Global Machine Learning 麻豆原创 2024 by Company, Regions, Type and Application, Forecast to 2030

Publication ID:

GIR012407275

Publication Date:

January 1, 2024

Pages:

135

Publisher:

GIR

Countries:

Global [1]

USD 3,480.00

Software [2]

IT Services [3]

Computing & Electronics [4]

Description:

The global Machine Learning market size was valued at USD 15910 million in 2023 and is forecast to a readjusted size of USD 134440 million by 2030 with a CAGR of 35.6% during review period.
Machine learning (ML) is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task.Machine learning (ML) is a discipline of artificial intelligence (AI) that provides machines with the ability to automatically learn from data and past experiences while identifying patterns to make predictions with minimal human intervention.Machine learning (ML) methods enable computers to operate autonomously without explicit programming. ML applications are fed with new data, and they can independently learn, grow, develop, and adapt.
Global top five manufacturers of Machine Learning occupied for a share over 30 percent, key players are IBM, Dell, HPE, Oracle and Google, etc. North America is the largest market of Machine Learning, has a share nearly 40%, followed by Europe.
The report includes an overview of the development of the Machine Learning industry chain, the market status of 麻豆原创ing and Advertising (Supervised Learning, Semi-supervised Learning), Fraud Detection and Risk Management (Supervised Learning, Semi-supervised Learning), and key enterprises in developed and developing market, and analysed the cutting-edge technology, patent, hot applications and market trends of Machine Learning.
Regionally, the report analyzes the Machine Learning markets in key regions. North America and Europe are experiencing steady growth, driven by government initiatives and increasing consumer awareness. Asia-Pacific, particularly China, leads the global Machine Learning market, with robust domestic demand, supportive policies, and a strong manufacturing base.
Key Features:
The report presents comprehensive understanding of the Machine Learning market. It provides a holistic view of the industry, as well as detailed insights into individual components and stakeholders. The report analysis market dynamics, trends, challenges, and opportunities within the Machine Learning industry.
The report involves analyzing the market at a macro level:
麻豆原创 Sizing and Segmentation: Report collect data on the overall market size, including the revenue generated, and market share of different by Type (e.g., Supervised Learning, Semi-supervised Learning).
Industry Analysis: Report analyse the broader industry trends, such as government policies and regulations, technological advancements, consumer preferences, and market dynamics. This analysis helps in understanding the key drivers and challenges influencing the Machine Learning market.
Regional Analysis: The report involves examining the Machine Learning market at a regional or national level. Report analyses regional factors such as government incentives, infrastructure development, economic conditions, and consumer behaviour to identify variations and opportunities within different markets.
麻豆原创 Projections: Report covers the gathered data and analysis to make future projections and forecasts for the Machine Learning market. This may include estimating market growth rates, predicting market demand, and identifying emerging trends.
The report also involves a more granular approach to Machine Learning:
Company Analysis: Report covers individual Machine Learning players, suppliers, and other relevant industry players. This analysis includes studying their financial performance, market positioning, product portfolios, partnerships, and strategies.
Consumer Analysis: Report covers data on consumer behaviour, preferences, and attitudes towards Machine Learning This may involve surveys, interviews, and analysis of consumer reviews and feedback from different by Application (麻豆原创ing and Advertising, Fraud Detection and Risk Management).
Technology Analysis: Report covers specific technologies relevant to Machine Learning. It assesses the current state, advancements, and potential future developments in Machine Learning areas.
Competitive Landscape: By analyzing individual companies, suppliers, and consumers, the report present insights into the competitive landscape of the Machine Learning market. This analysis helps understand market share, competitive advantages, and potential areas for differentiation among industry players.
麻豆原创 Validation: The report involves validating findings and projections through primary research, such as surveys, interviews, and focus groups.
麻豆原创 Segmentation
Machine Learning market is split by Type and by Application. For the period 2019-2030, the growth among segments provides accurate calculations and forecasts for consumption value by Type, and by Application in terms of value.
麻豆原创 segment by Type
Supervised Learning
Semi-supervised Learning
Unsupervised Learning
Reinforcement Learning
麻豆原创 segment by Application
麻豆原创ing and Advertising
Fraud Detection and Risk Management
Computer Vision
Security and Surveillance
Predictive Analytics
Augmented and Virtual Reality
Others
麻豆原创 segment by players, this report covers
IBM
Dell
HPE
Oracle
Google
SAP
SAS Institute
Fair Isaac Corporation (FICO)
Baidu
Intel
Amazon Web Services
Microsoft
Yottamine Analytics
H2O.ai
Databricks
BigML
Dataiku
Veritone
麻豆原创 segment by regions, regional analysis covers
North America (United States, Canada, and Mexico)
Europe (Germany, France, UK, Russia, Italy, and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Australia and Rest of Asia-Pacific)
South America (Brazil, Argentina and Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe Machine Learning product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of Machine Learning, with revenue, gross margin and global market share of Machine Learning from 2019 to 2024.
Chapter 3, the Machine Learning competitive situation, revenue and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and application, with consumption value and growth rate by Type, application, from 2019 to 2030.
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2019 to 2024.and Machine Learning market forecast, by regions, type and application, with consumption value, from 2025 to 2030.
Chapter 11, market dynamics, drivers, restraints, trends and Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of Machine Learning.
Chapter 13, to describe Machine Learning research findings and conclusion.

