Introduction to Machine Learning with Python : A Guide for Data Scientists
Andreas C Müller

Introduction to Machine Learning with Python : A Guide for Data Scientists

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Key item features

  • Practical Learning: Teaches practical methods for building machine learning solutions using Python, even for beginners, focusing on application over mathematical theory.
  • Core Libraries: Utilizes the scikit-learn library for machine learning application development, with familiarity with NumPy and matplotlib being beneficial.
  • Key Concepts: Covers fundamental machine learning concepts, advantages and shortcomings of algorithms, and effective data representation.
  • Advanced Techniques: Explores advanced methods for model evaluation, parameter tuning, and the concept of pipelines for workflow encapsulation.
  • Text Data: Includes methods for working with text data, incorporating text-specific processing techniques.
  • Skill Enhancement: Offers suggestions for improving machine learning and data science skills.
  • Publication Details: A paperback guide published on November 15, 2016, with 398 pages, authored by Andreas C Mller.
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