bit.ly/3fouyY0

2.1k views • 83 slides

Confusion Matrix In Machine Learning | Confusion Matrix Explained With Example | Simplilearn

A Confusion Matrix is an excellent metric to describe the performance of a classification model. This video on Confusion Matrix with Machine Learning will help you learn the basics of machine learning and the need for a confusion matrix. You will understand how to create a confusion matrix and how to create a confusion matrix. You will get an idea about the different metrics and perform a demo using logistic regression to find the model's accuracy. 1. Intro to Machine Learning 2. Type of Machine learning 3. Need for Confusion Matrix 4. What is Confusion Matrix 5. Creating a Confusion Matrix 6. Comparing Confusion matrix 7. 3x3 Confusion Matrix About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all peopleu2019s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. 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 ud83dudc49Learn more at: https://bit.ly/3fouyY0

4.96k views • 27 slides

Machine Glazed Paper Market to Witness Machine Glazed Paper, as Uncertainty Looms Following Global Coronavirus Outbreak

A recent market report published by Future Market Insights on the Machine Glazed Paper market provides global industry analysis for 2015-2019 & forecast for 2020-2030. The report offers a comprehensive assessment of the most important market dynamics. After conducting thorough research on the historical, as well as current growth parameters of the machine glazed paper market, the growth prospects of the market are obtained with maximum precision. Market Taxonomy The global machine glazed paper market is segmented in detail to cover every aspect of the market and present complete market intelligence to readers. Basis Weight u2022tUp to 40 GSM u2022t41 to 70 GSM u2022t71 to 100 GSM u2022tAbove 100 GSM Paper Type u2022tTissue Paper u2022tKraft Paper Grade u2022tUnbleached u2022tBleached Application u2022tBags & Pouches u2022tSacks u2022tWraps u2022tLabels & Release liners u2022tEnvelopes u2022tCoating & Lamination u2022tTrays, cups & bowls u2022tOthers (Gift Wrap) End Use u2022tFood & Beverages u2022tHealthcare u2022tElectrical & Electronics u2022tAutomotive u2022tPersonal care & Cosmetics u2022tTextile u2022tBuilding & Construction u2022tHousehold Regional Outlook u2022tNorth America (US, and Canada) u2022tLatin America (Brazil, and Mexico) u2022tEurope (Germany, UK, Russia, France, and Italy) u2022tSouth Asia (India, Indonesia, Thailand, Australia and New Zealand) u2022tEast Asia (China, Japan, and South Korea) u2022tOceania (Australia and New Zealand) u2022tMiddle East & Africa (MEA) (Turkey, GCC Countries, and South Africa) Report Chapters Chapter 01 u2013 Executive Summary The report begins with the executive summary of the machine glazed paper market, which includes a summary of the key findings and statistics of the market. It also includes dominant segments in the global machine glazed paper market, along with key facts about machine glazed paper market. It also includes key drivers and trends of the global machine glazed paper market. To Get the Sample Copy of Report visit @https://www.futuremarketinsights.com/reports/sample/rep-gb-7900 Chapter 02 u2013 Market Overview Readers can find detailed taxonomy and definition of the machine glazed paper market in this chapter, which will help them understand the basic information about machine glazed paper market present in the market. This section also highlights the inclusions and exclusions, which help readers to understand the scope of the machine glazed paper market report. Chapter 03 u2013 Key Market Trends It includes a comprehensive analysis of key trends such as product innovation and technological advancements that are impacting the global machine glazed paper market. Chapter 04 u2013 Global Machine Glazed Paper Market Demand Analysis (Volume u2019000 Tonnes) 2015-2019 and Forecast, 2020-2030 This section explains the global market volume