Packt Data Science & Machine Learning

Build real intuition through colourful, hands-on puzzles instead of boring theory. It helps you see what’s happening under the hood, which means better debugging, stronger model intuition, and sharper fundamentals. Linear algebra, but actually fun. #math #ml #data #DataScience

Turn vague questions into decision-ready insights in minutes ⚡ Join Sravya Madipalli (Data Science @ Superhuman), Shane Butler (AI Evals @ Ontra), & Hai Guan (Head of Data @ Ontra) live on June 27, 9:30 AM EDT.👉 [eventbrite.com/e/build-an-ai-…] #AI #DataAnalytics

From demo to deployment: build, secure, and scale real AI apps with Gabriele Venturi (PandasAI) — June 6, 9:30 AM EDT. Register here -> eventbrite.com/e/building-pro…🚀 #AI #GenAI #MLOps #AIAgents

Building GenAI apps? Stop guessing and start measuring—join Packt’s hands-on AI Evals workshop with Amy Chen & Sujeet Mishra (May 30, 9:30 AM–1:30 PM EDT) 🚀 #GenAI #LLM #AIEvals #AIEngineering eventbrite.com/e/ai-evals-for…

🚨 Few tickets left: Build a real-world forecasting pipeline (ML → Deep Learning → GenAI) in this 4-hour hands-on workshop led by experts Jeffrey Tackes & Manu Joseph — May 2, 9:30 AM EDT. #TimeSeries #Forecasting #DataScience Register here -> eventbrite.com/e/time-series-…

Design Production-Ready Power BI and Fabric Analytics Architectures & Systems — work and learn through real-world system scenarios. Register with 𝟰𝟬% 𝗢𝗙𝗙 using my discount code 'KIRK40' here: eventbrite.co.uk/e/design-produ… ...4-hour workshop hosted by @PacktDataML @PacktPublishing

Learn Microsoft Power BI — A comprehensive, beginner-friendly guide to real-world business intelligence [3rd Edition]: amzn.to/4mlvHTo by @GregDeckler via @PacktPublishing @PacktDataML

Workshop hosted by @PacktPublishing @PacktDataML Time Series Forecasting in Python: End-to-End Hands-on Practice Register here (with my 40% OFF discount code automatically applied for you): eventbrite.co.uk/e/time-series-… #DataScience #DataScientist #ML #GenAI #AI

Learn Microsoft Power BI — A comprehensive, beginner-friendly guide to real-world business intelligence [3rd Edition]: amzn.to/4mlvHTo by @GregDeckler via @PacktPublishing @PacktDataML

🌐Join this hands-on workshop “Context Engineering for Multi-Agent Systems” — hosted by @PacktPublishing @PacktDataML on April 25 ✅Register with my discount code ’Kirk30’ for 30% OFF: eventbrite.co.uk/e/context-engi… Denis Rothman will walk attendees through building stable, production-grade agentic systems, covering: 🔸 Semantic blueprints 🔸 Multi-agent orchestration (MCP) 🔸 High-fidelity RAG pipelines 🔸 Memory engineering 🔸 Trust, safeguards, production readiness This workshop is designed for: 🔷 AI engineers & developers 🔷 ML engineers & researchers 🔷 Software architects & platform engineers 🔷 Product teams building copilots/agents 🔷 Technical leaders driving AI adoption

"Building Business-Ready Generative #AI Systems — Build Human-Centered Generative AI Systems with Context-Aware Agents, Memory, and LLMs for the Enterprise" at amzn.to/3Jdcio5 v/ @PacktDataML Learn: 🔵Implement an AI controller with a conversation AI agent and orchestrator at its core 🔵Build contextual awareness with short-term, long-term, and cross-session memory 🔵Design cross-domain automation with multimodal reasoning, image generation, and voice features 🔵Expand a CoT agent by integrating consumer-memory understanding 🔵Integrate cutting-edge models of your choice without disrupting your existing GenAISys 🔵Connect to real-time external data while blocking security breaches

"Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning" — at amzn.to/448dSiA v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🔵Develop memory models to retain short-term and cross-session context 🟣Craft semantic blueprints and drive multi-agent orchestration with MCP 🟠Implement high-fidelity RAG pipelines with verifiable citations 🟡Apply safeguards against prompt injection and data poisoning 🔵Enforce moderation and policy-driven control in AI workflows 🟣Repurpose the Context Engine across legal, marketing, and beyond 🟠Deploy a scalable, observable Context Engine in production

Design Production-Ready Power BI and Fabric Analytics Architectures & Systems — work and learn through real-world system scenarios. Register with 𝟰𝟬% 𝗢𝗙𝗙 using my discount code 'KIRK40' here: eventbrite.co.uk/e/design-produ… ...4-hour workshop hosted by @PacktDataML @PacktPublishing

