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Machine learning for finance : beginner’s guide to explore machine learning in banking and finance / Saurav Singla.

By: Material type: TextTextPublisher: New Delhi : BPB Publications, 2020Edition: 1st editionDescription: 240 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9789389328622
Subject(s): DDC classification:
  • 23 658.404 S.S.M
Contents:
1. Introduction ‡ 2. Naïve Bayes, Normal Distribution & Clustering ‡ 3. Machine Learning for Data Structuring ‡ 4. Parsing Data Using NLP ‡ 5. Computer Vision ‡ 6. Neural Networks, GBM & Gradient Descent ‡ 7. Sequence Modeling ‡ 8. Reinforcement Learning for Financial Markets ‡ 9. Finance Use Cases ‡ 10. Impact of Machine Learning on Fintech ‡ 11. Machine Learning in Finance ‡ 12. e‑KYC & Anti‑Fraud Policy ‡ 13. Data Mining & Visualization ‡ 14. Advantages & Disadvantages of ML ‡ 15. Applications in Other Industries ‡ 16. Ethical Considerations in AI ‡ 17. AI in Banking ‡ 18. Common ML Algorithms ‡ 19. Frequently Asked Questions.
Summary: A practical introduction to machine learning applications in the financial sector, covering algorithms, NLP, computer vision, reinforcement learning, ethics, and real-world finance use cases.([turn0search2])([turn0search8])
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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Books Books Media and mass communication Library AJ1 658.404 S.S.M C.1 Available MA0002193
Total holds: 0

Includes bibliographical references.

1. Introduction ‡ 2. Naïve Bayes, Normal Distribution & Clustering ‡ 3. Machine Learning for Data Structuring ‡ 4. Parsing Data Using NLP ‡ 5. Computer Vision ‡ 6. Neural Networks, GBM & Gradient Descent ‡ 7. Sequence Modeling ‡ 8. Reinforcement Learning for Financial Markets ‡ 9. Finance Use Cases ‡ 10. Impact of Machine Learning on Fintech ‡ 11. Machine Learning in Finance ‡ 12. e‑KYC & Anti‑Fraud Policy ‡ 13. Data Mining & Visualization ‡ 14. Advantages & Disadvantages of ML ‡ 15. Applications in Other Industries ‡ 16. Ethical Considerations in AI ‡ 17. AI in Banking ‡ 18. Common ML Algorithms ‡ 19. Frequently Asked Questions.

A practical introduction to machine learning applications in the financial sector, covering algorithms, NLP, computer vision, reinforcement learning, ethics, and real-world finance use cases.([turn0search2])([turn0search8])

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