Category Archives: Machine Learning
AI and Machine Learning are Expected to Improve Fintech Apps in India, with Support from Regulators – Crowdfund Insider
The steady growth and adoption of Fintech services has helped in promoting greater financial inclusion in India. Emerging technologies like AI and machine learning (ML) are now expected to further promote the usage of Fintech apps in the $2.5 trillion economy, which should benefit consumers and the nations businesses.
As reported by local sources, India could potentially become a $1 trillion-digital payments market. While the nations government is focused on supporting the Digital India initiative, the Reserve Bank (RBI) is enabling the Fintech industrys growth by establishing a separate or independent unit for financial technology firms (supported by the central bank).
Mandar Agashe, Founder and MD at Sarvatra Technologies, a payments and banking solutions provider, has noted that most tech firms have been offering various products and services to local banks via back-end support. During the past few years, India has also seen a rise in the adoption of Fintech services that can help businesses and financial service providers with scaling their operations, Agashe confirmed.
He added:
[The COVID-19] pandemic has been a push for digital payments where every person from any walk of life is learning how to do digital commerce and transacting online. It has created an immediate need for safer, more efficient experiences, both online and offline.
According to the RBI, Fintech might fundamentally transform the financial sector, by offering consumers more seamless products and cheap transaction costs. Fintech can also improve the efficiency of traditional financial institutions.
The RBI has also mentioned that Fintechs are now offering a wider range of services such as crypto-assets, payments, insurance, stocks, bonds, peer to peer lending, Robo-advisors, and Regtech solutions.
Seema Prem, Co-Founder and CEO at FIA Global, a digital solutions provider, has noted that Fintechs have helped SMEs with being able to use certain services that were not easy for them to access.
She remarked:
Fintech players very evidently have been prioritizing their strategies with many changes in rules and market conditions. However, there is an increased need and requirement for a multi-stakeholder framework that encompasses the regulators and governments, to guide them better. The governments support, in providing adequate liquidity through regulated banks and (non-bank financial companies) NBFCs, will also provide the much-needed support to the Fintech to work towards a positive approach.
The RBI has also clarified:
A central banks interest in Fintech is not confined to its impact on the financial sector per se, but rather its implications for financial stability and monetary policy. The regulatory environment, like the roots that provide life to a tree, provides a solid foundation for fintech activities.
As reported recently, Google India Country Manager and VP, Sajay Gupta, has predicted that emerging technologies like AI could add $500 billion to the nations economy. At present, Google Pay claims a 43% market share in the country.
Government policies are also evolving quickly in India, and provide a favorable backdrop for Fintech, according to an industry executive.
Fintech lending is on the rise in India (and globally) but policymakers must watch the space closely to protect consumers, according to a recent report. As reported, digital payments and Fintech adoption surged in India during COVID, while other sectors struggled to maintain operations.
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AI and Machine Learning are Expected to Improve Fintech Apps in India, with Support from Regulators - Crowdfund Insider
Four Benefits Of Artificial Intelligence And Machine Learning In Banking – CIO Applications
Artificial intelligence in banking helps clients evaluate the vast amount of information, from the users request in social networks to make informed and safe decisions.
Fremont, CA: Artificial intelligence and machine learning in banking offer many opportunities for personalization, data analysis, tasks solving abilities, and also reasonable costs for implementation.
The widespread rise in the importance of artificial intelligence and machine learning for banking has strong foundations as the technologies offer new and useful benefit.
Here are four benefits of artificial intelligence and machine learning in banking:
A Cutting Edge Advantage:
Machine learning in banks have the capability to make users more competitive according to the task they want to solve.
Advanced Data Analysis:
Banks used to evaluate data with less access to information such as when a client comes with a request to issue a loan, the decision was made only based on the statement of income, current assets and liabilities of the client, and the credit history. Today, artificial intelligence in banking helps clients evaluate the vast amount of information, from the users request in social networks to make informed and safe decisions.
Better Security:
Artificial intelligence in banking can be implemented in various ways to achieve higher security. Credit card fraud detection implementing machine learning has become a common application of the technology, and innovative cameras with face recognition can identify if a client has wrong intentions by judging the facial expressions.
Costs Cut:
Artificial intelligence and machine learning can help cut costs for banks and financial institutions based on how these technologies are used. Integrating robo-advisors in the support team can help reduce the cost of staff maintenance.