Get "Mathematics of Machine Learning" here: amzn.to/4eN7i52 by @TivadarDanka v/ @PacktDataML — GitHub: github.com/cosmic-cortex/ — Here is my review: 𝗧𝗵𝗲 𝗦𝗲𝘁 𝗢𝗳 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗧𝗵𝗮𝘁 𝗟𝗲𝗮𝗿𝗻 𝗙𝗿𝗼𝗺 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 This massive book is incredible, with its comprehensive coverage of numerous fields of mathematics and their intersection with the world of AI, data science, and machine learning (AI+DSML). I remember the very first time that I encountered machine learning. This was 20+ years ago, and that was already after 20+ years of being drenched in advanced mathematics as an astrophysicist. That first encounter of mine with ML was this definition: "Machine learning is the set of mathematical algorithms that learn from experience" (slightly paraphrased from the original quote by Tom Mitchell, CMU). That definition surprised me, confused me, motivated me, and changed the course of my career from astrophysics into AI+DSML. This book by Tivadar Danka captures the full meaning of that definition. The book covers thoroughly the many areas and domains of mathematics through which patterns in data are detected, described, learned, and recognized - all for the benefit of powering ML and AI algorithms, applications, and aspirations. This book will motivate you, surprise you, and inspire you in many ways, no matter what level of mathematics has (or has not) already propelled your career journey. There is room for all of us to grow. This is a great book, worthy to sit on everyone's desktop, ready to help you explore and exploit the full set of mathematical algorithms that learn from experience. The book is accompanied by a rich GitHub code repository of Jupyter notebooks. Learn by doing! Do by learning! Disclosure: the publisher provided me with a free review copy of the book.

Deep Learning with TensorFlow and Keras — Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models (3rd Ed., 667 pages): amzn.to/3gitVEJ v/ @PacktDataML ————— #DataScientist #DataScience #AI #MachineLearning #ML

New release from @PacktDataML at amzn.to/4sjCbni "Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection" [2nd Edition; 812 pages]

Machine Learning Solutions Architect Handbook — Practical Strategies and Best Practices in the ML Lifecycle, System Design, MLOps, and Generative AI: amzn.to/4bx8t6b v/ @PacktDataML

Pandas Cookbook — Practical recipes for scientific computing, time series and exploratory data analysis using Python: amzn.to/3Y0XRY5 v/ @PacktDataML [3rd ed.] 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: 🟠The PANDAS type system - how to best navigate it 🟢Import/export DataFrames to/from common data formats 🔵Data exploration in PANDAS through dozens of practice problems 🟣Grouping, aggregation, transformation, reshaping, and filtering data 🔴Merge data from different sources through PANDAS SQL-like operations 🟡Leverage the robust PANDAS time series functionality in advanced analyses 🔵Scale PANDAS operations to get the most out of your system 🟠The large ecosystem that PANDAS complements

Graph Machine Learning — Latest advancements in Graph Data to build robust Machine Learning algorithms (2nd Edition) — at amzn.to/45Y3LyI v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🟠Master new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL) 🔵Explore GML frameworks and their main characteristics 🟠Leverage LLMs for machine learning on graphs and learn about temporal learning 🔵Purchase of the print or Kindle book includes a free PDF eBook

🏆The Kaggle Book — Master Data Analysis and #DataScience Competitions with #MachineLearning, GenAI, and LLMs [2nd Edition]: amzn.to/4pxJpTC v/ @PacktDataML Table of Contents: 🔶Introducing Data Science Competition 🔷Organizing Data with Datasets 🔶Work & Learn with Kaggle Notebooks 🔷Kaggle Models 🔶Leveraging Discussion Forums 🔷Detailing Competition Tasks & Metrics 🔶Designing Good Validation Schemes 🔷Modeling for Tabular Competitions 🔶Hyperparameter Optimization 🔷Ensembling & Stacking Solutions 🔶Modeling Image Classification & Segmentation My Review (on Amazon): This 700-page masterpiece of writing covers everything you need—start to finish—to be a competitive coder, specifically for Kaggle data science competitions. The book covers the mechanics of the competitions (platform, resources, rankings, leaderboards), then the infrastructure (notebooks, GitHub, data sets, frameworks, discussion forums), and then nearly 500 pages devoted to "Elevating Your Game" (in-depth coverage of modeling techniques, evaluation metrics, validation strategies, hyperparameter optimization, ensembles, stacking, and various categories of competitions: tabular data, computer vision, NLP, Gen AI, simulations). The book concludes with a valuable section on building your Kaggle portfolio for career advancement and new opportunities. This is an outstanding data science / AI / Machine Learning training resource for anyone, even if you are not into the competitions, though especially if you are a dedicated Kaggler.

🟠2nd Edition, 746 pages, massive! ⬇️ Modern Computer Vision with #PyTorch #DeepLearning — from practical fundamentals to advanced applications and Generative AI: amzn.to/3xAkB7X v/ @PacktDataML —— #DataScience #MachineLearning #ML #GenAI #DataScientist —— 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🟣Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models. 🔵Build solutions for real-world computer vision problems using PyTorch. 🟢All code files are available on GitHub and can be run on Google Colab.