Machine Learning for Finance - ePUB

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Machine Learning for Finance: Beginner's guide to explore machine learning in banking and finance by Saurav Singla

The field of mechanical autonomy has significantly progressed with new wide-ranging and innovative accomplishments. One is the ascent of huge amount of information, which offers a greater chance to incorporate programming ability with mechanical frameworks. Another is the use of advanced sensors and associated gadgets to screen environmental factors like temperature, pneumatic force and light; the sky is the limit. These achievements have helped develop advanced robots for use in assembling, health care, security, etc.

Robots are generally used to perform straightforward and redundant tasks such as in vehicle production and in businesses involving hazardous situations. Several parts of mechanical technology include human-made consciousness; robots might be furnished with what might be compared to human faculties (e.g., vision, touch, and the capacity to detect temperature). Some are even fit for basic straightforward leadership. Research in mechanical autonomy has led to the design of robots with a level of independence that will allow versatility and basic leadership in an unstructured domain. The present mechanical robots don’t look like humans; a robot with human-like appearance is called an android.

At present, we are habituated to the Internet in numerous ways in our everyday life. For example, to explore an obscure place, we use Google Maps. We use social media to express our musings or sentiments. Or, to know the latest news, we access online news websites. If we attempt to comprehend the impact of science in our life, we will see that, really, these are all applications of AI and machine learning. Of late, there has been increased interest in machine learning, and more and more people are realizing the extent of new applications empowered by the ML approach. It fabricates a guide to make contact with the gadget and make the gadget reasonable to react to our reactions. As our life gets progressively computerized, in this book, we will discuss some of the mind-blowing applications of machine learning.

The fields of adaptive machining, profound learning, and computerized reasoning are quickly expanding, and will probably do so for a long time to come. The advancements have been phenomenal, opening new ways to deal with long-standing innovation challenges (e.g., progress in computer vision and picture investigation). These innovations alone are opening new pathways and applications in the field of ICME/MGI.

Over the nineteen chapters in this book, you will learn the following:

Chapter 1 discusses the process of how machines are taught, and the relation between machine learning, AI, deep learning, data mining, and statistics. It then focuses on the application of machine learning in the finance domain.

Chapter 2 discusses Naive Bayes, normal distribution, and automatic cluster detection with Gaussian process in depth. It then focuses on the application of machine learning in the cybersecurity domain.

Chapter 3 discusses the difference between structured and unstructured data and how advanced ML tools have helped businesses make quicker decisions. It then focuses on how ML tools are helping business put data in order so that it can be processed properly and in a more orderly manner.

Chapter 4 gives a tour of NLP, and it reviews the advantages and applications of NLP.

Chapter 5 is a key chapter as it discusses, in depth, computer vision and its applications. It then reviews the neural network architecture of computer vision. It then also discusses the application of computer vision in image recognition, biometric recognition, and software vulnerabilities.

Chapter 6 gives an in-depth discussion on neural networks, and how they work, and their different types and benefits. It then reviews gradient boosting machines and gradient descent.

Chapter 7 describes some of the different types of sequence modeling techniques, and reviews the ML modeling procedure, which involves feature engineering and selecting a model, model 

training, validating the model, and testing the model on new data.

Chapter 8 discusses which ML algorithm is suitable for your business problem. It then shows what a data reduction technique is. It also reviews the concept, types, and application of reinforcement learning.

Chapter 9 describes why finance is at the forefront of technology. It further discusses how machine learning can benefit the finance industry. It also reviews some use cases and the impact of ML on the finance industry.

Chapter 10 describes the impact of technology on FinTech companies. It then describes the key challenges faced by FinTech companies.

Chapter 11 reviews ML use cases in the banking sector. It also discusses the application of machine learning in cybersecurity, credit scoring, algorithmic trading, and robo-advising.

Chapter 12 describes the different ways information can be stolen. It then discusses the different security measures to stop this fraud.

Chapter 13 discusses data mining and data visualization, and their real applications.

Chapter 14 discusses the advantages and disadvantages of machine learning.

Chapter 15 discusses in depth the application of machine learning.

Chapter 16 reviews the impact of machine learning not only on the financial sector but also on our lives, economy, and humanity as a whole.

Chapter 17 discusses the applications of AI in banking.

Chapter 18 discusses in depth traditional algorithms such as regression, k-means clustering, k-nearest neighbor, principal component analysis algorithm, polynomial fitting and least squares algorithm, forced linear regression algorithm, support vector machine algorithm, conditional random fields algorithm, and decision tree algorithm.

Chapter 19 lists some frequently asked questions about machine learning.



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