The global Machine Learning market was valued at US$ 21040 million in 2023 and is anticipated to reach US$ 187750 million by 2030, witnessing a CAGR of 37.3% during the forecast period 2024-2030.
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 machine learning market is poised for significant growth, driven by a confluence of factors, including the expansion of data, advances in algorithms, a growing need for automation, and industry-specific applications. As organizations across various sectors increasingly recognize the benefits of machine learning, investments in this technology will likely continue to rise, leading to innovative applications and new market opportunities in the future. With ongoing advancements and increasing integration with other technologies, machine learning is set to play a pivotal role in transforming industries and businesses.
This report aims to provide a comprehensive presentation of the global market for Machine Learning, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning.
The Machine Learning market size, estimations, and forecasts are provided in terms of and revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning companies, new entrants, and industry chain related companies in this market with information on the revenues for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
麻豆原创 Segmentation
By Company
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
Segment by Type
Supervised Learning
Semi-supervised Learning
Unsupervised Learning
Reinforcement Learning
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
By Region
North America
United States
Canada
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Europe
Germany
France
U.K.
Italy
Russia
Nordic Countries
Rest of Europe
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
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1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global Machine Learning 麻豆原创 Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Supervised Learning
1.2.3 Semi-supervised Learning
1.2.4 Unsupervised Learning
1.2.5 Reinforcement Learning
1.3 麻豆原创 by Application
1.3.1 Global Machine Learning 麻豆原创 Growth by Application: 2019 VS 2023 VS 2030
1.3.2 麻豆原创ing and Advertising
1.3.3 Fraud Detection and Risk Management
1.3.4 Computer Vision
1.3.5 Security and Surveillance
1.3.6 Predictive Analytics
1.3.7 Augmented and Virtual Reality
1.3.8 Others
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Global Growth Trends
2.1 Global Machine Learning 麻豆原创 Perspective (2019-2030)
2.2 Global Machine Learning Growth Trends by Region
2.2.1 Global Machine Learning 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
2.2.2 Machine Learning Historic 麻豆原创 Size by Region (2019-2024)
2.2.3 Machine Learning Forecasted 麻豆原创 Size by Region (2025-2030)
2.3 Machine Learning 麻豆原创 Dynamics
2.3.1 Machine Learning Industry Trends
2.3.2 Machine Learning 麻豆原创 Drivers
2.3.3 Machine Learning 麻豆原创 Challenges
2.3.4 Machine Learning 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Top Machine Learning Players by Revenue
3.1.1 Global Top Machine Learning Players by Revenue (2019-2024)
3.1.2 Global Machine Learning Revenue 麻豆原创 Share by Players (2019-2024)
3.2 Global Machine Learning 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Global Key Players Ranking by Machine Learning Revenue
3.4 Global Machine Learning 麻豆原创 Concentration Ratio
3.4.1 Global Machine Learning 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Machine Learning Revenue in 2023
3.5 Global Key Players of Machine Learning Head office and Area Served
3.6 Global Key Players of Machine Learning, Product and Application
3.7 Global Key Players of Machine Learning, Date of Enter into This Industry
3.8 Mergers & Acquisitions, Expansion Plans
4 Machine Learning Breakdown Data by Type
4.1 Global Machine Learning Historic 麻豆原创 Size by Type (2019-2024)
4.2 Global Machine Learning Forecasted 麻豆原创 Size by Type (2025-2030)
5 Machine Learning Breakdown Data by Application
5.1 Global Machine Learning Historic 麻豆原创 Size by Application (2019-2024)
5.2 Global Machine Learning Forecasted 麻豆原创 Size by Application (2025-2030)
6 North America
6.1 North America Machine Learning 麻豆原创 Size (2019-2030)
6.2 North America Machine Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Machine Learning 麻豆原创 Size by Country (2019-2024)
6.4 North America Machine Learning 麻豆原创 Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Machine Learning 麻豆原创 Size (2019-2030)
7.2 Europe Machine Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Machine Learning 麻豆原创 Size by Country (2019-2024)
7.4 Europe Machine Learning 麻豆原创 Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning 麻豆原创 Size (2019-2030)
8.2 Asia-Pacific Machine Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Machine Learning 麻豆原创 Size by Region (2019-2024)
8.4 Asia-Pacific Machine Learning 麻豆原创 Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Machine Learning 麻豆原创 Size (2019-2030)
