Machine Learning for Finance

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 by 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.

Machine Learning has become very important in the finance industry, which is mostly used for better risk management and risk analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. Machine Learning to empower fintech to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability.

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.
* Learn the rising application of Machine Learning in the Finance sector.
* Exposure to data mining, data visualization and data analytics.

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.
* Build mastery skillset in designing smart AI applications including NLP, Computer Vision and Deep Learning.

Who This Book is For

Data Scientist, Machine Learning Engineers and Individuals who want to adopt machine learning in the financial domain. Practitioners are working in banks, asset management, hedge funds or working the first time in the finance domain. Individuals who want to learn about applications of machine learning in finance or individuals entering the fintech domain.

1. Introduction
2. Naive Bayes, Normal Distribution and Automatic Clustering Processes
3. Machine Learning for Data Structuring
4. Parsing Data Using NLP
5. Computer Vision
6. Neural Network, GBM and 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. eKYC and Anti-Fraud Policy
13. Uses of Data Mining and Data Visualization
14. Advantages and Disadvantages of Machine Learning
15. Applications of Machine Learning in Other Industries
16. Ethical considerations in Artificial Intelligence
17. Artificial Intelligence in Banking
18. Common Machine Learning Algorithms
19. Frequently Asked Questions

Saurav is a high performing Senior Data Scientist with 15 years of deep expertise in the application of analytics, business intelligence, machine learning, and statistics in multiple industries and 3 years of consulting experience and 5 years of managing a team in the data science field. He is a creative problem solver with a unique mix of technical, business, and research proficiency that lends itself to developing key strategies and solutions with a significant impact on revenue and ROI. He has working experience in machine learning, statistics, natural language processing, and deep learning with extensive use of Python, R, SQL & Tableau.

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