Applied Machine Learning : Solving Real-World Problems with Regression and Classification
Language: English
Published by Amazon Digital Services LLC - Kdp Jul 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 31.79
Quantity: 2 available
Add to basketItem description from seller
Neuware - Data is everywhere, but turning it into useful predictions is where the real work begins.Applied Machine Learning takes you through the complete process of building practical predictive solutions-from understanding a business question and preparing messy information to training, evaluating, improving, and presenting a reliable model.Instead of focusing only on theory, this guide shows you how each decision affects the final result.Inside, you'll discover how to: - Define a clear prediction objective- Clean missing, inconsistent, and unusual values- Prepare numerical and categorical features- Choose suitable algorithms for continuous and categorical outcomes- Split datasets without creating information leakage- Compare models using meaningful evaluation metrics- Handle overfitting, class imbalance, and weak features- Tune performance without making the process unnecessarily complicated- Explain results to technical and nontechnical audiences- Turn experiments into repeatable workflowsYou'll explore practical examples such as estimating prices, predicting customer behaviour, identifying risk, and assigning observations to useful groups.The explanations are conversational, beginner-friendly, and focused on helping you understand why each step matters. You won't simply copy code and accept the output. You'll learn how to question the data, interpret results, recognize misleading performance, and decide whether a model is truly useful.Whether you're a student, analyst, programmer, researcher, or professional entering data science, this book will help you move from raw information to dependable predictions with greater confidence.…
Seller Inventory # 9798188639730
- Title
- Applied Machine Learning : Solving Real-World Problems with Regression and Classification
- Author
- Corin Halstead
- Publisher
- Amazon Digital Services LLC - Kdp Jul 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798188639730
- Item weight
- 227 grams
- Dimensions
- 229x152x12 mm
Data is everywhere, but turning it into useful predictions is where the real work begins.
Applied Machine Learning takes you through the complete process of building practical predictive solutions—from understanding a business question and preparing messy information to training, evaluating, improving, and presenting a reliable model.
Instead of focusing only on theory, this guide shows you how each decision affects the final result.
Inside, you’ll discover how to:
• Define a clear prediction objective
• Clean missing, inconsistent, and unusual values
• Prepare numerical and categorical features
• Choose suitable algorithms for continuous and categorical outcomes
• Split datasets without creating information leakage
• Compare models using meaningful evaluation metrics
• Handle overfitting, class imbalance, and weak features
• Tune performance without making the process unnecessarily complicated
• Explain results to technical and nontechnical audiences
• Turn experiments into repeatable workflows
You’ll explore practical examples such as estimating prices, predicting customer behaviour, identifying risk, and assigning observations to useful groups.
The explanations are conversational, beginner-friendly, and focused on helping you understand why each step matters. You won’t simply copy code and accept the output. You’ll learn how to question the data, interpret results, recognize misleading performance, and decide whether a model is truly useful.
Whether you’re a student, analyst, programmer, researcher, or professional entering data science, this book will help you move from raw information to dependable predictions with greater confidence.
"Synopsis" may belong to another edition of this title.
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|---|---|---|
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