Kirk Borne

💥Scikit-learn Cookbook — 80+ recipes for Machine Learning in Python with scikit-learn [3rd Edition]: http://amzn.to/4oDGOq7 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼: 🔹Common Conventions & API Elements of Scikit-Learn 🔹Pre-Model Workflow and Data Preprocessing 🔹Dimensionality Reduction Techniques 🔹Build Models with Distance Metrics & Nearest Neighbors 🔹Linear Models and Regularization 🔹Advanced Logistic Regression and Extensions 🔹Support Vector Machines and Kernel Methods 🔹Tree-Based Algorithms and Ensemble Methods 🔹Text Processing and Multiclass Classification 🔹Clustering Techniques 🔹Novelty and Outlier Detection 🔹Cross-Validation and Model Evaluation Techniques 🔹Deploying Scikit-Learn Models in Production My Review (on Amazon): I love this book, not only for its amazing content, but also for its brilliant adherence to its "Cookbook" label. The table of contents looks repetitive, but that's the magic of it. Find the algorithm, method, or application that you want "to cook up," and the presentation follows a uniform format from chapter to chapter. This is an extraordinarily practical, useful, and reader-friendly style, perfect for the target audience: anyone who needs to use (or is discovering how to use) specific Scikit-learn tools for their Python-based machine learning tasks. Consequently, this is great not only for individual use but also for training workshops and other educational settings. Python code in a cookbook context has never looked so good. IMHO.