HARRISBURG, PA — It started as a joke at the dinner table.
Harrisburg University of Science and Technology (HU) doctoral student Meetu Malhotra ’27 and her husband, Rajeev Kumar, were deep in one of their long-running debates about artificial intelligence (AI) and machine learning when one of them joked, “Why don’t we just write this down?”
Nearly two years later, that conversation hit the shelves. “Decoding Machine Learning,” co-authored by Malhotra and Kumar and published by BPB Publications, is now available in paperback and Kindle editions.
“It wasn’t really a decision in the traditional sense,” Malhotra said. “The idea surfaced organically out of a dinner table conversation. But later, when I decided to look back on our conversations and our passion for AI, I thought, why not make this idea into a reality?”
What followed, she said, was “almost two years of life happening around a book.” Ideas were scribbled during long car rides and flights: notes on what to cover, how to explain it, and which real-life example would make a concept finally click.
“That notebook came everywhere,” Malhotra said. “And slowly, those scribbles turned into a structure. The structure turned into chapters. The chapters went through reviews. And finally, it made it off the notebook pages and onto actual shelves.”
A Book for Those Who ‘Nod Along’
Malhotra, who is pursuing a PhD in Data Science at HU, describes the book’s audience this way:
“You know that feeling when you walk into a room and everyone around you is confidently throwing around AI terms, and you’re nodding along, smiling, but inside you’re thinking, wait, do I actually know what any of this means? This book is for exactly that moment. It’s written for anyone who has heard these terms a hundred times but never had someone sit down and just explain them. Clear and simple, without making you feel like you should already know.”
The 332-page book moves from machine learning fundamentals and exploratory data analysis through supervised and unsupervised learning, ensemble methods, time series analysis, neural networks, natural language processing, recommendation systems, and a closing chapter on large language models (LLMs), pairing concepts with hands-on implementation in Python.
“We wanted a book that explained machine learning the way we actually explained it to each other — in a clear way and with genuine industry depth,” Malhotra said. “The goal was simple. We wanted you to finish a chapter and think, ‘Oh, okay, that actually makes sense.’”
Filling the Gap Her Mentees Keep Asking About
Malhotra draws on 18 years of industry experience in data analytics and machine learning, including work with global organizations. As a mentor, she said, one question comes up more than any other.
“It doesn’t matter if the person is a fresh graduate or someone switching careers. The question is always, where do I start, and in what order?” she said. “There is so much out there — videos, blogs, courses, tutorials — and it’s overwhelming. You don’t feel informed. You feel lost.”
Some of it, she added, is also simply wrong.
“I came across an article not long ago, with hundreds of views, and there was a basic data leakage mistake right there in the code. Nobody caught it. Nobody reviewed it. People were learning from it,” Malhotra said. “In machine learning, mistakes like that give you a model that looks great but fails in the real world. And that has real consequences.”
She sees a related pattern in how newcomers approach AI today.
“People tend to jump straight to LLMs without knowing the basics. The excitement around AI is real, and I completely understand it. But sometimes the foundation gets skipped. It made me think: what if there was a structured book to build that foundation properly? Without judgment. Just — here is where you start, here is the sequence, here is what ties the foundation together.”
What HU’s Doctoral Program Added
“At HU, I have learned to see things from the lens of a researcher,” Malhotra said. “The industry gave me 18 years of content, experience, and real-world depth, but the PhD taught me to look at that same knowledge differently, in a more precise manner. And I think that directly shaped how the book was structured, how concepts were sequenced, and how carefully every explanation was crafted.”
Her data science and machine learning coursework was especially useful in the book’s early phase, she said.
“The book was influenced by how these courses were designed,” Malhotra said. “So, in a way, the courses didn’t give me the content. They gave me the blueprint.”
Presenting in HU classrooms, she added, gave her the confidence to share what she knows with a global audience.
On Co-Authoring with a Spouse
A two-year writing project with a husband invites an obvious question, and Malhotra answers it directly.
“Let’s just say we survived it, and we’re still married, so I’d call that a success,” she said. “In all seriousness, though, it was the most rewarding experience. If something wasn’t clear enough, we would phrase it in a way that makes sense. If an example didn’t land, we would work together to write a better one. When either of us struggled, having another person to rely on offered a new perspective.”
The couple’s daughter earned a credit, too.
“She kept asking us questions, and we answered them in easier terms,” Malhotra said. “That’s how she became our unofficial editor.”
The book is the latest in a run of publications for Malhotra, who co-authored three books over the past year: “Combating Cyberbullying with Generative AI,” “Harnessing Generative AI to Combat Cyberbullying in Industry,” and “Automating Software Defect Detection Through Machine Learning and LLMs.” Asked what comes next, she is in no rush.
“I am waiting for the next conversation that can lead to another book,” she said. “But for now, I am just grateful for this one.”
“Decoding Machine Learning” is available in paperback and Kindle editions.
ABOUT HARRISBURG UNIVERSITY
Harrisburg University of Science and Technology (HU) is an independent, nonprofit university offering degrees in advanced manufacturing, analytics, biotechnology, cybersecurity, nursing, and other critical fields. Accredited by the Middle States Commission on Higher Education, HU serves a diverse student body through bachelor’s, master’s, and doctoral programs that link research with practical applications. For information about HU’s affordable STEM degrees and professional development programs, call 717.901.5146 or email Connect@HarrisburgU.edu. Stay in the know by following Harrisburg University on Facebook, Instagram, LinkedIn, X.com, and YouTube.
MEDIA CONTACT
Do you have questions about this story? Interested in lining up an interview? Please contact Dan Wilhelm, Director of Communications for Harrisburg University, at DWilhelm@HarrisburgU.edu or 717.901.5100×1724.
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