9.2 Latin America Machine Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Machine Learning 麻豆原创 Size by Country (2019-2024)
9.4 Latin America Machine Learning 麻豆原创 Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning 麻豆原创 Size (2019-2030)
10.2 Middle East & Africa Machine Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Machine Learning 麻豆原创 Size by Country (2019-2024)
10.4 Middle East & Africa Machine Learning 麻豆原创 Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 IBM
11.1.1 IBM Company Details
11.1.2 IBM Business Overview
11.1.3 IBM Machine Learning Introduction
11.1.4 IBM Revenue in Machine Learning Business (2019-2024)
11.1.5 IBM Recent Development
11.2 Dell
11.2.1 Dell Company Details
11.2.2 Dell Business Overview
11.2.3 Dell Machine Learning Introduction
11.2.4 Dell Revenue in Machine Learning Business (2019-2024)
11.2.5 Dell Recent Development
11.3 HPE
11.3.1 HPE Company Details
11.3.2 HPE Business Overview
11.3.3 HPE Machine Learning Introduction
11.3.4 HPE Revenue in Machine Learning Business (2019-2024)
11.3.5 HPE Recent Development
11.4 Oracle
11.4.1 Oracle Company Details
11.4.2 Oracle Business Overview
11.4.3 Oracle Machine Learning Introduction
11.4.4 Oracle Revenue in Machine Learning Business (2019-2024)
11.4.5 Oracle Recent Development
11.5 Google
11.5.1 Google Company Details
11.5.2 Google Business Overview
11.5.3 Google Machine Learning Introduction
11.5.4 Google Revenue in Machine Learning Business (2019-2024)
11.5.5 Google Recent Development
11.6 SAP
11.6.1 SAP Company Details
11.6.2 SAP Business Overview
11.6.3 SAP Machine Learning Introduction
11.6.4 SAP Revenue in Machine Learning Business (2019-2024)
11.6.5 SAP Recent Development
11.7 SAS Institute
11.7.1 SAS Institute Company Details
11.7.2 SAS Institute Business Overview
11.7.3 SAS Institute Machine Learning Introduction
11.7.4 SAS Institute Revenue in Machine Learning Business (2019-2024)
11.7.5 SAS Institute Recent Development
11.8 Fair Isaac Corporation (FICO)
11.8.1 Fair Isaac Corporation (FICO) Company Details
11.8.2 Fair Isaac Corporation (FICO) Business Overview
11.8.3 Fair Isaac Corporation (FICO) Machine Learning Introduction
11.8.4 Fair Isaac Corporation (FICO) Revenue in Machine Learning Business (2019-2024)
11.8.5 Fair Isaac Corporation (FICO) Recent Development
11.9 Baidu
11.9.1 Baidu Company Details
11.9.2 Baidu Business Overview
11.9.3 Baidu Machine Learning Introduction
11.9.4 Baidu Revenue in Machine Learning Business (2019-2024)
11.9.5 Baidu Recent Development
11.10 Intel
11.10.1 Intel Company Details
11.10.2 Intel Business Overview
11.10.3 Intel Machine Learning Introduction
11.10.4 Intel Revenue in Machine Learning Business (2019-2024)
11.10.5 Intel Recent Development
11.11 Amazon Web Services
11.11.1 Amazon Web Services Company Details
11.11.2 Amazon Web Services Business Overview
11.11.3 Amazon Web Services Machine Learning Introduction
11.11.4 Amazon Web Services Revenue in Machine Learning Business (2019-2024)
11.11.5 Amazon Web Services Recent Development
11.12 Microsoft
11.12.1 Microsoft Company Details
11.12.2 Microsoft Business Overview
11.12.3 Microsoft Machine Learning Introduction
11.12.4 Microsoft Revenue in Machine Learning Business (2019-2024)
11.12.5 Microsoft Recent Development
11.13 Yottamine Analytics
11.13.1 Yottamine Analytics Company Details
11.13.2 Yottamine Analytics Business Overview
11.13.3 Yottamine Analytics Machine Learning Introduction
11.13.4 Yottamine Analytics Revenue in Machine Learning Business (2019-2024)
11.13.5 Yottamine Analytics Recent Development
11.14 H2O.ai
11.14.1 H2O.ai Company Details
11.14.2 H2O.ai Business Overview
11.14.3 H2O.ai Machine Learning Introduction
11.14.4 H2O.ai Revenue in Machine Learning Business (2019-2024)
11.14.5 H2O.ai Recent Development
11.15 Databricks
11.15.1 Databricks Company Details
11.15.2 Databricks Business Overview
11.15.3 Databricks Machine Learning Introduction
11.15.4 Databricks Revenue in Machine Learning Business (2019-2024)
11.15.5 Databricks Recent Development
11.16 BigML
11.16.1 BigML Company Details
11.16.2 BigML Business Overview
11.16.3 BigML Machine Learning Introduction
11.16.4 BigML Revenue in Machine Learning Business (2019-2024)
11.16.5 BigML Recent Development
11.17 Dataiku
11.17.1 Dataiku Company Details
11.17.2 Dataiku Business Overview
11.17.3 Dataiku Machine Learning Introduction
11.17.4 Dataiku Revenue in Machine Learning Business (2019-2024)
11.17.5 Dataiku Recent Development
11.18 Veritone
11.18.1 Veritone Company Details
11.18.2 Veritone Business Overview
11.18.3 Veritone Machine Learning Introduction
11.18.4 Veritone Revenue in Machine Learning Business (2019-2024)
11.18.5 Veritone Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.1.1 Research Programs/Design
13.1.1.2 麻豆原创 Size Estimation
13.1.1.3 麻豆原创 Breakdown and Data Triangulation
13.1.2 Data Source
13.1.2.1 Secondary Sources
13.1.2.2 Primary Sources
13.2 Author Details
13.3 Disclaimer
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
听
听
*If Applicable.