See Also:
TopBanking Technology Solution Companies
TopBanking Technology Consulting/Service Companies
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Four Benefits Of Artificial Intelligence And Machine Learning In Banking - CIO Applications
Machine Learning for Child and Adolescent Health: A Systematic Review – American Academy of Pediatrics
CONTEXT: In the last few decades, data acquisition and processing has seen tremendous amount of growth, thus sparking interest in machine learning (ML) within the health care system.
OBJECTIVE: Our aim for this review is to provide an evidence map of the current available evidence on ML in pediatrics and adolescent medicine and provide insight for future research.
DATA SOURCES: A literature search was conducted by using Medline, the Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature Plus, Web of Science Library, and EBSCO Dentistry & Oral Science Source.
STUDY SELECTION: Articles in which an ML model was assessed for the diagnosis, prediction, or management of any condition in children and adolescents (018 years) were included.
DATA EXTRACTION: Data were extracted for year of publication, geographical location, age range, number of participants, disease or condition under investigation, study methodology, reference standard, type, category, and performance of ML algorithms.
RESULTS: The review included 363 studies, with subspecialties such as psychiatry, neonatology, and neurology having the most literature. A majority of the studies were from high-income (82%; n = 296) and upper middle-income countries (15%; n = 56), whereas only 3% (n = 11) were from low middle-income countries. Neural networks and ensemble methods were most commonly tested in the 1990s, whereas deep learning and clustering emerged rapidly in the current decade.
LIMITATIONS: Only studies conducted in the English language could be used in this review.
CONCLUSIONS: The interest in ML has been growing across various subspecialties and countries, suggesting a potential role in health service delivery for children and adolescents in the years to come.
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Machine Learning for Child and Adolescent Health: A Systematic Review - American Academy of Pediatrics
What the hell is an AI factory? – The Next Web
If you follow the news on artificial intelligence, youll find two diverging threads. The media and cinema often portray AI withhuman-like capabilities, mass unemployment, and a possible robot apocalypse. Scientific conferences, on the other hand, discuss progress towardartificial general intelligencewhile acknowledging thatcurrent AI is weakand incapable of many of the basic functions of the human mind.
But regardless of where they stand in comparison to human intelligence, todays AI algorithms have already becomea defining component for many sectors, including health care, finance, manufacturing, transportation, and many more. And very soon no field of human endeavor will remain independent of artificial intelligence, as Harvard Business School professors Marco Iansiti and Karim Lakhani explain in their bookCompeting in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World.
In fact, weak AI has already led the growth and success of companies such as Google, Amazon, Microsoft, and Facebook, and is impacting the daily lives of billions of people. As Lakhani and Iansiti discuss in their book, We dont need a perfect human replica to prioritize content on a social network, make a perfect cappuccino, analyze customer behavior, set the optimal price, or even, apparently, paint in the style of Rembrandt. Imperfect, weak AI is already enough to transform the nature of firms and how they operate.
Startups that understand the rules of running AI-powered businesses have been able to create new markets and disrupt traditional industries. Established companies that have adapted themselves to the age of AI survived and thrived. Those that stuck to old methods have ceased to exist or become marginalized after losing ground to companies that have harnessed the power of AI.
Among the many topics Iansiti and Lakhani discuss is the concept AI factories, the key component that enables companies to compete and grow in the age of AI.
Competing in the Age of AI by Marco Iansiti and Karim Lakhani
The keyAI technologies used in todays businessare machine learning algorithms, statistical engines that can glean patterns from past observations and predict new outcomes. Along with other key components such as data sources, experiments, and software,machine learning algorithmscan create AI factories, a set of interconnected components and processes that nurture learning and growth.
Heres how the AI factory works. Quality data obtained from internal and external sources train machine learning algorithms to make predictions on specific tasks. In some cases, such as diagnosis and treatment of diseases, these predictions canhelp human experts in their decisions. In others, such as content recommendation, machine learning algorithms can automate tasks with little or no human intervention.
The algorithm and data-driven model of the AI factory allows organizations to test new hypotheses and make changes that improve their system. This could be new features added to an existing product or new products built on top of what the company already owns. These changes in turn allow the company to obtain new data, improve AI algorithms, and again find new ways to increase performance, create new services and product, grow, and move across markets.
In its essence, the AI factory creates a virtuous cycle between user engagement, data collection, algorithm design, prediction, and improvement, Iansiti and Lakhani write inCompeting in the Age of AI.
The idea of building, measuring, learning, and improving is not new. It has been discussed and practiced by entrepreneurs and startups for many years. But AI factories take this cycle to a new level by entering fields such asnatural language processingandcomputer vision, which had very limited software penetration until a few years ago.
