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Global Machine learning market (2018-2023)
Machine learning market The value of the machine learning market is expected to reach USD 23.46 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 42.6% during 2018-2023. Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights. North America has the most significant share of the machine learning market, while Asia-Pacific is expected to witness the highest CAGR.
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Table of Contents:
Chapter 1: Executive summary
1.1. Market scope and segmentation
1.2. Key questions answered in this study
1.3. Executive summary

Chapter 2: Machine learning market – market overview
2.1. The global market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
2.2. Global – market drivers and challenges
2.3. Value chain analysis – machine learning market
2.4. Porter’s five forces analysis
2.5. Market size- by components (software tools, cloud and web-based APIs and others)
2.5. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.5. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.5. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.6. Market size- by service (professional services and managed services)
2.6. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.6. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.7. Market size- by organization size (SMEs and large enterprises)
2.7. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.7. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. Market size- by application (BFSI, automotive, healthcare, government and others)
2.8. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 3: North America machine learning market- market overview
3.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
3.2. North America – market drivers and challenges
3.3. Market size- by components (software tools, cloud and web-based APIs and others)
3.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.3. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.4. Market size- by service (professional services and managed services)
3.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. Market size- by organization size (SMEs and large enterprises)
3.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. Market size- by application (BFSI, automotive, healthcare, government and others)
3.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 4: Europe machine learning market – market overview
4.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
4.2. Europe – market drivers and challenges
4.3. Market size- By components (software tools, cloud and web-based APIs and others)
4.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.3. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.4. Market size- by service (professional services and managed services)
4.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. Market size- by organization size (SMEs and large enterprises)
4.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. Market size- By application (BFSI, automotive, healthcare, government and others)
4.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 5: Asia-Pacific machine learning market – market overview
5.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
5.2. Asia-Pacific- market drivers and challenges
5.3. Market size- by components (software tools, cloud and web-based APIs and others)
5.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.3. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.4. Market size- by service (professional services and managed services)
5.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. Market size- by organization size ( SMEs and large enterprises)
5.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. Market size- by application (BFSI, automotive, healthcare, government and others)
5.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 6: Latin America machine learning market – market overview
6.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
6.2. Latin America- market drivers and challenges
6.3. Market size- by components (software tools, cloud and web-based APIs and others)
6.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.3. c. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.4. Market size- by service (professional services and managed services)
6.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. Market size- by organisation size (SMEs and large enterprises)
6.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. Market size- By application (BFSI, automotive, healthcare, government and others)
6.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 7: The Middle East & Africa machine learning market – market overview
7.1. Market overview- market trends, market attractiveness analysis, geography-wise market revenue (USD)
7.2. Middle East and Africa- market drivers and challenges
7.3. Market size- by components (software tools, cloud and web-based APIs and others)
7.3. a. Revenue from software tools- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.3. b. Revenue from cloud and web-based APIs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.3. c. Revenue of Others – Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.4. Market size- by service (professional services and managed services)
7.4. a. Revenue from professional services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.4. b. Revenue from managed services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. Market size- by organization size (SMEs and large enterprises)
7.5. a. Revenue from SMEs- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. b. Revenue from large enterprises- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. Market size- by application (BFSI, automotive, healthcare, government and others)
7.6. a. Revenue from BFSI- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. b. Revenue from automotive- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. c. Revenue from healthcare- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. d. Revenue from government- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.6. e. Revenue from others- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 8: Competitive landscape
8.1. Microsoft
8.1.a. Company snapshot
8.1.b. Product offerings
8.1.c. Growth strategies
8.1.d. Initiatives
8.1.e. Geographical presence
8.1.f. Key numbers
8.2. Google Inc.
8.2.a. Company snapshot
8.2.b. Product offerings
8.2.c. Growth strategies
8.2.d. Initiatives
8.2.e. Geographical presence
8.2.f. Key numbers

8.3. IBM Watson
8.3.a. Company snapshot
8.3.b. Product offerings
8.3.c. Growth strategies
8.3.d. Initiatives
8.3.e. Geographical presence
8.3.f. Key numbers

8.4. Amazon
8.4.a. Company snapshot
8.4.b. Product offerings
8.4.c. Growth strategies
8.4.d. Initiatives
8.4.e. Geographical presence
8.4.f. Key numbers

8.5. Baidu
8.5.a. Company snapshot
8.5.b. Product offerings
8.5.c. Growth strategies
8.5.d. Initiatives
8.5.e. Geographical presence
8.5.f. Key numbers

8.6. Intel
8.6.a. Company snapshot
8.6.b. Product offerings
8.6.c. Growth strategies
8.6.d. Initiatives
8.6.e. Geographical presence
8.6.f. Key numbers

8.7. Facebook
8.7.a. Company snapshot
8.7.b. Product offerings
8.7.c. Growth strategies
8.7.d. Initiatives
8.7.e. Geographical presence
8.7.f. Key numbers

8.8. Apple Inc.
8.8.a. Company snapshot
8.8.b. Product offerings
8.8.c. Growth strategies
8.8.d. Initiatives
8.8.e. Geographical presence
8.8.f. Key numbers

8.9. Uber
8.9.a. Company snapshot
8.9.b. Product offerings
8.9.c. Growth strategies
8.9.d. Initiatives
8.9.e. Geographical presence
8.9.f. Key numbers

8.10. Luminoso
8.10.a. Company snapshot
8.10.b. Product offerings
8.10.c. Growth strategies
8.10.d. Initiatives
8.10.e. Geographical presence
8.10.f. Key numbers

Chapter 9: Conclusion

Chapter 10: Appendix
10.1. List of tables
10.2. Research methodology
10.3. Assumptions
10.4. About Netscribes Inc.
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