Description
BPB Publications Hands-On Supervised Learning With Python by Madeleine Shang Gnana Lakshmi T C
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.Learn the rising application of Machine Learning in the Finance sector. 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. 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. 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. Table of Contents IntroductionIntroductionNaive Bayes Normal Distribution and Automatic Clustering ProcessesMachine Learning for Data StructuringParsing Data Using NLPComputer VisionNeural Network GBM and Gradient DescentSequence ModelingReinforcement Learning For Financial MarketsFinance Use CasesImpact of Machine Learning on FintechMachine Learning in FinanceeKYC and Anti-Fraud PolicyUses of Data Mining and Data VisualizationAdvantages and Disadvantages of Machine LearningApplications of Machine Learning in Other IndustriesEthical considerations in Artificial IntelligenceArtificial Intelligence in BankingCommon Machine Learning AlgorithmsFrequently Asked Questions