Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

This is a "complete" book related to ML and AI in Finance with almost all the applications of ML/AI in finance presented along with case studies and code examples. There are separate chapters dedicated to each ML/AI type and the case studies presented for each are quite useful and intuitive. I was able to leverage the code of the case studies and the master template on the GitHub repo of the book and was able to implement some of the problem statements that I was thinking about for a long time in a couple of hours. With no doubt, one of the best books customized for ML and AI in finance. Highly recommended for folks curious about exploring current and future applications of Machine Learning in Finance from a practical perspective.

This book uses a hands-on and practical approach as opposed to other books in finance and machine learning books that dive deep into the a lot of theory from the start. The book gives you a great deal of context and practical tools for solving all kinds of problems. It is code-focused so you'll have the option to run working code on real problems throughout the book. The coverage of this book is extensive. It covers almost everything that is needed for someone to know about machine learning is finance. The book deals with the content in a very practical manner without too unnecessary equations. But, does so without diluting the intensity.

I’m sorry but the book does not get very basic concept right. Look at Figure 4.2!

Whether you are a quantitative analyst in a hedge fund or investment banks looking to start building machine learning models in Python, or a machine learning student looking to work on a ML related project, look no further! There is an excellent balance between theory/background and implementation. Needless to say, the Jupyter notes accompanying each chapter and case studies are more than helpful. In summary, this book is an absolute must-have for a Python-rooted data scientist/ML engineer focused on Finance.

This book is very clearly written, covering the basics (such as basic classification and regression models) and more advanced topics (for example, reinforcement learning). While working in finance, I have been observing how machine learning methods have become popular and widely used in hedge funds, banks and other financial institutions. Thus, I am happy to have such a nice and useful book on these methods and their applications. The authors have done a great job.

A really practical book. It has a GitHub code repo containing the python code for all case studies included with the book. The code can be easily customized for related ML/AI problems in Finance.

A great book with hands-on and detailed case studies. A great read for anyone interested in a career in ML.

The content is great - perfect combination of theory and application, but the version of the book I received was in BLACK & WHITE!!!! The charts and graphs, code references, and other conventions need to be in color to be effective. Not sure what happened here, but a black & white doesn't work for a book like this.

Decent overview of stuff, but don't expect to develop trading strategies just from this

Without colors

Reinforced learning is challenging concept and has very high potential for trading analysis. Since world is changing with times in multiple directions, assessing markets through reinforcement learning will boost the investors to work in a directional manner.

A comprehensive guide for a beginner-intermediate python skill level. Written categorically and makes you actually think through the code rather than just copy pasting it. Word of advice for beginners, on page 17, replace ‘data’ with dataset and remove ‘names=names’ that should rid your ’variable not defined’ and ‘int not defined iloc’ error. Great purchase, gonna be spending a lot of time on it.

I only read so far the chapter 5, it is the best book ever I read on ML and finance. It covers complete ML spectrum and application in Finance. It will be my only ONE go-to book for reviewing ML algorithms and time series forecast. I will provide more feedback once I finish the book. Thank you Hariom and other co-authors.