Shroff Publishers

🎉 Launching The MIT Press Indian Editions!🎉 ***Pre-order now*** tinyurl.com/ShroffMIT #datascience #cloudcomputing #deeplearning #airobotics #coding #robotics #mitpress #shroffpublishers

🌙✨ Eid Mubarak & Navroze Mubarak! 🌸 Wishing you peace, prosperity, and beautiful new beginnings 💫📚 Because great careers begin with good books ✨ #EidMubarak #NavrozeMubarak #FestiveWishes #NewBeginnings #Books #Education #Success

🤖 AI that Sees. Hears. Speaks. 📘 Multimodal AI Agents for Professionals — Releasing Soon! 🧠 Vision • Audio • Voice • Memory • Tools 🐍 Python | GenAI | Whisper | Ollama | Qdrant 312 pages | ₹750 Pre-Order Now 👇 share.google/vfRH4GIpUkVdSH… #MultimodalAI #AIAgents #GenAI

If you want to work in AI or Data Science, read this. O’Reilly published a 533-page book teaching the real analytical skills behind AI. Topics inside: • statistical learning • regression models • clustering • Monte Carlo methods • data visualization Basically the foundation of modern AI systems. I’m giving it away FREE. To get it: 1. Follow me 2. Like + RT 3. Comment AI I’ll DM the book.

Most AI/ML books won’t make you better. They’ll just make you feel busy. But a few actually change how you build. Here are 9 that cover what you really need to 𝗕𝗨𝗜𝗟𝗗, 𝗦𝗛𝗜𝗣 & 𝗦𝗖𝗔𝗟𝗘 AI systems: 1️⃣ AI Engineering Full-stack view — from data pipelines to production failures. Real-world systems, not theory. 2️⃣ Prompt Engineering for LLMs Patterns, testing, consistency. If you’re using Claude or OpenAI — start here. 3️⃣ Hands-On Large Language Models Covers Hugging Face, LangChain, vector DBs. Tools you’ll actually use daily. 4️⃣ LLM Engineer's Handbook RAG, fine-tuning, vector databases. Practical + code-heavy. 5️⃣ Building LLMs from Scratch From tokenization → attention. No black boxes. 6️⃣ Building LLMs for Production Scaling, inference, real traffic. Where most books actually stop. 7️⃣ Designing Machine Learning Systems Why models fail in production. Data drift, monitoring, reliability. 8️⃣ Deep Learning Dense but foundational. Explains why everything works. 9️⃣ Mathematics for Machine Learning Linear algebra, calculus, probability — applied to ML. That’s it. Nine books. Everything else is optional. Most people collect resources. Few actually go deep on the right ones. Which one are you starting with? 📚🚀

Practical Statistics for Data Scientists — 50+ Essential Concepts Using R and Python: amzn.to/2BqU4wE

Analytical Skills for AI and Data Science — Building Skills for an AI-Driven Enterprise: amzn.to/4b6fufu …helps practitioners to create value from AI and data science using an analytical skillset — each chapter illustrates how each skill works across a collection of use cases.

🎊 Happy Gudi Padwa! 🎊 May this auspicious new year fill your life with joy, prosperity, and new opportunities. ✨ 📚 Great Careers Begin with Good Books #HappyGudiPadwa #UgadiSpecial2026 #ShroffPublishers

If you want to work in AI or Data Science, read this. O’Reilly published a 533-page book teaching the real analytical skills behind AI. Topics inside: • statistical learning • regression models • clustering • Monte Carlo methods • data visualization Basically the foundation of modern AI systems. I’m giving it away FREE. To get it: 1. Follow me 2. Like + RT 3. Comment AI I’ll DM the book.

The Greek 🇬🇷 🇨🇾 translation of my book is now available! 🎉 That's Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd edition. You will find it (along with all the other versions and translations) at homl.info Huge thanks to the translators! 🙏

Data Science for Business — What You Need to Know about Data Mining and Data-Analytic Thinking: amzn.to/3dRgs18 ———— #ML #AI #MachineLearning #DataScientist #Analytics @LeadershipData

Python for Data Analysis, by @wesmckinn Definitive handbook for manipulating, processing, cleaning, & crunching datasets in Python. Updated 3rd edition is packed with practical case studies to solve a broad set of data analysis problems. Get it: amzn.to/3wJJaMy -or- Read it online: wesmckinney.com/book/

Analytical Skills for AI and Data Science — Building Skills for an AI-Driven Enterprise: amzn.to/4b6fufu …helps practitioners to create value from AI and data science using an analytical skillset — each chapter illustrates how each skill works across a collection of use cases.

Greyscale Indian reprint of DDIA 2nd edition is out on Amazon ..

"System Design Interview — An Insider’s Guide by Alex Xu or Designing Data-Intensive Applications? Which is better?" dev.to/somadevtoo/sys…

Machine Learning Design Patterns — Solutions to Common Challenges in Data Preparation, Model-Building, and MLOps: amzn.to/2W7YSy0 —————— #AI #ML #DataScience #DataScientist

Practical Linear Algebra for #DataScience — From Core Concepts to Applications Using #Python — amzn.to/3WWJKR4 ———— #DataScientist #AI #ML #MachineLearning #Mathematics

One of the side projects that keeps my mind busy! 👩🏻‍💻 Writing a new book, the 2nd Edition of Learning Microsoft Azure: Cloud and AI Native Development, in collaboration with my book publisher @OReillyMedia This time writing it with more chapters, enhanced, better and AI Native focus for intermediate-advance learners of Microsoft @Azure A big project of 2026 that I am excited to finish in time for publishing! The first draft of 2 chapters is out raw and unedited at O’Reilly’s platform oreilly.com/library/view/l… If you want to be part of the list of early readers, tech reviewers or contributors before it is 💯 final published, let me know! My first edition Learning Microsoft Azure: Cloud Computing and Development Fundamentals is still pretty good to those who are new to cloud and Azure learningmicrosoftazure.com and my on-demand course Breaking Into Cloud Engineering oreilly.com/library/view/b… #learningmicrosoftazurebook #microsoftazure #cloudengineering #mvpbuzz #earlyrelease

Why do O’Reilly books have animal images on their covers....? Is it just to help them stand out from the crowd, or is there another reason behind it....?

Another brief book review👇 🌟 Building LLMs for Production 🌟 TLDR: A hands-on technical book with code-heavy examples using LangChain, RAG and data pipelines. 👍 What’s good about the book: → Hands-on, with lots of code samples and explanations. → Focuses on building a real apps → Good for readers who like learning by reading code. 👎 What can be better: → Text-heavy and would benefit from more diagrams and colour. → A lot of the code is already showing its age, it is langchain, llamaindex, AutoGPT → Given how fast this space moves, some parts are already be outdated. 👉 Overall: Good hands-on book for readers who like learning through code, but it would benefit from a refresh and stronger visual presentation.

Have you read this book yet? get it here - amazon.com/dp/1098134354?… (aff)