One of the examplesCompeting in the Age of AIdiscusses is Ant Financial (now known as Ant Group), a company founded in 2014 that has 9,000 employees and provides a broad range of financial services to more than 700 million customers with the help of a very efficient AI factory (and genius leadership). To put that in perspective, Bank of America, founded in 1924, employs 209,000 people to serve 67 million customers with a more limited array of offerings.
Ant Financial is just a different breed, Iansiti and Lakhani write.
Image credit: Depositphotos
It is a known fact that machine learning algorithms rely heavily on mass amounts of data. The value of data has given rise to idioms such as data is the new oil, a clich that has been usedinmanyarticles.
But large volumes of data alone do not make for good AI algorithms. In fact, many companies sit on vast stores of data, but their data and software exist in separate silos, stored in an inconsistent fashion, and in incompatible models and frameworks.
Even though customers view the enterprise as a unified entity, internally the systems and data across units and functions are typically fragmented, thereby preventing the aggregation of data, delaying insight generation, and making it impossible to leverage the power of analytics and AI, Iansiti and Lakhani write.
Furthermore, before being fed to AI algorithms, data must be preprocessed. For instance, you might want to use the history of past correspondence with clients to develop an AI-powered chatbot that automates parts of your customer support. In this case, the text data must be consolidated, tokenized, stripped of excessive words and punctuations, and go through other transformations before it can be used to train the machine learning model.
Even when dealing with structured data such as sales records, there might be gaps, missing information, and other inaccuracies that need to be resolved. And if the data comes from various sources, it needs to be aggregated in a way that doesnt cause inaccuracies. Without preprocessing, youll be training your machine learning models on low-quality data, which will result in AI systems that perform poorly.
And finally, internal data sources might not be enough to develop the AI pipeline. Sometimes, youll need to complement your information with external sources such as data obtained from social media, stock market, news sources, and more. An example is BlueDot, a company that uses machine learningto predict the spread of infectious diseases. To train and run its AI system, BlueDot automatically gathers information from hundreds of sources, including statements from health organizations, commercial flights, livestock health reports, climate data from satellites, and news reports. Much of the companys efforts and software is designed for the gathering and unifying the data.
InCompeting in the Age of AI, the authors introduce the concept of the data pipeline, a set of components and processes that consolidate data from various internal and external sources, clean the data, integrate it, processes it, and store it for use in different AI systems. Whats important, however, is that the data pipeline works in a systematic, sustainable, and scalable way. This means that there should be the least amount of manual effort involved to avoid causing a bottleneck in the AI factory.
Iansiti and Lakhani also expand on the challenges involved in the other aspects of the AI factory, such as establishing the right metrics and features forsupervised machine learning algorithms, finding the right split between human expert insight and AI predictions, and tackling the challenges of running experiments and validating the results.
If the data is the fuel that powers the AI factory, then infrastructure makes up the pipes that deliver the fuel, and the algorithms are the machines that do the work. The experimentation platform, in turn, controls the valves that connect new fuel, pipes, and machines to existing operational systems, the authors write.
In many ways, building a successful AI company is as much a product management challenge as an engineering one. In fact, many successful companies have figured out how to build the right culture and processes on long-existing AI technology instead of trying to fit the latest developments indeep learninginto an infrastructure that doesnt work.
And this applies to both startups and long-standing firms. As Iansiti and Lakhani explain inCompeting in the Age of AI, technology companies that survive are those that continuously transform their operating and business models.
For traditional firms, becoming a software-based, AI-driven company is about becoming a different kind of organizationone accustomed to ongoing transformation, they write. This is not about spinning off a new organization, setting up the occasional skunkworks, or creating an AI department. It is about fundamentally changing the core of the company by building a data-centric operating architecture supported by an agile organization that enables ongoing change.
Competing in the age of AIis rich with relevant case studies. This includes the stories of startups that have built AI factories from the ground up such as Peleton, which disrupted the traditional home sports equipment market, to Ocado, which leveraged AI to digitize groceries, a market that relies on very tight profit margins. Youll also read about established tech firms, such as Microsoft, that have managed to thrive in the age of AI by going through multiple transformations. And there are stories of traditional companies like Walmart have leveraged digitization and AI to avoid the fate of the likes of Sears, the longstanding retail giant that filed for bankruptcy in 2018.
