Download or read book Deep Finance written by Glenn Hopper and published by Leaders Press. This book was released on 2021-11-16 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Finance is informative, enlightening, and embraces the innovation all around us - perfect for trailblazing CFOs ready to dive deep into an era of information, analytics, and Big Data. ARE YOU READY FOR A DIGITAL TRANSFORMATION? LEAD THE AGE OF ANALYTICS WITH DEEP FINANCE. Glenn Hopper uses a unique blend of financial leadership and technical expertise to help businesses of all sizes optimize and modernize. Not a software engineer? Neither is Glenn Hopper, but his story shows how any finance leader can embrace the tech innovations shaping our world to revolutionize finance operations. Accounting has come a long way since the time of the abacus, computer punch cards, or even the paper ledger. Modern finance leaders have the ability and tools to build a team that harnesses the power of business intelligence to make their jobs easier. Leaders who aren’t aware of these opportunities are simply going to be outpaced by competitors willing to adapt to the 21st century and beyond. Deep Finance will take you from asking “What Is AI?” to walking a clear path toward your own digital transformation. Elevate your leadership and be a champion for data science in your department. In Deep Finance, you will: · Study the history of accounting—and why the age of analytics is the next logical step for all finance departments. · Step into the age of artificial intelligence and view the pathway to a digital transformation. · Expand your role as CFO by integrating business intelligence and analytics into your everyday tasks. · Weigh the pros and cons of buying or building software to manage transactions, analyze and collect data, and identify trends. · Become a “New Age CFO” who can make better financial decisions and identify where your company is moving. · Develop the language to elevate your entire management team as you enter the age of artificial intelligence. Don’t get left behind. Your competitors or team members recognize the possibilities that are available to finance departments everywhere. Take the first steps toward a digital transformation and evolution to a data-driven culture. Grab your copy of Deep Finance today!
Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Download or read book Deep Finance written by Glenn Hopper and published by . This book was released on 2021-08-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Finance is informative, enlightening, and embraces the innovation all around us - perfect for trailblazing CFOs ready to dive deep into an era of information, analytics, and Big Data.ARE YOU READY FOR A DIGITAL TRANSFORMATION? LEAD THE AGE OF ANALYTICS WITH DEEP FINANCE.Glenn Hopper uses a unique blend of financial leadership and technical expertise to help businesses of all sizes optimize and modernize. Not a software engineer? Neither is Glenn Hopper, but his story shows how any finance leader can embrace the tech innovations shaping our world to revolutionize finance operations.Accounting has come a long way since the time of the abacus, computer punch cards, or even the paper ledger. Modern finance leaders have the ability and tools to build a team that harnesses the power of business intelligence to make their jobs easier. Leaders who aren't aware of these opportunities are simply going to be outpaced by competitors willing to adapt to the 21st century and beyond.Deep Finance will take you from asking "What Is AI?" to walking a clear path toward your own digital transformation.Elevate your leadership and be a champion for data science in your department. In Deep Finance, you will: Study the history of accounting-and why the age of analytics is the next logical step for all finance departments. Step into the age of artificial intelligence and view the pathway to a digital transformation. Expand your role as CFO by integrating business intelligence and analytics into your everyday tasks. Weigh the pros and cons of buying or building software to manage transactions, analyze and collect data, and identify trends. Become a "New Age CFO" who can make better financial decisions and identify where your company is moving. Develop the language to elevate your entire management team as you enter the age of artificial intelligence. Don't get left behind. Your competitors or team members recognize the possibilities that are available to finance departments everywhere.Take the first steps toward a digital transformation and evolution to a data-driven culture. Grab your copy of Deep Finance today!
Download or read book Deep Learning for Finance written by Sofien Kaabar and published by "O'Reilly Media, Inc.". This book was released on 2024-01-08 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential
Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Download or read book A Free Nation Deep in Debt written by James MacDonald and published by Princeton University Press. This book was released on 2006-05-22 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the greater part of recorded history the most successful and powerful states were autocracies; yet now the world is increasingly dominated by democracies. In A Free Nation Deep in Debt, James Macdonald provides a novel answer for how and why this political transformation occurred. The pressures of war finance led ancient states to store up treasure; and treasure accumulation invariably favored autocratic states. But when the art of public borrowing was developed by the city-states of medieval Italy as a democratic alternative to the treasure chest, the balance of power tipped. From that point on, the pressures of war favored states with the greatest public creditworthiness; and the most creditworthy states were invariably those in which the people who provided the money also controlled the government. Democracy had found a secret weapon and the era of the citizen creditor was born. Macdonald unfolds this tale in a sweeping history that starts in biblical times, passes via medieval Italy to the wars and revolutions of the seventeenth and eighteenth centuries, and ends with the great bond drives that financed the two world wars.
