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Python for Machine Learning: Implement ML Models with Scikit-Learn

by Thompson Carter
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Current price ₹1,766.00
Original price ₹1,960.00
Original price ₹1,960.00
Original price ₹1,960.00
(-10%)
₹1,766.00
Current price ₹1,766.00

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Book cover type: Paperback
  • ISBN13: 9798303523296
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 218
  • Original Price: USD 19.99
  • Language: English
  • Edition: N/A
  • Item Weight: 300 grams
  • BISAC Subject(s): Languages / Python

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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