Please Note - This is an on demand report and will be delivered in 2 business days (48 Hours) post payment.

Table Of Contents:

1 麻豆原创 Overview
1.1 Product Overview and Scope of Machine Learning
1.2 麻豆原创 Estimation Caveats and Base Year
1.3 Classification of Machine Learning by Type
1.3.1 Overview: Global Machine Learning 麻豆原创 Size by Type: 2019 Versus 2023 Versus 2030
1.3.2 Global Machine Learning Consumption Value 麻豆原创 Share by Type in 2023
1.3.3 Supervised Learning
1.3.4 Semi-supervised Learning
1.3.5 Unsupervised Learning
1.3.6 Reinforcement Learning
1.4 Global Machine Learning 麻豆原创 by Application
1.4.1 Overview: Global Machine Learning 麻豆原创 Size by Application: 2019 Versus 2023 Versus 2030
1.4.2 麻豆原创ing and Advertising
1.4.3 Fraud Detection and Risk Management
1.4.4 Computer Vision
1.4.5 Security and Surveillance
1.4.6 Predictive Analytics
1.4.7 Augmented and Virtual Reality
1.4.8 Others
1.5 Global Machine Learning 麻豆原创 Size & Forecast
1.6 Global Machine Learning 麻豆原创 Size and Forecast by Region
1.6.1 Global Machine Learning 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
1.6.2 Global Machine Learning 麻豆原创 Size by Region, (2019-2030)
1.6.3 North America Machine Learning 麻豆原创 Size and Prospect (2019-2030)
1.6.4 Europe Machine Learning 麻豆原创 Size and Prospect (2019-2030)
1.6.5 Asia-Pacific Machine Learning 麻豆原创 Size and Prospect (2019-2030)
1.6.6 South America Machine Learning 麻豆原创 Size and Prospect (2019-2030)
1.6.7 Middle East and Africa Machine Learning 麻豆原创 Size and Prospect (2019-2030)
2 Company Profiles
2.1 IBM
2.1.1 IBM Details
2.1.2 IBM Major Business
2.1.3 IBM Machine Learning Product and Solutions
2.1.4 IBM Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.1.5 IBM Recent Developments and Future Plans
2.2 Dell
2.2.1 Dell Details
2.2.2 Dell Major Business
2.2.3 Dell Machine Learning Product and Solutions
2.2.4 Dell Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.2.5 Dell Recent Developments and Future Plans
2.3 HPE
2.3.1 HPE Details
2.3.2 HPE Major Business
2.3.3 HPE Machine Learning Product and Solutions
2.3.4 HPE Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.3.5 HPE Recent Developments and Future Plans
2.4 Oracle
2.4.1 Oracle Details
2.4.2 Oracle Major Business
2.4.3 Oracle Machine Learning Product and Solutions
2.4.4 Oracle Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.4.5 Oracle Recent Developments and Future Plans
2.5 Google
2.5.1 Google Details
2.5.2 Google Major Business
2.5.3 Google Machine Learning Product and Solutions
2.5.4 Google Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.5.5 Google Recent Developments and Future Plans
2.6 SAP
2.6.1 SAP Details
2.6.2 SAP Major Business
2.6.3 SAP Machine Learning Product and Solutions
2.6.4 SAP Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.6.5 SAP Recent Developments and Future Plans
2.7 SAS Institute
2.7.1 SAS Institute Details
2.7.2 SAS Institute Major Business
2.7.3 SAS Institute Machine Learning Product and Solutions
2.7.4 SAS Institute Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.7.5 SAS Institute Recent Developments and Future Plans
2.8 Fair Isaac Corporation (FICO)
2.8.1 Fair Isaac Corporation (FICO) Details
2.8.2 Fair Isaac Corporation (FICO) Major Business
2.8.3 Fair Isaac Corporation (FICO) Machine Learning Product and Solutions
2.8.4 Fair Isaac Corporation (FICO) Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.8.5 Fair Isaac Corporation (FICO) Recent Developments and Future Plans
2.9 Baidu
2.9.1 Baidu Details
2.9.2 Baidu Major Business
2.9.3 Baidu Machine Learning Product and Solutions
