Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies

by Steven Finlay

Please note that the fourth edition of this book is now available. This older edition is being "given away" at the lowest possible price.Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Consequently, organizations which understand these tools and know how to use them are benefiting at the expense of their rivals.Artificial Intelligence and Machine Learning for Business cuts through the hype and technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people. The focus is very much on practical application and how to work with technical specialists (data scientists) to maximize the benefits of these technologies. This third edition has been substantially revised and updated. It contains several new chapters and covers a broader set of topics than before, but retains the no-nonsense style of the original.Steven Finlay is a data scientist with more than 20 years' experience of developing practical machine learning-based solutions. He holds a PhD in management science and is an honorary research fellow at Lancaster University in the UK. He is currently Head of Analytics for Computershare Loan Services (CLS) in the UK. Dr Finlay has published a number of practically focused books about machine learning, artificial intelligence and financial services.Please note that the new (fourth) edition of this book is now available.

Genres: Artificial Intelligence, Business, Technology, Nonfiction, Amazon, Science

194 pages, Kindle Edition

Published June 30, 2018

Reviews

[Tariq Ibrahim · Rating 5 out of 5]

No non-sense entry to machine learning with no technical prerequisites

This book delivers on its promise as in its title. It explains the pivotal business case and decisions to take on while embarking on a machine learning project. It is also an entry to the subject, so advanced readers can skip it.

[Pan Jiuhai · Rating 5 out of 5]

Good book and easy to understand for beginner.

[Maurizio Boldrini · Rating 4 out of 5]

Easy to be read also for inexperienced readers in machine learning. Useful to get an overview and so consciously apply machine learning tools in business context.

[David · Rating 5 out of 5]

‘This is a good introduction on artificial intelligence and machine learning for business managers. If you’re a data scientist, you can skip it. But if you’re looking for an introduction to this field, then I highly recommend it. The book is concise and written clearly with practical examples on different predictive models and how to use and evaluate them. Well worth the time.

[Shane Burford · Rating 5 out of 5]

Great introduction

This book is a great introduction to machine learning. Important for anyone who wants to begin to explore how AI will change their profession.

[Eric · Rating 5 out of 5]

Excellent book!

[Abhishek Sengupta · Rating 3 out of 5]

A good book explaining and introducing AI and it’s possible impact. It got a bit boring in the middle. I would have preferred it to be more case study oriented or with examples of industry implementation and their impact. I really appreciated the ethical perspective of the author because that’s a big debate. However the book scratched the surface, went deep to explain different types of predictive models but then switch gears to implementation challenges. Overall an ok read for a beginner but not one who knows the basics and has experience.

[Emil Gigov · Rating 4 out of 5]

This book is a great introduction into the topic of artificial intelligence and machine learning for those who are not in a technical role. The language is easy to understand and deliberately avoids jargon which makes it very accessible even for those with no prior knowledge of the topic. Keeping the text short was a key objective of the author, though a few more case studies and examples of the application of AI would have been helpful.

[Jax Vullinghs · Rating 4 out of 5]

An accessible introduction to machine learning that describes the different types of predictive models, their respective benefits, and how to operationalise machine learning within a big organisation. The focus is on understanding the problem the model needs to solve, ensuring you have sufficient data to train the model, putting in place the tools and infrastructure required to analyse the data, and developing a plan for how the insights emerging from the model will be used.

[Virginia · Rating 4 out of 5]

⭐️⭐️⭐️⭐️
I received an advance review copy for free, and I am leaving this review voluntarily.
Artificial Intelligence and Machine Learning for Business is a clear and practical introduction to how AI and machine learning can drive success. I loved how it simplified complex concepts, making advanced technology feel accessible and actionable for any business!

[Yazan Al-masafah · Rating 3 out of 5]

Did not add much value

I expected the book to have a wider perceptive about how AI will evolve in the future. Also, business examples are a few and most of them are already well known.. nothing new

[Lyka Odan · Rating 2 out of 5]

I WANT TO READ THIS!!! WHERE CAN I DOWNLOAD IT

[Katy · Rating 4 out of 5]

Succinct and well written read on AI and machine learning at high level

[John Peltier · Rating 4 out of 5]

Solid entry level book for background and reference

[Mahesh · Rating 4 out of 5]

Good introductory read for developing an understanding of AI and data science. Some of the complex concepts are presented in a very simple and easy to understand way.

[Wairimu Mburu · Rating 5 out of 5]

Concise and Easy to Understand

This book is a great introduction to AI and why AI matters to a business, I found it easy to understand.

[Rob Tarling · Rating 5 out of 5]

This is a great read.

The author provides one of the clearest explanations I’ve yet seen of how AI actually creates value in organisations. It treats ML as decision infrastructure – scores, thresholds, constraints, regulation – rather than tech hype and walks through the whole pipeline from problem definition to deployment and governance in very practical terms.

The book reminds readers that “model building is only a few percent of the process”; advice that should be stapled to every data scientist’s monitor.

It doesn’t cover generative AI or large language models (i.e., my edition was published in 2018, and predates the current wave), but the core ideas about data, problems, decisions and oversight are completely transferable. Despite the lack of GenAI content, it’s still five stars from me.

Highly recommended.