The rise of AI has also brought new meaning to network effects, a phenomenon that has been studied by tech companies since the founding of the first search engines and social networks.Competing in the age of AIdiscusses the various aspects and types of networks and how AI algorithms integrated into networks can boost growth, learning, and product improvement.
As other experts have already observed, advances in AI will have implications for everyone running an organization, not just the people developing the technology. Per Iansiti and Lakhani: Many of the best managers will have to retool and learn both the foundational knowledge behind AI and the ways that technology can be effectively deployed in their organizations business and operation models. They do not need to become data scientists, statisticians, programmers, or AI engineers; rather, just as every MBA student learns about accounting and its salience to business operations without wanting to become a professional accountant, managers need to do the same with AI and the related technology and knowledge stack.
This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.
Published January 1, 2021 22:00 UTC
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What the hell is an AI factory? - The Next Web
2020: A Year Full of Amazing AI papers- A Review Machine Learning Times – The Predictive Analytics Times
Originally published in GitHub.com.
A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, andcode
Even with everything that happened in the world this year, we still had the chance to see a lot of amazing research come out. Especially in the field of artificial intelligence. More, many important aspects were highlighted this year, like the ethical aspects, important biases, and much more. Artificial intelligence and our understanding of the human brain and its link to AI is constantly evolving, showing promising applications in the soon future.
Here are the most interesting research papers of the year, in case you missed any of them. In short, it is basically a curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation, link to a more in-depth article, and code (if applicable). Enjoy the read!
The complete reference to each paper is listed at the end of this repository.
To continue reading this article, click here.
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2020: A Year Full of Amazing AI papers- A Review Machine Learning Times - The Predictive Analytics Times
Ninjacart makes investment in machine learning to improve operations – InfotechLead.com
India-based B2B fresh produce supply chain company Ninjacart is making investment in machine learning to improve operations.We are investing in machine learning to improve forecasting, pricing engine, and crop recommendation to farmers based on our 5 years of data and research, Thirukumaran Nagarajan, CEO and Co-founder of Ninjacart, said.
This will lead to lower food wastage and create higher predictability and sustainability for both farmers and us. We continue to invest in supply chain technology and infrastructure to reach more customers and farmers contributing to the growth prospect of the sector enormously, he added.
The Ninjacart CEO said the platform leverages radio frequency identification (RFID) technology to track deliveries. The platform in the past has leveraged deep machine learning to perfect forecasting to 97 percent and reduce the overall wastage to 4 percent.
Ninjacart also has specific apps for the farmers to help them with demand forecasting, harvest planning and determining the price indent.
Ninjacart on October 12, 2020 announced Walmart and Flipkart made a fresh round of investment in the innovative startup disrupting Indias fresh produce market with its made-for-India business-to-business (B2B) supply chain infrastructure and technology solutions.
This follows the investment made by Walmart and Flipkart in December 2019.
Ninjacart has already received investment from Tiger Global, Accel, Tanglin, Steadview, Syngenta, Nandan Nilekani and Qualcomm among other prominent investors.
As Flipkart grows its Supermart (grocery) and Flipkart Quick (hyperlocal) businesses, Ninjacart will continue to play a key role in providing fresh produce to consumers across the country as they increasingly look at e-grocery to meet their needs.
Ninjacart has built Indias low-cost last-mile network using an innovative network model coupled with data science. Its less-than-12 hours connectivity from farm to store helps avoid the need for control temperature supply chain for perishable goods.
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Ninjacart makes investment in machine learning to improve operations - InfotechLead.com
Machine Learning As A Service Market Size, Share, Growth Trends, Revenue, Top Companies, Regional Outlook, and Forecast, 2020-2027 – LionLowdown
New Jersey, United States,- The report, titled Machine Learning As A Service Market Research Report is based on the extensive analysis of analysts and contains detailed information on the global market area. A detailed examination of the business landscape, as well as the essential parameters that shape the marketing matrix of the market, is included.
A thorough qualitative and quantitative study of the global market has been conducted in this report. The study takes into account various important aspects of the market by focusing on historical and forecast data. The report provides information on the SWOT analysis as well as Porters Five Forces Model and the PESTEL analysis.
The Machine Learning As A Service Market research documentation provides details on drivers and restraints, regional growth opportunities, market size, as well as the spectrum of competition, prominent market candidates, and segment analysis.
The following Manufacturers are covered in this report:
The report aims to enumerate various data and updates related to the World Market while developing various growth opportunities that are believed to support the market growth at a significant rate during the forecast period. The report provides an insightful overview of the Machine Learning As A Service market along with a well-summarized market definition and detailed industry scenario.