Download or read book Modern Computational Finance written by Antoine Savine and published by John Wiley & Sons. This book was released on 2018-11-20 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware. AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance. Danske Bank's wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank's systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software. This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates. The book comes with professional source code in C++, including an efficient, up to date implementation of AAD and a generic parallel simulation library. Modern C++, high performance parallel programming and interfacing C++ with Excel are also covered. The book builds the code step-by-step, while the code illustrates the concepts and notions developed in the book.
Download or read book Artificial Intelligence in Finance written by Yves Hilpisch and published by "O'Reilly Media, Inc.". This book was released on 2020-10-14 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about
Download or read book Deep Value written by Tobias E. Carlisle and published by John Wiley & Sons. This book was released on 2014-08-18 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: The economic climate is ripe for another golden age of shareholder activism Deep Value: Why Activist Investors and Other Contrarians Battle for Control of Losing Corporations is a must-read exploration of deep value investment strategy, describing the evolution of the theories of valuation and shareholder activism from Graham to Icahn and beyond. The book combines engaging anecdotes with industry research to illustrate the principles and methods of this complex strategy, and explains the reasoning behind seemingly incomprehensible activist maneuvers. Written by an active value investor, Deep Value provides an insider's perspective on shareholder activist strategies in a format accessible to both professional investors and laypeople. The Deep Value investment philosophy as described by Graham initially identified targets by their discount to liquidation value. This approach was extremely effective, but those opportunities are few and far between in the modern market, forcing activists to adapt. Current activists assess value from a much broader palate, and exploit a much wider range of tools to achieve their goals. Deep Value enumerates and expands upon the resources and strategies available to value investors today, and describes how the economic climate is allowing value investing to re-emerge. Topics include: Target identification, and determining the most advantageous ends Strategies and tactics of effective activism Unseating management and fomenting change Eyeing conditions for the next M&A boom Activist hedge funds have been quiet since the early 2000s, but economic conditions, shareholder sentiment, and available opportunities are creating a fertile environment for another golden age of activism. Deep Value: Why Activist Investors and Other Contrarians Battle for Control of Losing Corporations provides the in-depth information investors need to get up to speed before getting left behind.
Download or read book Python for Finance Cookbook written by Eryk Lewinson and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
Download or read book DeFi and the Future of Finance written by Campbell R. Harvey and published by John Wiley & Sons. This book was released on 2021-08-24 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the Global Financial Crisis in 2008, our financial infrastructure failed. Governments bailed out the very institutions that let the economy down. This episode spurred a serious rethink of our financial system. Does it make any sense that it takes two days to settle a stock transaction? Why do retailers, operating on razor thin margins, have to pay 3% for every customer credit card swipe? Why does it take two days to transfer money from a bank account to a brokerage—or any other company? Why are savings rates miniscule or negative? Why is it so difficult for entrepreneurs to get financing at traditional banks? In DeFi and the Future of Finance, Campbell R. Harvey, Ashwin Ramachandran and Joey Santoro, introduce the new world of Decentralized Finance. The book argues that the current financial landscape is ripe for disruption and we are seeing, in real time, the reinvention of finance. The authors provide the reader with a clear assessment of the problems with the current financial system and how DeFi solves many of these problems. The essence of DeFi is that we interact with peers—there is no brick and mortar and all of the associated costs. Savings and lending are reinvented. Trading takes place with algorithms far removed from traditional brokerages. The book conducts a deep dive on some of the most innovative protocols such as Uniswap and Compound. Many of the companies featured in the book you might not have heard of—however, you will in the future. As with any new technology, there are a myriad of risks and the authors carefully catalogue these risks and assess which ones can be successfully mitigated. Ideally suited for people working in any part of the finance industry as well as financial policy makers, DeFi and the Future of Finance gives readers a vision of the future. The world of finance will fundamentally be changed over the coming decade. The book enables you to become part of the disruption – not the target of the disruption.
Download or read book Value written by McKinsey & Company Inc. and published by John Wiley & Sons. This book was released on 2010-10-26 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible guide to the essential issues of corporate finance While you can find numerous books focused on the topic of corporate finance, few offer the type of information managers need to help them make important decisions day in and day out. Value explores the core of corporate finance without getting bogged down in numbers and is intended to give managers an accessible guide to both the foundations and applications of corporate finance. Filled with in-depth insights from experts at McKinsey & Company, this reliable resource takes a much more qualitative approach to what the authors consider a lost art. Discusses the four foundational principles of corporate finance Effectively applies the theory of value creation to our economy Examines ways to maintain and grow value through mergers, acquisitions, and portfolio management Addresses how to ensure your company has the right governance, performance measurement, and internal discussions to encourage value-creating decisions A perfect companion to the Fifth Edition of Valuation, this book will put the various issues associated with corporate finance in perspective.