analysis and forecast for the machine glazed paper market between the forecast period of 2020-2030. It includes the detailed analysis of the historical machine glazed paper market, along with an opportunity analysis for the future. Readers can also find the Y-o-Y growth opportunity for the forecast period (2020-2030). Chapter 05 u2013 Global Machine Glazed Paper Market u2013 Pricing Analysis This section highlights the average pricing analysis of basis weight (Up to 40 GSM, 41 to 70 GSM, 71 to 100 GSM and above 100 GSM) of machine glazed paper market in different regions across the globe. The weighted average pricing at the manufacturer-level is analyzed in this section. Chapter 06 u2013 Global Machine Glazed Paper Market Demand (Value in US$ Mn) Analysis 2015-2019 and Forecast, 2020-2030 This section explains the global market value analysis and forecast for the machine glazed paper market between the forecast period of 2020-2030. It includes the detailed analysis of the historical machine glazed paper market, along with an opportunity analysis for the future. Readers can also find the Y-o-Y growth opportunity for the forecast period (2020-2030). Chapter 07 u2013 Market Background This chapter explains the key forecast factors that are expected to influence the growth of machine glazed paper market over the forecast period. This chapter also highlights the key market dynamics of the machine glazed paper market, which include the drivers, restraints and opportunities. This section also includes value chain analysis. Chapter 08 u2013 Impact of COVID-19 This chapter explains how the COVID-19 has impacted the overall packaging industry. It also explains the impacts of COVID-19 on the key regions, current statistics and probable future impacts and current economic projections. Chapter 09 u2013 Global Machine Glazed Paper Market Analysis 2015-2019 and Forecast 2020-2030, by Basis weight Based on basis weight, the machine glazed paper market is segmented as Up to 40 GSM, 41 to 70 GSM, 71 to 100 GSM, and Above 100 GSM. In this chapter, readers can find information about key segments during the forecast period. Ask an Analyst @https://www.futuremarketinsights.com/ask-question/rep-gb-7900 Chapter 10 u2013 Global Machine Glazed Paper Market Analysis 2015-2019 and Forecast 2020-2030, By Paper type Based on paper type, the machine glazed paper market is segmented on the basis of tissue paper and kraft paper. In this chapter, readers can find information about key segments during the forecast period. so on.. About Us Future Market Insights (FMI) is a leading provider of market intelligence and consulting services, serving clients in over 150 countries. FMI is headquarters in London, the global financial capital, and has delivery centers in the US and India.FMIu2019s latest market research reports and industry analysis help businesses navigate challenges and make critical decision with confidence and clarity amidst breakneck competition. Our customize and syndicate market research reports deliver actionable insights that drive sustainable growth. A team of expert-led analysts at FMI continuously tracks emerging trends and events in a broad range of industries to ensure that our client prepare for the evolving needs of their customer Contact Us Mr. Abhishek Budholiya Unit No: AU-01-H Gold Tower (AU), Plot No: JLT-PH1-I3A, Jumeirah Lakes Towers, Dubai, United Arab Emirates MARKET ACCESS DMCC Initiative For Sales Enquiries: sales@futuremarketinsights.com For Media Enquiries: press@futuremarketinsights.com

133 views • 11 slides

Download Skim, Dive, Surface: Teaching Digital Reading (Teaching and Learning in Higher Education) | Ebook

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[EPUB] Deep Learning Full

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Bottle Labeling Machine

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Valid Google Professional-Machine-Learning-Engineer Study Plan, Professional-Machine-Learning-Engineer Mock Exams