A comprehensive summary revolves around market dynamics. The segment encompasses insights into the drivers driving the growth of the Machine Learning As A Service market, restrictive parameters, existing growth opportunities in the industry, and the numerous trends that define the global marketplace. The report also includes data on pricing models and a value chain analysis. The expected growth of the market during the analysis period based on the estimates and historical figures has also been factored into the study.
The Machine Learning As A Service market report provides details of the expected CAGR recorded by the industry during the investigation period. Additionally, the report includes a number of technological advances and innovations that will boost the industrys prospects over the estimated period.
The report further studies the segmentation of the market based on product types offered in the market and their end-use/applications.
2-Ethylhexanoic Acid Market, By Production
2-Ethylhexanoic Acid Market, By Application
2-Ethylhexanoic Acid Market, By End User
Geographic Segmentation
The report offers an exhaustive assessment of different region-wise and country-wise Machine Learning As A Service markets such as the U.S., Canada, Germany, France, U.K., Italy, Russia, China, Japan, South Korea, India, Australia, Taiwan, Indonesia, Thailand, Malaysia, Philippines, Vietnam, Mexico, Brazil, Turkey, Saudi Arabia, U.A.E, etc.
North America, Europe, Asia-Pacific, Latin America, The Middle East and Africa
What are the main takeaways from this report?
A comprehensive price analysis was carried out in relation to product area, range of applications and regional landscape A comprehensive round up of the key market players and leading companies operating in the Machine Learning As A Service Market to understand the competitive perspective of the global marketplace Important information on the regulatory scenario that defines the market, as well as the inflow of investments from majority stakeholders in the world market An in-depth assessment of the various trends that are fueling overall market growth and their impact on global market projection and dynamics A descriptive guide that identifies the key aspects along with the many growth opportunities in the Machine Learning As A Service market A detailed documentation of a wide variety of ongoing issues in the world market that will encourage important developments
Some Points from Table of Content
1. Study coverage2. Summary3. Machine Learning As A Service Market Size by Manufacturer4. Production by region5. Consumption by region6.Machine Learning As A Service Market Size by Type7. Machine Learning As A Service Market size according to application8. Manufacturer profiles9. Production forecasts10. Consumption forecasts11. Analysis of customers upstream, industrial chain and downstream12. Opportunities and challenges, threats and influencing factors13. Main results14. Appendix
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Machine Learning As A Service Market Size, Share, Growth Trends, Revenue, Top Companies, Regional Outlook, and Forecast, 2020-2027 - LionLowdown
Machine Learning Software Market Trends Forecast Analysis by Manufacturers, Regions, Type and Application to 2026 – LionLowdown
In4Research has added a new report on Machine Learning Software Market which consist of in-depth synopsis of Machine Learning Software business vertical over the forecast period 2020 2026. The report is inclusive of the prominent industry drivers and provides an accurate analysis of the key growth trends and market outlook in the years to come in addition to the competitive hierarchy of this sphere.
The research report on Machine Learning Software market elaborates on the major trends defining the industry growth with regards to the regional terrain and competitive scenario. The document also lists out the limitations & challenges faced by industry participants alongside information such as growth opportunities. Apart from this, the report contains information regarding the impact of COVID-19 pandemic on the overall market outlook.
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Global Machine Learning Software Market Report is a professional and in-depth research report on the worlds major regional market conditions of the Machine Learning Software industry, focusing on the main regions and the main countries (United States, Europe, Japan and China).
Global Machine Learning Software market competition by top manufacturers, with production, price, revenue (value) and market share for each manufacturer.
Top players Covered in Machine Learning Software Market Report are:
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The report introduces Machine Learning Software basic information including definition, classification, application, industry chain structure, industry overview, policy analysis, and news analysis. Insightful predictions for the Machine Learning Software market for the coming few years have also been included in the report.
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The Machine Learning Software industry development trends and marketing channels are analyzed. Finally, the feasibility of new investment projects is assessed, and overall research conclusions offered.
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CHAPTERS COVERED IN Machine Learning Software MARKET REPORT ARE AS FOLLOW:
Impact of COVID-19 on Machine Learning Software Market
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Machine Learning Software Market Trends Forecast Analysis by Manufacturers, Regions, Type and Application to 2026 - LionLowdown
New Study Report on Global Machine Learning in Medicine Market by Forecast to 2025 | Epic Systems, Cerner Corporation, McKesson, Allscripts and many…
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New Study Report on Global Machine Learning in Medicine Market by Forecast to 2025 | Epic Systems, Cerner Corporation, McKesson, Allscripts and many...
