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Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems.
Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications.
You'll learn how
Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications.
You'll learn how
325 pages, Paperback
Published October 17, 2023
13 people want to read
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Displaying 1 - 2 of 2 reviews
March 29, 2024
It is good book for beginners. It shows basics of ML and especially shows how to run ML using cloud services. Book contains some introduction and then it teaches ML on projects starting with simple project completely done using no-code approach and ending with tuning hyperparameters of existing model which it shows using several approaches (custom python code using Scikit-Learn, then Keras approach is shown and then Google Cloud BigQuery ML approach is shown). Book is written by people affiliated with Google and thus Google Cloud services are used across the book. Non-google tools and services are mentioned several times, but never shown in-depth.
I like the style that book is written in. It describes some problem and then explain ML topics along the way as they come in. Frequently, topics come in when solving some problem based on received results which is nice because it seems like real solving ML problems.
Pay attention to running examples in cloud on your own. Book mostly do not mention expense of service used. Some examples are quite expensive (in my case, experiment from chapter 4 cost me about 64 USD), most later exercises cost less than one dollar. I recommend enabling Google Cloud trial which gives you credits for covering these experiments.
Book assumes understanding of basic stat properties. Some of them are explained very well (for example, corelation), some are not described at all (for example, degree of freedom). Similarly, book explain some basic of Python, SQL, and basic Linux commands, but some previous experiences are helpful to proper understand the codes. Most codes are self-explanatory and even non-programmers can use, understand and modify them.
I recommend buying e-book instead of print book. At first, while it says low-code, it still contains code and some of them are quite long (but still simple to understand, just long to write). Additionally, for some reason book encodes most (but not all) links to online sources as clickable hyperlinks, but this naturally do not work in printed book. I am not sure why do they did it in this way, because other O’Reilly book which I read had all links explicitly shown as shortened URLs.
As of march of 2024, book is up to day including screenshots of cloud services.
I like the style that book is written in. It describes some problem and then explain ML topics along the way as they come in. Frequently, topics come in when solving some problem based on received results which is nice because it seems like real solving ML problems.
Pay attention to running examples in cloud on your own. Book mostly do not mention expense of service used. Some examples are quite expensive (in my case, experiment from chapter 4 cost me about 64 USD), most later exercises cost less than one dollar. I recommend enabling Google Cloud trial which gives you credits for covering these experiments.
Book assumes understanding of basic stat properties. Some of them are explained very well (for example, corelation), some are not described at all (for example, degree of freedom). Similarly, book explain some basic of Python, SQL, and basic Linux commands, but some previous experiences are helpful to proper understand the codes. Most codes are self-explanatory and even non-programmers can use, understand and modify them.
I recommend buying e-book instead of print book. At first, while it says low-code, it still contains code and some of them are quite long (but still simple to understand, just long to write). Additionally, for some reason book encodes most (but not all) links to online sources as clickable hyperlinks, but this naturally do not work in printed book. I am not sure why do they did it in this way, because other O’Reilly book which I read had all links explicitly shown as shortened URLs.
As of march of 2024, book is up to day including screenshots of cloud services.
November 2, 2024
Should be a good starting point for non-coder, but Chapter 7 and 8 are both steep in knowledge background.
Displaying 1 - 2 of 2 reviews