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DP-100模擬練習 & DP-100赤本合格率

r u7c21u5358u306bMicrosoftu306eDP-100u8a8du5b9au8a66u9a13u306bu5408u683cu3057u305fu3044u304bu3002ShikenPASSu306eMicrosoftu306eDP-100u8a66u9a13u30c8u30ecu30fcu30cbu30f3u30b0u8cc7u6599u306fu6b20u304fu3053u3068u304cu3067u304du306au3044u5b66u7fd2u6559u6750u3067u3059u3002ShikenPASSu306eMicrosoftu306eDP-100u8a66u9a13u30c8u30ecu30fcu30cbu30f3u30b0u8cc7u6599u306fu8c4au5bccu306au7d4cu9a13u3092u6301u3063u3066u3044u308bITu5c02u9580u5bb6u304cu7814u7a76u3057u305fu3082u306eu3067u3001u554fu984cu3068u89e3u7b54u304cu7dcau5bc6u306bu7d50u3093u3067u3044u308bu3082u306eu3067u3059u3002u4ed6u306eu30cdu30c3u30c8u3067u306eu8cc7u6599u306fu305du308cu3068u6bd4u3079u308bu3059u3089u3082u3067u304du307eu305bu3093u3002ShikenPASSu306fu541bu306eu3082u3063u3068u8f1du3044u5c06u6765u306bu52a9u3051u3089u308cu307eu3059u3002r Microsoft DP-100 u8a8du5b9au8a66u9a13u306eu51fau984cu7bc4u56f2uff1ar u30c8u30d4u30c3u30afu51fau984cu7bc4u56f2u30c8u30d4u30c3u30af 1Create An Azure Data Science Environmentr Configure Data Science Work Environmentsr u30c8u30d4u30c3u30af 2Determine Ideal Split Based On The Nature Of The Datar Determine Number Of Splitsr Identify Data Imbalancesr u30c8u30d4u30c3u30af 3Design The Data Preparation Flowr Identify Anomalies, Outliers, And Other Data Inconsistenciesr u30c8u30d4u30c3u30af 4Resolve Anomalies, Outliers, And Other Data Inconsistenciesr Standardize Data Formatsr r u30c8u30d4u30c3u30af 5Select An Algorithmic Approachr Consider Data Preparation Steps That Are Specific To The Selected Algorithmsr u30c8u30d4u30c3u30af 6Assess The Deployment Environment Constraintsr Select The Development Environmentr u30c8u30d4u30c3u30af 7Analyze And Recommend Tools That Meet System Requirementsr Set Up Development Environmentr u30c8u30d4u30c3u30af 8Transform Data Into Usable Datasetsr Develop Data Structuresr Perform Exploratory Data Analysis (Eda)r u30c8u30d4u30c3u30af 9Review Visual Analytics Data To Discover Patterns And Determine Next Stepsr Design A Data Sampling Strategyr u30c8u30d4u30c3u30af 10Determine Appropriate Performance Metricsr Implement Appropriate Algorithmsr >> DP-100u6a21u64ecu7df4u7fd2 <<r DP-100u8d64u672cu5408u683cu7387 & DP-100u65e5u672cu8a9er DP-100u8a66u9a13u8cc7u683cu8a3cu660eu66f8u3092u53d6u5f97u3059u308bu3053u3068u306fu96e3u3057u3044u3067u3059u3002u3067u3082u3001Microsoftu3000DP-100u5fa9u7fd2u6559u6750u3092u9078u3070u308cu3070u3001u8a66u9a13u306bu5408u683cu3059u308bu3053u3068u306fu7c21u5358u3067u3059u3002DP-100u5fa9u7fd2u6559u6750u306eu5185u5bb9u306fu5168u9762u7684u3067u3001u4fa1u683cu306fu5408u7406u7684u3067u3059u3002u305du3057u3066u3001Microsoftu306fu304au5ba2u69d8u306bu30c7u30a3u30b9u30abu30a6u30f3u30c8u30b3u30fcu30c9u3092u63d0u4f9bu3067u304du3001DP-100u5fa9u7fd2u6559u6750u3092u3088u308au5b89u304fu8cfcu5165u3067u304du307eu3059u3002r Microsoft Designing and Implementing a Data Science Solution on Azure u8a8du5b9a DP-100 u8a66u9a13u554fu984c (Q272-Q277):r u8ceau554f # 272 You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.r u6b63u89e3uff1ar u89e3u8aacuff1aExplanation:Step 1: Sweep ClusteringStart by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering.One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.Step 