Download or read book Value Investing written by Bruce C. Greenwald and published by John Wiley & Sons. This book was released on 2004-01-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the "guru to Wall Street's gurus" comes the fundamental techniques of value investing and their applications Bruce Greenwald is one of the leading authorities on value investing. Some of the savviest people on Wall Street have taken his Columbia Business School executive education course on the subject. Now this dynamic and popular teacher, with some colleagues, reveals the fundamental principles of value investing, the one investment technique that has proven itself consistently over time. After covering general techniques of value investing, the book proceeds to illustrate their applications through profiles of Warren Buffett, Michael Price, Mario Gabellio, and other successful value investors. A number of case studies highlight the techniques in practice. Bruce C. N. Greenwald (New York, NY) is the Robert Heilbrunn Professor of Finance and Asset Management at Columbia University. Judd Kahn, PhD (New York, NY), is a member of Morningside Value Investors. Paul D. Sonkin (New York, NY) is the investment manager of the Hummingbird Value Fund. Michael van Biema (New York, NY) is an Assistant Professor at the Graduate School of Business, Columbia University.
Download or read book Seeking Virtue in Finance written by JC de Swaan and published by Cambridge University Press. This book was released on 2020-09-17 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the Global Financial Crisis, a surge of interest in the use of finance as a tool to address social and economic problems suggests the potential for a generational shift in how the finance industry operates and is perceived. J. C. de Swaan seeks to channel the forces of well-intentioned finance professionals to improve finance from within and help restore its focus on serving society. Drawing from inspiring individuals in the field, de Swaan proposes a framework for pursuing a viable career in finance while benefiting society and upholding humanistic values. In doing so, he challenges traditional concepts of success in the industry. This will also engage readers outside of finance who are concerned about the industry's impact on society.
Download or read book Principles of Project Finance written by E. R. Yescombe and published by Academic Press. This book was released on 2013-11-13 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Second Edition of this best-selling introduction for practitioners uses new material and updates to describe the changing environment for project finance. Integrating recent developments in credit markets with revised insights into making project finance deals, the second edition offers a balanced view of project financing by combining legal, contractual, scheduling, and other subjects. Its emphasis on concepts and techniques makes it critical for those who want to succeed in financing large projects. With extensive cross-references and a comprehensive glossary, the Second Edition presents anew a guide to the principles and practical issues that can commonly cause difficulties in commercial and financial negotiations. Provides a basic introduction to project finance and its relationship with other financing techniques Describes and explains: sources of project finance; typical commercial contracts (e.g., for construction of the project and sale of its product or services) and their effects on project-finance structures; project-finance risk assessment from the points of view of lenders, investors, and other project parties; how lenders and investors evaluate the risks and returns on a project; the rôle of the public sector in public-private partnerships and other privately-financed infrastructure projects; how all these issues are dealt with in the financing agreements
Download or read book The End of Finance written by Massimo Amato and published by John Wiley & Sons. This book was released on 2013-12-19 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new book by two distinguished Italian economists is a highly original contribution to our understanding of the origins and aftermath of the financial crisis. The authors show that the recent financial crisis cannot be understood simply as a malfunctioning in the subprime mortgage market: rather, it is rooted in a much more fundamental transformation, taking place over an extended time period, in the very nature of finance. The ‘end’ or purpose of finance is to be found in the social institutions by which the making and acceptance of promises of payment are made possible - that is, the creation and cancellation of debt contracts within a specified time frame. Amato and Fantacci argue that developments in the modern financial system by which debts are securitized has endangered this fundamental credit/debt structure. The illusion has been created that debts are universally liquid in the sense that they need not be redeemed but can be continually sold on in increasingly extensive global markets. What appears to have reduced the riskiness of default for individual agents has in fact increased the fragility of the system as a whole. The authors trace the origins of this profound transformation backwards in time, not just to the neoliberal reforms of the 1980s and 90s but to the birth of capitalist finance in the mercantile networks of the sixteenth and seventeenth centuries. This long historical perspective and deep analysis of the nature of finance enables the authors to tackle the challenges we face today in a fresh way - not simply by tinkering with existing mechanisms, but rather by asking the more profound question of how institutions might be devised in which finance could fulfil its essential functions.
Download or read book Machine Learning for Finance written by Saurav Singla and published by BPB Publications. This book was released on 2021-01-05 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the essentials of Machine Learning and its impact in financial sector KEY FEATURESÊ _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. _ÊUnderstand the Reinforcement Learning and their applications. _Learn the rising application of Machine Learning in the Finance sector. Ê_Exposure to data mining, data visualization and data analytics. DESCRIPTION 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.Ê Ê The book demonstrates how to solve some of the most common issues in the financial industry.Ê 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 Nave 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. Ê 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. WHAT WILL YOU LEARN _ Ê Ê Ê You will grasp the most relevant techniques of Machine Learning for everyday use. _ Ê Ê Ê You will be confident in building and implementing ML algorithms. _ Ê Ê Ê Familiarize the adoption of Machine Learning for your business need. _ Ê Ê Ê Discover more advanced concepts applied in banking and other sectors today. _ Ê Ê Ê Build mastery skillset in designing smart AI applications including NLP, Computer Vision and Deep Learning. WHO THIS BOOK IS FORÊ 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. TABLE OF CONTENTS 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
