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

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.com/e/design-produ… ...4-hour workshop hosted by @PacktDataML @PacktPublishing

Most ML models never make it to production 🚨 Build, optimize & deploy real PyTorch systems in 3 hours (Mar 29) with Ashish Ranjan Jha — use code MLSYSTEM35 for 35% off 🚀eventbrite.com/e/building-pro…

Stop building dashboards, start building production-ready Power BI & Fabric systems → Use code BIMASTERY40 eventbrite.com/e/design-produ…

Virtual Workshop hosted by @PacktPublishing @PacktDataML "Ship a Production PyTorch System in a Day — Build, Train, Optimize, and Deploy" Register with my discount code 'KIRK40' to get 𝟰𝟬% 𝗢𝗙𝗙 and see all the amazing benefits of the workshop here: eventbrite.com/e/ship-product… (3 hours on March 29)

2nd Edition — now at amzn.to/45Y3LyI v/ @PacktDataML Graph Machine Learning — Latest advancements in Graph Data to build robust #MachineLearning algorithms 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

"Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond" - amzn.to/40IFEQR via @PacktDataML ————— #AI #ML #MachineLearning #DataScience #DataScientist #GenAI

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

You can listen in or subscribe on you favorite podcast host or here: pythonshow.com/p/56-python-il…

The latest episode of The Python Show podcast is out now! In this episode, I chat with two sisters who wrote a #Python beginner's book with many cute illustrations to help you learn the language.

Brilliant new release by @tarekatwan 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]

"Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond" - amzn.to/40IFEQR via @PacktDataML ————— #AI #ML #MachineLearning #DataScience #DataScientist #GenAI

2nd Edition, 746 pages, massive! Modern Computer Vision with PyTorch Deep Learning — from practical fundamentals to advanced applications and Generative AI: amzn.to/3xAkB7X v/ @PacktDataML 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.

Streamlit turns your data scripts into shareable web apps in minutes: streamlit.io + "Streamlit for #DataScience" = Step-by-step guide to building interactive data apps in #Python — amzn.to/45bY8IZ v/ @PacktDataML ——— #DataScientist #AI #ML #DataViz #Analytics

Here are the links: • Matrix Multiplication Workbook: a.co/d/0dxMexp3 • Deep Learning Math Workbook: a.co/d/0db54hXH @ProfTomYeh, these are excellent! Thank you!

Don't let your brain become yogurt because of AI. Exercise your mind. Think. Learn AI by hand. These workbooks will teach you the fundamental building blocks of deep learning: • Matrix multiplication • Dot product • Activations • Linear layers • Gradients And every single thing you need to understand deep learning from first principles. The Matrix Multiplication book is a beautiful, 254-page workbook that covers every imaginable detail of matrices. No computers. You only need the book and a pencil.

Machine Learning Engineering with Python — Manage the lifecycle of machine learning models using MLOps with practical examples: amzn.to/3QYTQAq v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🟢Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems 🔵[2nd edition] Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain 🟢Delve deep into key machine learning topics, CI/CD, and system design 🔵Explore core MLOps practices, such as model management and performance monitoring 🟢Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools

Python Machine Learning By Example: amzn.to/3MczBwk v/ @PacktDataML + GitHub: github.com/PacktPublishin… 518-pages! What you will learn: 🟣Machine learning best practices throughout data preparation and model development 🟣Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning 🟣Develop and fine-tune neural networks using TensorFlow and PyTorch 🟣Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP 🟣Build classifiers using support vector machines (SVMs) and boost performance with PCA 🟣Avoid overfitting using regularization, feature selection, and more

🏆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.

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

Exciting new Virtual Workshop hosted by @PacktPublishing @PacktDataML "Ship a Production PyTorch System in a Day — Build, Train, Optimize, and Deploy" Register with my discount code 'KIRK40' to get 𝟰𝟬% 𝗢𝗙𝗙 and see all the amazing benefits of the workshop here: eventbrite.com/e/ship-product… (Time: 3 hours on March 29)