2: Train ModelStep 3: Evaluate ModelScenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.References:http://breaking-bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.htmlr u8ceau554f # 273 You use Azure Machine Learning Studio to build a machine learning experiment.You need to divide data into two distinct datasets.Which module should you use?r A. Assign Data to Clustersr B. Partition and Sampler C. Load Trained Modelr D. Tune Model-Hyperparametersr u6b63u89e3uff1aBr u89e3u8aacuff1aPartition and Sample with the Stratified split option outputs multiple datasets, partitioned using the rules you specified.References:https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sampler u8ceau554f # 274 You need to implement a new cost factor scenario for the ad response models as illustrated in the performance curve exhibit.Which technique should you use?r A. Set the threshold to 0.5 and retrain if weighted Kappa deviates /- 5% from 0.45.r B. Set the threshold to 0.75 and retrain if weighted Kappa deviates /- 5% from 0.15.r C. Set the threshold to 0.2 and retrain if weighted Kappa deviates /- 5% from 0.6.r D. Set the threshold to 0.05 and retrain if weighted Kappa deviates /- 5% from 0.5.r u6b63u89e3uff1aAr u89e3u8aacuff1aScenario:Performance curves of current and proposed cost factor scenarios are shown in the following diagram:The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated from 0.1 /- 5%.r u8ceau554f # 275 For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.r u6b63u89e3uff1ar u89e3u8aacuff1ar u8ceau554f # 276 You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.You need to configure store1.What should you do? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.r u6b63u89e3uff1ar u89e3u8aacuff1aReference:https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-datar u8ceau554f # 277......r Microsoftu306eDP-100u30afu30a4u30bau30c8u30ecu30f3u30c8u306fu7121u6599u306eu8a66u7528u7248u3092u63d0u4f9bu3057u307eu3059u3002u3057u305fu304cu3063u3066u3001DP-100u30c6u30b9u30c8u6e96u5099u306bu3064u3044u3066u3088u308au6df1u304fu7406u89e3u3057u3001u3053u306eu7a2eu306eu5b66u7fd2u6559u6750u304cu8cfcu5165u306bu9069u3057u3066u3044u308bu304bu3069u3046u304bu3092u63a8u5b9au3059u308bu306eu306bu5f79u7acbu3061u307eu3059u3002 ShikenPASSu8a66u7528u7248u3092u4f7fu7528u3059u308bu3068u3001u30c6u30b9u30c8u30d7u30e9u30c3u30c8u30d5u30a9u30fcu30e0u3067u5229u7528u53efu80fdu306a3u3064u306eu7570u306au308bu30d0u30fcu30b8u30e7u30f3u306eu9078u629eu304bu3089u30a2u30d5u30bfu30fcu30b5u30fcu30d3u30b9u307eu3067u3001u3055u307eu3056u307eu306au5074u9762u304bu3089u306eDP-100u8a66u9a13u30c8u30ecu30f3u30c8u306bu3064u3044u3066u3088u308au6df1u304fu7406u89e3u3067u304du307eu3059u3002 DP-100u8a66u9a13u554fu984cu3092u8a66u3057u3066u307fu305fu3089u3001Designing and Implementing a Data Science Solution on Azureu8cfcu5165u3059u308bu306eu304cu5927u597du304du3067u3059u3002r DP-100u8d64u672cu5408u683cu7387: https://www.shikenpass.com/DP-100-shiken.htmlr Tags: DP-100u6a21u64ecu7df4u7fd2,DP-100u8d64u672cu5408u683cu7387,DP-100u65e5u672cu8a9e,DP-100u8a66u9a13u95a2u9023u60c5u5831,DP-100u53d7u9a13u8cc7u6599u66f4u65b0u7248r

