{"product_id":"supervised-learning-mastery-crafting-accurate-predictions-learn-regression-and-classification-for-reliable-ml-outcomes-9798264275869","title":"Supervised Learning Mastery Crafting Accurate Predictions: Learn regression and classification for reliable ML outcomes","description":"\u003cp\u003e • Author(s): Isandro Myles\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Machine Theory\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMaster the art of supervised learning to make accurate predictions.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eIn \u003cb\u003eSupervised Learning Mastery\u003c\/b\u003e, you'll dive deep into \u003cb\u003eregression\u003c\/b\u003e and \u003cb\u003eclassification\u003c\/b\u003e techniques that form the backbone of machine learning. This hands-on guide teaches you how to build accurate, reliable models from labeled data-perfect for beginners and practitioners who want to create machine learning models that drive real-world outcomes.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside, you'll learn how to: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eUnderstand supervised learning\u003c\/b\u003e: the core concepts of regression and classification, and how they apply to real-world problems.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003ePrepare data for model building\u003c\/b\u003e: clean, preprocess, and transform data for regression and classification tasks.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eBuild and train \u003cb\u003eregression models\u003c\/b\u003e (linear regression, polynomial regression) for predicting continuous values such as price, sales, or demand.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eDevelop \u003cb\u003eclassification models\u003c\/b\u003e (logistic regression, decision trees, random forests, k-NN) for categorizing data into distinct classes like spam detection or customer segmentation.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eEvaluate model performance\u003c\/b\u003e using metrics like \u003cb\u003eaccuracy, precision, recall, F1 score\u003c\/b\u003e, and \u003cb\u003econfusion matrices\u003c\/b\u003e for classification tasks, and \u003cb\u003emean squared error (MSE)\u003c\/b\u003e for regression.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUnderstand \u003cb\u003ebias-variance trade-off\u003c\/b\u003e and how it impacts model performance and generalization.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUse \u003cb\u003ecross-validation\u003c\/b\u003e to assess model reliability and avoid overfitting or underfitting.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eExplore \u003cb\u003efeature engineering\u003c\/b\u003e to enhance model accuracy by creating meaningful variables from raw data.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eHyperparameter tuning\u003c\/b\u003e: optimize models using techniques like grid search and random search for better performance.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eApply \u003cb\u003eensemble methods\u003c\/b\u003e (boosting, bagging) to improve accuracy and model robustness.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eGain practical experience with real-world examples like \u003cb\u003ecustomer churn prediction\u003c\/b\u003e, \u003cb\u003ehousing price prediction\u003c\/b\u003e, and \u003cb\u003eimage classification\u003c\/b\u003e.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eLearn to use popular Python libraries like \u003cb\u003escikit-learn\u003c\/b\u003e, \u003cb\u003epandas\u003c\/b\u003e, and \u003cb\u003ematplotlib\u003c\/b\u003e for building, training, and visualizing models.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003ePacked with \u003cb\u003estep-by-step examples\u003c\/b\u003e, \u003cb\u003ehands-on exercises\u003c\/b\u003e, and \u003cb\u003ereal-world datasets\u003c\/b\u003e, this book helps you build \u003cb\u003ereliable machine learning models\u003c\/b\u003e that achieve \u003cb\u003eaccurate predictions\u003c\/b\u003e for regression and classification tasks.\u003c\/p\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eBeginners\u003c\/b\u003e to machine learning looking to understand the basics of supervised learning\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eData analysts\u003c\/b\u003e and \u003cb\u003escientists\u003c\/b\u003e who want to improve their model-building skills\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eDevelopers\u003c\/b\u003e aiming to implement regression and classification algorithms into applications\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eStudents\u003c\/b\u003e looking for a practical approach to supervised learning techniques\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eProfessionals\u003c\/b\u003e seeking to master supervised learning to advance their career in AI and machine learning\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eMaster regression and classification techniques to make reliable predictions and build powerful machine learning models.\u003c\/b\u003e\u003c\/p\u003e","brand":"Atlantic 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