Machine Learning Market Size, Share, Analysis, Demand, Applications, Sale, Growth Insight, Trends, Leaders, Services and Forecast to 2025 -…
Global Machine Learning Market (2020-2026) status and position of worldwide and key regions, with perspectives of manufacturers, regions, product types and end industries; this report analyses the topmost companies in worldwide and main regions, and splits the Machine Learning market by product type and applications/end industries. The Machine Learning market trend research process includes the analysis of different factors affecting the industry, with the government policy, competitive landscape, historical data, market environment, present trends in the market, upcoming technologies, technological innovation, and the technical progress in related industry, and market risks, market barriers, opportunities, and challenges.
The report has been prepared by taking into account several aspects of marketing research and analysis which includes market size estimations, market dynamics, company & market best practices, entry level marketing strategies, positioning and segmentations, opportunity analysis, economic forecasting, industry-specific technology solutions, roadmap analysis, targeting key buying criteria, and in-depth benchmarking of vendor offerings. This Machine Learning Market research report gives CAGR values along with its fluctuations for the specific forecast period.
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The Global Machine Learning market 2020 research provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Global Machine Learning Market Share analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status. Development policies and plans are discussed as well as manufacturing processes and cost structures are also analyzed. This report also states import/export consumption, supply and demand Figures, cost, price, revenue and gross margins. For each manufacturer covered, this report analyzes their Machine Learning manufacturing sites, capacity, production, ex-factory price, revenue and market share in global market.
The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions. The rapidly changing market scenario and initial and future assessment of the impact is covered in the report. The Machine Learning market report puts together a concise analysis of the growth factors influencing the current business scenario across various regions. Significant information pertaining to the industry analysis size, share, application, and statistics are summed in the report in order to present an ensemble prediction. Additionally, this report encompasses an accurate competitive analysis of major market players and their strategies during the projection timeline.
Browse the complete report @ https://www.adroitmarketresearch.com/industry-reports/machine-learning-market?utm_source=Pranali
Some of the key questions answered in these reports:What will the market growth rate, growth momentum or acceleration market carries during the forecast period?Which are the key factors driving the Machine Learning Market?What was the size of the emerging Machine Learning Market by value in 2019?What will be the size of the emerging Machine Learning Market in 2025?Which region is expected to hold the highest market share in the Machine Learning Market?What trends, challenges and barriers will impact the development and sizing of the Global Machine Learning Market?What is sales volume, revenue, and price analysis of top manufacturers of Machine Learning Market?What are the Machine Learning Market opportunities and threats faced by the vendors in the global Machine Learning Market Industry?
With tables and figures helping analyse worldwide Global Machine Learning Market growth factors, this research provides key statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the market.
Machine Learning Market research report delivers a close watch on leading competitors with strategic analysis, micro and macro market trend and scenarios, pricing analysis and a holistic overview of the market situations in the forecast period. It is a professional and a detailed report focusing on primary and secondary drivers, market share, leading segments and geographical analysis. Further, key players, major collaborations, merger & acquisitions along with trending innovation and business policies are reviewed in the report. The report contains basic, secondary and advanced information pertaining to the Machine Learning global status and trend, market size, share, growth, trends analysis, segment and forecasts from 2019-2025.
The scope of the report extends from market scenarios to comparative pricing between major players, cost and profit of the specified market regions. The numerical data is backed up by statistical tools such as SWOT analysis, BCG matrix, SCOT analysis, and PESTLE analysis. The statistics are represented in graphical format for a clear understanding on facts and figures.
Some Points from Table of Content:1.Executive Summary2.Assumptions and Acronyms Used3.Research Methodology4.Machine Learning Market Overview5.Machine Learning Supply Chain Analysis6.Machine Learning Pricing Analysis7.Global Machine Learning Market Analysis and Forecast by Type8.Global Machine Learning Market Analysis and Forecast by Application9.Global Machine Learning Market Analysis and Forecast by Sales Channel10.Global Machine Learning Market Analysis and Forecast by Region11.North America Machine Learning Market Analysis and Forecast12.Latin America Machine Learning Market Analysis and Forecast13.Europe Machine Learning Market Analysis and Forecast14.Asia Pacific Machine Learning Market Analysis and Forecast15.Middle East & Africa Machine Learning Market Analysis and Forecast16.Competition Landscape
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