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Artificial-Intelligence-Foundationブロンズ教材、Artificial-Intelligence-Foundation問題例

r u5f53u793eTech4Examu306eu8a2du7acbu4ee5u6765u3001u79c1u305fu3061u306fArtificial-Intelligence-Foundationu8a66u9a13u8cc7u6599u306bu5927u898fu6a21u306au4ebau6750u3001u8cc7u6599u3001u304au3088u3073u8ca1u6e90u3092u6295u5165u3057u3066u304du307eu3057u305fu3002u305du3057u3066u4ecau307eu3067u3001u79c1u305fu3061u306fu9593u9055u3044u306au304fu5168u4e16u754cu306bu7814u7a76u8cc7u6599u3092u7d39u4ecbu3057u3001u5e78u904bu3092u6c42u3081u308bu3059u3079u3066u306eu4ebau3005u3092u4f5cu308bu3068u3044u3046u5927u80c6u306au8003u3048u3092u6301u3063u3066u3044u307eu3059u3088u308au826fu3044u6a5fu4f1au306fu3001u5f7cu3089u306eu4ebau751fu306eu4fa1u5024u3092u5b9fu73feu3059u308bu305fu3081u306eu30a2u30afu30bbu30b9u6a29u3092u6301u3063u3066u3044u307eu3059u3002u3057u305fu304cu3063u3066u3001u5f53u793eu306eArtificial-Intelligence-Foundationu7df4u7fd2u554fu984cu306fu3001u8a66u9a13u306bu5408u683cu3057u3001u3088u308au826fu3044u672au6765u3092u52ddu3061u53d6u308bu306eu306bu5f79u7acbu3061u307eu3059u3002u307eu305fu3001u5e38u306bu5148u99c6u7684u306au7cbeu795eu3092u6301u3061u7d9au3051u3001u3042u306au305fu306eu9053u3092u6b69u3080u30d7u30edu30b8u30a7u30afu30c8u306bu7a4du6975u7684u306bu53d6u308au7d44u307fu307eu3059u3002r Tech4Examu306fu3001Artificial-Intelligence-Foundationu8a66u9a13u306bu30b9u30e0u30fcu30bau306bu5408u683cu3059u308bu306eu306bu5f79u7acbu3061u307eu3059u3002u6700u9ad8u306eArtificial-Intelligence-Foundationu5b66u7fd2u6559u6750u3078u306eu30c1u30e3u30f3u30cdu30ebu306bu3064u3044u3066u5fc3u914du3059u308bu5fc5u8981u306fu3042u308au307eu305bu3093u3002u591au304fu306eu53d7u9a13u8005u306fu3001u5f7cu3089u306eu52a9u3051u3092u63d0u4f9bu3059u308bu5bdbu5927u3055u3092u8cdeu8cdbu3057u307eu3059u3002u3053u308cu307eu3067u3001u3053u306eu5206u91ceu306eu4e3bu5c0eu7684u5730u4f4du306bu6311u6226u3057u305fu4ebau306fu3044u307eu305bu3093u3067u3057u305fu3002 APMG-Internationalu306eArtificial-Intelligence-Foundationu5b66u7fd2u30acu30a4u30c9u306eu5b58u5728u306fu3001u8a66u9a13u306bu5408u683cu3059u308bu52b9u7387u3092u9ad8u3081u308bu3082u306eu3068u898bu306au3055u308cu307eu3059u3002u5f53u793eu306fu3001u6642u9593u306eu7d4cu904eu3068u3068u3082u306bu3001u53d7u9a13u8005u304cu6700u592798u301c100uff05u306eu5408u683cu7387u3067u53d7u9a13u3067u304du308bu3088u3046u306bu306au308au307eu3059u3002u3059u3079u3066u306eu884cu52d5u306fu3001Artificial-Intelligence-Foundationu8a66u9a13u3067Foundation Certification Artificial Intelligenceu6210u529fu3059u308bu53efu80fdu6027u3092u5411u4e0au3055u305bu308bu3053u3068u3092u76eeu6307u3057u3066u3044u307eu3059u3002r >> Artificial-Intelligence-Foundationu30d6u30edu30f3u30bau6559u6750 <<r u8a8du5b9au3059u308bArtificial-Intelligence-Foundationu30d6u30edu30f3u30bau6559u6750u8a66u9a13-u8a66u9a13u306eu6e96u5099u65b9u6cd5-u30e6u30cbu30fcu30afu306aArtificial-Intelligence-Foundationu554fu984cu4f8br u3069u3093u306au696du754cu3067u81eau5206u306bu826fu3044u6607u9032u6a5fu4f1au304cu3042u308bu3068u5e0cu671bu3059u308bu8077u4ebau304cu3068u3066u3082u591au3044u3068u601du3063u3066u3001ITu696du754cu306bu3082u4f8bu5916u3067u306fu3042u308au307eu305bu3093u3002ITu306eu5c02u9580u8005u306fAPMG-Internationalu306eArtificial-Intelligence-Foundationu8a8du5b9au8a66u9a13u304cu3042u306au305fu306eu9858u671bu3092u52a9u3051u3063u3066u5b9fu73feu3067u304du308bu306eu304cu3088u304fu5206u304bu308au307eu3059u3002Tech4Examu306fu3042u306au305fu306eu5922u306bu5b9fu73feu3055u305bu308bu30b5u30a4u30c8u3067u3054u3056u3044u307eu3059u3002r APMG-International Foundation Certification Artificial Intelligence u8a8du5b9a Artificial-Intelligence-Foundation u8a66u9a13u554fu984c (Q32-Q37):r u8ceau554f # 32 In the 1800's the development of statistics led to___________theorem and is used in probabilistic inference.