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Synopsis
Unlock the power of Machine Learning with this comprehensive, hands-on guide that transforms complex ML concepts into practical solutions. Whether you're a data scientist, developer, or ML enthusiast, this book delivers battle-tested strategies for implementing production-ready ML models using Python and scikit-learn.
What You'll Master
From data preprocessing to model deployment, discover how to build robust ML pipelines that solve real-world problems. Dive deep into classification, regression, clustering, and dimensionality reduction techniques while working with real datasets that matter.
Practical Focus
No more theoretical jargon - learn through hands-on projects, including sentiment analysis, customer segmentation, and predictive maintenance. Each chapter builds your expertise with industry-standard practices and optimization techniques.
Perfect For
• Python developers ready to level up their ML skills
• Data analysts transitioning to machine learning
• Students seeking practical ML implementation skills
Key Features
Modern Techniques
Master the latest scikit-learn features, including pipeline optimization, automated ML workflows, and model evaluation strategies. Learn to fine-tune hyperparameters and build ensemble models that outperform traditional approaches.
Real-World Applications
Transform raw data into valuable insights using production-ready code. Implement advanced techniques for feature engineering, cross-validation, and model selection that actually work in business environments.
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Python for Machine Learning (Paperback)
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Paperback. Condition: new. Paperback. Unlock the power of Machine Learning with this comprehensive, hands-on guide that transforms complex ML concepts into practical solutions. Whether you're a data scientist, developer, or ML enthusiast, this book delivers battle-tested strategies for implementing production-ready ML models using Python and scikit-learn. What You'll MasterFrom data preprocessing to model deployment, discover how to build robust ML pipelines that solve real-world problems. Dive deep into classification, regression, clustering, and dimensionality reduction techniques while working with real datasets that matter. Practical FocusNo more theoretical jargon - learn through hands-on projects, including sentiment analysis, customer segmentation, and predictive maintenance. Each chapter builds your expertise with industry-standard practices and optimization techniques. Perfect For- Python developers ready to level up their ML skills- Data analysts transitioning to machine learning- Students seeking practical ML implementation skills Key Features Modern Techniques Master the latest scikit-learn features, including pipeline optimization, automated ML workflows, and model evaluation strategies. Learn to fine-tune hyperparameters and build ensemble models that outperform traditional approaches. Real-World Applications Transform raw data into valuable insights using production-ready code. Implement advanced techniques for feature engineering, cross-validation, and model selection that actually work in business environments. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798303523296
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Carter, T: Python for Machine Learning
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Taschenbuch. Condition: Neu. Neuware - Unlock the power of Machine Learning with this comprehensive, hands-on guide that transforms complex ML concepts into practical solutions. Whether you're a data scientist, developer, or ML enthusiast, this book delivers battle-tested strategies for implementing production-ready ML models using Python and scikit-learn. Seller Inventory # 9798303523296
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Python for Machine Learning (Paperback)
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Paperback. Condition: new. Paperback. Unlock the power of Machine Learning with this comprehensive, hands-on guide that transforms complex ML concepts into practical solutions. Whether you're a data scientist, developer, or ML enthusiast, this book delivers battle-tested strategies for implementing production-ready ML models using Python and scikit-learn. What You'll MasterFrom data preprocessing to model deployment, discover how to build robust ML pipelines that solve real-world problems. Dive deep into classification, regression, clustering, and dimensionality reduction techniques while working with real datasets that matter. Practical FocusNo more theoretical jargon - learn through hands-on projects, including sentiment analysis, customer segmentation, and predictive maintenance. Each chapter builds your expertise with industry-standard practices and optimization techniques. Perfect For- Python developers ready to level up their ML skills- Data analysts transitioning to machine learning- Students seeking practical ML implementation skills Key Features Modern Techniques Master the latest scikit-learn features, including pipeline optimization, automated ML workflows, and model evaluation strategies. Learn to fine-tune hyperparameters and build ensemble models that outperform traditional approaches. Real-World Applications Transform raw data into valuable insights using production-ready code. Implement advanced techniques for feature engineering, cross-validation, and model selection that actually work in business environments. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798303523296
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