Worth: $55.99
(as of Sep 12, 2025 07:49:03 UTC – Particulars)
A information to advances in machine studying for monetary professionals, with working Python code
Key FeaturesExplore advances in machine studying and easy methods to put them to work in monetary industries Clear clarification and skilled dialogue of how machine studying works, with an emphasis on monetary purposes Deep protection of superior machine studying approaches together with neural networks, GANs, and reinforcement studying Guide Description
Machine Studying for Finance explores new advances in machine studying and exhibits how they are often utilized throughout the monetary sector, together with in insurance coverage, transactions, and lending. It explains the ideas and algorithms behind the primary machine studying methods and offers instance Python code for implementing the fashions your self.
The e-book relies on Jannes Klaas’ expertise of operating machine studying coaching programs for monetary professionals. Somewhat than offering ready-made monetary algorithms, the e-book focuses on the superior ML ideas and concepts that may be utilized in all kinds of how.
The e-book exhibits how machine studying works on structured information, textual content, photographs, and time collection. It contains protection of generative adversarial studying, reinforcement studying, debugging, and launching machine studying merchandise. It discusses easy methods to combat bias in machine studying and ends with an exploration of Bayesian inference and probabilistic programming.
What you’ll learnApply machine studying to structured information, pure language, pictures, and written textual content How machine studying can detect fraud, forecast monetary tendencies, analyze buyer sentiments, and extra Implement heuristic baselines, time collection, generative fashions, and reinforcement studying in Python, scikit-learn, Keras, and TensorFlow Dig deep into neural networks, look at makes use of of GANs and reinforcement studying Debug machine studying purposes and put together them for launch Handle bias and privateness issues in machine learningWho this e-book is for
This e-book is right for readers who perceive math and Python, and wish to undertake machine studying in monetary purposes. The e-book assumes college-level data of math and statistics.
Desk of ContentsNeural Networks and Gradient-Primarily based OptimizationApplying Machine Studying to Structured DataUtilizing Laptop VisionUnderstanding Time SeriesParsing Textual Information with Pure Language ProcessingUsing Generative ModelsReinforcement Studying for Monetary MarketsPrivacy, Debugging, and Launching Your ProductsFighting BiasBayesian Inference and Probabilistic Programming
Writer : Packt Publishing
Publication date : Could 30 2019
Language : English
Print size : 456 pages
ISBN-10 : 1789136369
ISBN-13 : 978-1789136364
Merchandise weight : 776 g
Dimensions : 19.05 x 2.62 x 23.5 cm
Greatest Sellers Rank: #1,766,637 in Books (See Prime 100 in Books) #604 in Enterprise Intelligence Instruments #682 in AI Human Imaginative and prescient & Language Techniques #707 in A.I. Neural Networks
Buyer Evaluations: 3.1 3.1 out of 5 stars 7 scores var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘prepared’).execute(operate(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click on’, { “allowLinkDefault”: true }, operate (occasion) { if (window.ue) } ); } }); P.when(‘A’, ‘cf’).execute(operate(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click on’, { “allowLinkDefault” : true }, operate(occasion){ if(window.ue) 0) + 1); }); });