(Select the missing word.)r A. Boltzmann'sr B. Kolmogorov'sr C. The central limitr D. Bayes'r u6b63u89e3uff1aDr u89e3u8aacuff1aExplanationThe development of statistics in the 1800s led to the development of the Bayes' theorem, named after Reverend Thomas Bayes. This theorem is used in probabilistic inference, which is the process of using data to calculate the likelihood of a hypothesis or outcome. The theorem is used for determining the probability of an event occurring given its prior probability, as well as its associated conditions. The Bayes' theorem is also used in a variety of fields, such as machine learning, artificial intelligence, economics, and medical research.Sources:* BCS Foundation Certificate In Artificial Intelligence Study Guide: https://www.bcs.org/category/18071* APMGInternational: https://www.apmg-international.com/en/qualifications/qualification-resources/bcs-foundatio* EXIN: https://www.exin.com/en/certification/bcs-foundation-certificate-in-artificial-intelligencer u8ceau554f # 33 Collaboration, learning and iterative are terms used to describe what?r A. Waterfall projects.r B. Rapid software development.r C. Agile projectsr D. Trustworthy Al.r u6b63u89e3uff1aCr u89e3u8aacuff1aExplanationCollaboration, learning, and iterative are terms used to describe agile projects. Agile projects are designed to be adaptive and flexible, allowing teams to incorporate feedback and learn from their mistakes. This process encourages collaboration between team members, and emphasizes the importance of iterative development and continual improvement. Agile projects focus on delivering value quickly and efficiently, allowing teams to make changes and adapt to changing customer needs.References:[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-internationalr u8ceau554f # 34 What is one of the MAIN contributions of Al to the rapid development of The Fourth Industrial Revolution?r A. Enhanced design.r B. Big Datar C. Automationr D. Al personal assistants.r u6b63u89e3uff1aCr u89e3u8aacuff1aExplanationhttps://research.com/careers/what-is-the-fourth-industrial-revolutionArtificial Intelligence (AI) is playing a major role in the rapid development of the Fourth Industrial Revolution. AI technologies are enabling the automation of many processes that were previously carried out by humans or machines, which has greatly increased the speed, efficiency, and accuracy of these processes.Automation is one of the main contributions of AI to the Fourth Industrial Revolution, as it has greatly increased the productivity of businesses and industries, while reducing the cost of production and improving the quality of products.References: https://www.bcs.org/more/certifications/foundation-certificate-in-artificial-intelligence/ https://wwwr u8ceau554f # 35 If Al undertakes routine