Python for Machine Learning: Implement ML Models with Scikit-Learn - Paperback
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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.
Python for Machine Learning: Implement ML Models with Scikit-Learn - Paperback
$26.98
Sale price
$26.98
Regular price
by Thompson Carter (Author)
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 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.
Number of Pages: 218
Dimensions: 0.46 x 9 x 6 IN
Publication Date: December 14, 2024