2.9.4 Baidu Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.9.5 Baidu Recent Developments and Future Plans
2.10 Intel
2.10.1 Intel Details
2.10.2 Intel Major Business
2.10.3 Intel Machine Learning Product and Solutions
2.10.4 Intel Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.10.5 Intel Recent Developments and Future Plans
2.11 Amazon Web Services
2.11.1 Amazon Web Services Details
2.11.2 Amazon Web Services Major Business
2.11.3 Amazon Web Services Machine Learning Product and Solutions
2.11.4 Amazon Web Services Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.11.5 Amazon Web Services Recent Developments and Future Plans
2.12 Microsoft
2.12.1 Microsoft Details
2.12.2 Microsoft Major Business
2.12.3 Microsoft Machine Learning Product and Solutions
2.12.4 Microsoft Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.12.5 Microsoft Recent Developments and Future Plans
2.13 Yottamine Analytics
2.13.1 Yottamine Analytics Details
2.13.2 Yottamine Analytics Major Business
2.13.3 Yottamine Analytics Machine Learning Product and Solutions
2.13.4 Yottamine Analytics Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.13.5 Yottamine Analytics Recent Developments and Future Plans
2.14 H2O.ai
2.14.1 H2O.ai Details
2.14.2 H2O.ai Major Business
2.14.3 H2O.ai Machine Learning Product and Solutions
2.14.4 H2O.ai Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.14.5 H2O.ai Recent Developments and Future Plans
2.15 Databricks
2.15.1 Databricks Details
2.15.2 Databricks Major Business
2.15.3 Databricks Machine Learning Product and Solutions
2.15.4 Databricks Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.15.5 Databricks Recent Developments and Future Plans
2.16 BigML
2.16.1 BigML Details
2.16.2 BigML Major Business
2.16.3 BigML Machine Learning Product and Solutions
2.16.4 BigML Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.16.5 BigML Recent Developments and Future Plans
2.17 Dataiku
2.17.1 Dataiku Details
2.17.2 Dataiku Major Business
2.17.3 Dataiku Machine Learning Product and Solutions
2.17.4 Dataiku Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.17.5 Dataiku Recent Developments and Future Plans
2.18 Veritone
2.18.1 Veritone Details
2.18.2 Veritone Major Business
2.18.3 Veritone Machine Learning Product and Solutions
2.18.4 Veritone Machine Learning Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
2.18.5 Veritone Recent Developments and Future Plans
3 麻豆原创 Competition, by Players
3.1 Global Machine Learning Revenue and Share by Players (2019-2024)
3.2 麻豆原创 Share Analysis (2023)
3.2.1 麻豆原创 Share of Machine Learning by Company Revenue
3.2.2 Top 3 Machine Learning Players 麻豆原创 Share in 2023
3.2.3 Top 6 Machine Learning Players 麻豆原创 Share in 2023
3.3 Machine Learning 麻豆原创: Overall Company Footprint Analysis
3.3.1 Machine Learning 麻豆原创: Region Footprint
3.3.2 Machine Learning 麻豆原创: Company Product Type Footprint
3.3.3 Machine Learning 麻豆原创: Company Product Application Footprint
3.4 New 麻豆原创 Entrants and Barriers to 麻豆原创 Entry
3.5 Mergers, Acquisition, Agreements, and Collaborations
4 麻豆原创 Size Segment by Type
4.1 Global Machine Learning Consumption Value and 麻豆原创 Share by Type (2019-2024)
4.2 Global Machine Learning 麻豆原创 Forecast by Type (2025-2030)
5 麻豆原创 Size Segment by Application
5.1 Global Machine Learning Consumption Value 麻豆原创 Share by Application (2019-2024)
5.2 Global Machine Learning 麻豆原创 Forecast by Application (2025-2030)
6 North America
6.1 North America Machine Learning Consumption Value by Type (2019-2030)
