Large Language Models for Mortals book released
I have published a new book, Large Language Models for Mortals: A Practical Guide for Analysts with Python. The book is available to purchase in my store, either as a paperback (for $59.99) or an epub (for $49.99).
The book is a tutorial on using python with all the major LLM foundation model providers (OpenAI, Anthropic, Google, and AWS Bedrock). The book goes through the basics of API calls, structured outputs, RAG applications, and tool-calling/MCP/agents. The book also has a chapter on LLM coding tools, with example walk throughs for GitHub Copilot, Claude Code (including how to set it up via AWS Bedrock), and Google’s Antigravity editor. (It also has a few examples of local models, which you can see Chapter 2 I discuss them before going onto the APIs in Chapter 3).
You can review the first 60 some pages (PDF link here if on Iphone).
You can see the table of contents for all of the sections, but it includes over 250 different python code snippets, and over 80 screenshots. The print book is in letter sized paper and is a total of 354 pages.
Why write this book?
I wrote the book because of the rapid pace of advancement in data science in just the past few years. I basically self-taught traditional machine learning applications during the end of my PhD and early career as a professor (so around 2015). That has been the focus of my work until the past year. The advancement of LLMs have really upended the data science industry in just the past few years, where the majority of my job flipped from traditional machine learning models to LLM applications.
The hype is real, and folks with similar background to me (advanced social science degrees) you need to learn these fundamentals to be able to get a data science job. This is my introduction to major components of foundation models. You will definitely need to know python basics before this book (in which I would recommend my book, Data Science for Crime Analysis with Python. But other than that, you should be able to review the material in this book and have a good, fundamental understanding of large language models.
The book that goes through the major components of calling APIs (temperature, structured outputs, thinking/reasoning, caching, measuring costs), and introductions to designing more complicated systems (like RAG, agents, testing, and measuring how accurate your system is). In addition to this it gets your feet wet with LLM coding tools and what they can (reasonably) accomplish.
It is the book I wish I had several years ago. While the APIs are rapidly changing, this should give you the fundamentals in understanding and building real LLM applications with foundation models. The APIs will no doubt change and add new features, this is a good base from which to build upon though.
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