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Machine Learning for Finance
Machine Learning for Finance

Machine Learning for Finance: Principles and practice for financial insiders

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Profile Icon James Le Profile Icon Jannes Klaas
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£9.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (59 Ratings)
Paperback May 2019 456 pages 1st Edition
eBook
£26.99
Paperback
£32.99
Subscription
Free Trial
Renews at £9.99p/m
Arrow left icon
Profile Icon James Le Profile Icon Jannes Klaas
Arrow right icon
£9.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (59 Ratings)
Paperback May 2019 456 pages 1st Edition
eBook
£26.99
Paperback
£32.99
Subscription
Free Trial
Renews at £9.99p/m
eBook
£26.99
Paperback
£32.99
Subscription
Free Trial
Renews at £9.99p/m

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Key benefits

  • Explore advances in machine learning and how to put them to work in financial industries
  • Gain expert insights into how machine learning works, with an emphasis on financial applications
  • Discover advanced machine learning approaches, including neural networks, GANs, and reinforcement learning

Description

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.

Who is this book for?

This book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.

What you will learn

  • Apply machine learning to structured data, natural language, photographs, and written text
  • Understand how machine learning can help you detect fraud, forecast financial trends, analyze customer sentiments, and more
  • Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow
  • Delve into neural networks, and examine the uses of GANs and reinforcement learning
  • Debug machine learning applications and prepare them for launch
  • Address bias and privacy concerns in machine learning

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 30, 2019
Length: 456 pages
Edition : 1st
Language : English
ISBN-13 : 9781789136364
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Languages :

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Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
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Product Details

Publication date : May 30, 2019
Length: 456 pages
Edition : 1st
Language : English
ISBN-13 : 9781789136364
Category :
Languages :

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Frequently bought together


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Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
(59 Ratings)
5 star 59.3%
4 star 15.3%
3 star 8.5%
2 star 5.1%
1 star 11.9%
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Kenneth E. Mayer Jul 18, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
While going over supervised learning and unsupervised learning, the book also covers NLP with textual data and time series methods. The book is long but that is because it has many diagrams and much code.
Amazon Verified review Amazon
James Ma Jun 01, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Got a review copy from the publisher. Topics covered are comprehensive for machine learning and deep learning. Beginners can easily pick up and follow. Love the way how the author explains complex concepts in an easy to understand manner, containing explanations, illustrations, equations, and pictures. My favourite is the NLP part using Spacy on text data modeling.
Amazon Verified review Amazon
Anthony Ng Jun 09, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A much-needed text in the treatment of the latest development of AI for finance. I thoroughly enjoyed Jannes writing style which combined an appreciation of the state of art AI models and deep understanding of the challenges faced in working with financial data.It is refreshing to read a text that does not focus solely on modeling financial time series for either alpha or profit generation. Jannes wrote intelligently covering various groups of machine learning algorithms (CNN, RNN/LSTM, NLP, GAN, Reinforcement Learning and more) mixed-in with practical codes. His treatment of Reinforcement Learning, in particular, is a masterclass on its own. His treatment of the topics on privacy, biases, and Bayesian and probabilistic programming is not to be missed.
Amazon Verified review Amazon
Jesus Encinas Castillo Sep 24, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Irlon Terblanche Mar 13, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This course is definitely not for beginners. I had to check out a few other Python and 'ML for Trading' courses before coming back to this one. Once I did, I was able to follow the instructor, and I appreciate his rigor. He covers a lot in a short space of time, which is good if you want to get to the end-result quickly. So, I do recommend this course either for those of you who are experienced, or those who are willing to research concepts in parallel to taking this course. Congrats to the instructor.
Udemy Verified review Udemy
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