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Python for Machine Learning: Implement ML Models with Scikit-Learn
| AUTHOR | Carter, Thompson |
| PUBLISHER | Independently Published (12/14/2024) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
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.
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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Product Format
Product Details
ISBN-13:
9798303523296
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
218
Carton Quantity:
36
Product Dimensions:
6.00 x 0.46 x 9.00 inches
Weight:
0.66 pound(s)
Country of Origin:
US
Subject Information
BISAC Categories
Computers | Languages - Python
Descriptions, Reviews, Etc.
publisher marketing
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.
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.
Show More