6.2 North America Machine Learning Consumption Value by Application (2019-2030)
6.3 North America Machine Learning 麻豆原创 Size by Country
6.3.1 North America Machine Learning Consumption Value by Country (2019-2030)
6.3.2 United States Machine Learning 麻豆原创 Size and Forecast (2019-2030)
6.3.3 Canada Machine Learning 麻豆原创 Size and Forecast (2019-2030)
6.3.4 Mexico Machine Learning 麻豆原创 Size and Forecast (2019-2030)
7 Europe
7.1 Europe Machine Learning Consumption Value by Type (2019-2030)
7.2 Europe Machine Learning Consumption Value by Application (2019-2030)
7.3 Europe Machine Learning 麻豆原创 Size by Country
7.3.1 Europe Machine Learning Consumption Value by Country (2019-2030)
7.3.2 Germany Machine Learning 麻豆原创 Size and Forecast (2019-2030)
7.3.3 France Machine Learning 麻豆原创 Size and Forecast (2019-2030)
7.3.4 United Kingdom Machine Learning 麻豆原创 Size and Forecast (2019-2030)
7.3.5 Russia Machine Learning 麻豆原创 Size and Forecast (2019-2030)
7.3.6 Italy Machine Learning 麻豆原创 Size and Forecast (2019-2030)
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning Consumption Value by Type (2019-2030)
8.2 Asia-Pacific Machine Learning Consumption Value by Application (2019-2030)
8.3 Asia-Pacific Machine Learning 麻豆原创 Size by Region
8.3.1 Asia-Pacific Machine Learning Consumption Value by Region (2019-2030)
8.3.2 China Machine Learning 麻豆原创 Size and Forecast (2019-2030)
8.3.3 Japan Machine Learning 麻豆原创 Size and Forecast (2019-2030)
8.3.4 South Korea Machine Learning 麻豆原创 Size and Forecast (2019-2030)
8.3.5 India Machine Learning 麻豆原创 Size and Forecast (2019-2030)
8.3.6 Southeast Asia Machine Learning 麻豆原创 Size and Forecast (2019-2030)
8.3.7 Australia Machine Learning 麻豆原创 Size and Forecast (2019-2030)
9 South America
9.1 South America Machine Learning Consumption Value by Type (2019-2030)
9.2 South America Machine Learning Consumption Value by Application (2019-2030)
9.3 South America Machine Learning 麻豆原创 Size by Country
9.3.1 South America Machine Learning Consumption Value by Country (2019-2030)
9.3.2 Brazil Machine Learning 麻豆原创 Size and Forecast (2019-2030)
9.3.3 Argentina Machine Learning 麻豆原创 Size and Forecast (2019-2030)
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning Consumption Value by Type (2019-2030)
10.2 Middle East & Africa Machine Learning Consumption Value by Application (2019-2030)
10.3 Middle East & Africa Machine Learning 麻豆原创 Size by Country
10.3.1 Middle East & Africa Machine Learning Consumption Value by Country (2019-2030)
10.3.2 Turkey Machine Learning 麻豆原创 Size and Forecast (2019-2030)
10.3.3 Saudi Arabia Machine Learning 麻豆原创 Size and Forecast (2019-2030)
10.3.4 UAE Machine Learning 麻豆原创 Size and Forecast (2019-2030)
11 麻豆原创 Dynamics
11.1 Machine Learning 麻豆原创 Drivers
11.2 Machine Learning 麻豆原创 Restraints
11.3 Machine Learning Trends Analysis
11.4 Porters Five Forces Analysis
11.4.1 Threat of New Entrants
11.4.2 Bargaining Power of Suppliers
11.4.3 Bargaining Power of Buyers
11.4.4 Threat of Substitutes
11.4.5 Competitive Rivalry
12 Industry Chain Analysis
12.1 Machine Learning Industry Chain
12.2 Machine Learning Upstream Analysis
12.3 Machine Learning Midstream Analysis
12.4 Machine Learning Downstream Analysis
13 Research Findings and Conclusion
14 Appendix
14.1 Methodology
14.2 Research Process and Data Source
14.3 Disclaimer

Companies Mentioned:

IBM
Dell
HPE
Oracle
Google
SAP
SAS Institute
Fair Isaac Corporation (FICO)
Baidu
Intel
Amazon Web Services
Microsoft
Yottamine Analytics
H2O.ai
Databricks
BigML
Dataiku
Veritone

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