Machine Learning for Finance:Beginner’s guide to explore machine learning in banking and finance

Machine Learning for Finance:Beginner's guide to explore machine learning in banking and finance (English Edition)
Author:Saurav Singla
Publisher Finelybook 出版社:BPB Publications (November 26, 2020)
Language 语言:English
pages 页数:240 pages
ISBN-10 书号:9389328624
ISBN-13 书号:9789389328622

Book Description
Understand the essentials of Machine Learning and its impact in financial sector

Key Features

Explore the spectrum of machine learning and its usage.
Understand the NLP and Computer Vision and their use cases.
Understand the Neural Network, CNN, RNN and their applications.
Understand the Reinforcement Learning and their applications.
The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this review. Now and again, the advancement has been emotional, opening new ways to deal with long-standing innovation challenges, for example, progresses in PC vision and picture investigation.

The book demonstrates how to solve some of the most common issues in the financial industry. The book addresses real-life problems faced Author:practitioners on a daily basis. The book explains how machine learning works on structured data, text, and images. You will cover the exploration of Naïve Bayes, Normal Distribution, Clustering with Gaussian process, advanced neural network, sequence modeling, and reinforcement learning. Later chapters will discuss machine learning use cases in the finance sector and the implications of deep learning. The book ends with traditional machine learning algorithms.

What will you learn

You will grasp the most relevant techniques of Machine Learning for everyday use.
You will be confident in building and implementing ML algorithms.
Familiarize the adoption of Machine Learning for your business need.
Discover more advanced concepts applied in banking and other sectors today


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