and monotonous tasks and takes these away from humans, what will humans do?r A. Leisure activitiesr B. Higher value work.r C. Sabotage the Al.r D. Change jobs.r u6b63u89e3uff1aBr u89e3u8aacuff1aExplanationAl is designed to take on routine and monotonous tasks, freeing up humans to take on more complex, higher value work. This can include tasks such as research, problem-solving, and decision-making. This shift in work roles is expected to increase productivity and efficiency, allowing humans to focus on more creative and innovative tasks. For example, robots can be used to automate mundane manufacturing processes, freeing up human workers to take on jobs that require more creative thinking and problem-solving.References:[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-internationalr u8ceau554f # 36 In Machine learning what are a brain's axons called?r A. Dendritesr B. Tetrahedra.r C. Edgesr D. Nodesr u6b63u89e3uff1aDr u89e3u8aacuff1aExplanationIn Machine Learning, the brain's axons are referred to as nodes. Nodes are the components of a neural network that are responsible for processing the input data and generating the output. A node is a mathematical function that takes input data, performs a computation on it, and produces an output. Each node is connected to other nodes in the network via edges, which represent the strength of the connection between the respective nodes. The strength of the connection between two nodes is determined by the weights assigned to each edge.The weights are adjusted during the training process to generate the desired results.For more information, please refer to the BCS Foundation Certificate In Artificial Intelligence Study Guide (https://www.bcs.org/upload/pdf/bcs-foundation-certificate-in-artificial-intelligence-study-guide.pdf) or the EXIN Artificial Intelligence Foundation Certification (https://www.exin.com/en/exams/artificial-intelligence-foundation).r u8ceau554f # 37......r Artificial-Intelligence-Foundationu30c8u30ecu30fcu30cbu30f3u30b0u30afu30a4u30bau304cu5f79u7acbu3064u3068u81eau4fe1u3092u6301u3063u3066u8a00u3048u307eu3059u3002u307eu305au7b2cu4e00u306bu3001u5f53u793eu306fu30e6u30fcu30b6u30fcu306eu30cbu30fcu30bau306bu5fdcu3058u3066u5e38u306bu88fdu54c1u3092u6539u5584u3057u3066u3044u307eu3059u3002u5b66u7fd2u88fdu54c1u304cu672cu5f53u306bu5f79u7acbu3064u3053u3068u3092u672cu5f53u306bu671bu3093u3067u3044u308bu306au3089u3001u79c1u305fu3061u306eArtificial-Intelligence-Foundationu5b66u7fd2u6559u6750u306fu9593u9055u3044u306au304fu3042u306au305fu306eu6700u826fu306eu9078u629eu3067u3059u3002u3042u306au305fu306fu305du308cu3088u308au5b8cu74a7u306au88fdu54c1u3092u898bu3064u3051u308bu3053u3068u306fu3067u304du307eu305bu3093u3002u7b2cu4e8cu306bu3001Artificial-Intelligence-Foundationu306eu5b66u7fd2u306bu95a2u3059u308bu8ceau554fu306fu591au304fu306eu4ebau3005u3092u672cu5f53u306bu52a9u3051u3066u304fu308cu307eu3057u305fu3002u3053u308cu3089u306eu9ad8u9f62u8005u306eu7d4cu9a13u3092u898bu308bu3068u3001Artificial-Intelligence-Foundationu8a66u9a13u306bu5408u683cu3059u308bu3053u3068u3092u5f37u304fu6c7au610fu3057u3066u3044u308bu3068u601du3044u307eu3059u3002r Artificial-Intelligence-Foundationu554fu984cu4f8b: https://www.tech4exam.com/Artificial-Intelligence-Foundation-pass-shiken.htmlr 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