Machine Learning Pocket Reference: Working with Structured Data in Python
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Machine Learning Pocket Reference: Working with Structured Data in Python
Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data.
Machine Learning Pocket Reference: Working with Structured Data in Python
Бр. на производ: 15847279

Machine Learning Pocket Reference: Working with Structured Data in Python

97% of respondents would recommend this to a friend

Бр. на производ: 15847279

MKD 1558

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Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data.
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What Stands Out

Concise Guidance
Offers clear, compact information on machine learning techniques, making it accessible for both beginners and seasoned practitioners seeking quick insights without wading through dense texts.
Practical Examples
Includes practical examples utilizing structured data in Python, enabling users to apply learning directly to real-world scenarios and enhance their programming skills effectively.
Targeted Audience
Designed specifically for data scientists and developers, addressing their unique challenges in machine learning, thus promoting efficient and targeted learning experiences.

Детали за производот

Discover the power of Machine Learning with our 1st Edition Machine Learning Pocket Reference. Get hands-on experience working with structured data in Python. Shop now at Ubuy North Macedonia.
  • Handy reference for navigating the basics of structured machine learning
  • Authored by Matt Harrison, ideal for programmers, data scientists, and AI engineers
  • Covers classification, cleaning data, exploratory data analysis, preprocessing steps, feature selection, and model selection
  • Includes regression examples, clustering, dimensionality reduction, and Scikit-learn pipelines
  • Provides valuable guide for additional support during training and machine learning projects
  • Contains detailed notes, tables, and examples for practical application
Publisher O'Reilly Media
Publication date October 8, 2019
Edition 1st
Language English
Print length 318 pages
ISBN-10 1492047546
ISBN-13 978-1492047544
Item Weight 2.31 pounds (1.05 kg)
Dimensions 4.5 x 0.75 x 7 inches (11.4 x 1.9 x 17.8 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Provides concise guidance on handling structured data, quick reference for core machine learning concepts and Python applications.

  • Students

    Ideal for learners seeking a compact resource to assist with machine learning coursework and practical exercises in Python.

  • Developers

    Great for software developers looking to incorporate machine learning into their applications without deep theoretical knowledge.

Not Suitable For
  • Beginners

    May be overwhelming for those with no prior knowledge of programming or machine learning concepts and techniques.

ОПИС НА ПРОИЗВОД

Machine Learning Pocket Reference: Working with Structured Data in Python

About This Item

Introducing the Machine Learning Pocket Reference: Working with Structured Data in Python, 1st Edition. Whether you're a seasoned data scientist or just starting out in Python programming, this pocket guide is your essential companion for all your machine learning needs. Structured data is the backbone of any machine learning project, and this reference book is specifically designed to help you navigate through the intricacies of working with structured data in Python. Packed with practical examples and step-by-step guidance, it will empower you to effectively analyze and manipulate your data to extract meaningful insights. This 1st Edition is tailored for Python enthusiasts of all levels.

Beginners will appreciate the clear explanations and comprehensive coverage of foundational Python concepts, while experienced programmers will find value in the advanced techniques and Python best practices discussed throughout the book. The Machine Learning Pocket Reference covers a wide range of topics, including data analysis, data visualization, Python libraries, algorithms, and machine learning techniques. It also dives into the application of Python in fields such as finance, artificial intelligence, natural language processing, and data analytics. With this pocket guide by your side, you'll have quick access to fundamental Python functions, code snippets, and helpful tips that will accelerate your productivity and streamline your workflow. The concise yet informative format makes it easy to find the information you need on the go, without overwhelming you with unnecessary details. No matter if you're developing machine learning models, building data-driven applications, or conducting research in the field of data science, the Machine Learning Pocket Reference is a must-have resource for any Python developer or data enthusiast. Don't miss out on this valuable tool for mastering structured data in Python.

Order your copy of the Machine Learning Pocket Reference today and take your machine learning skills to the next level.

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Intelligence & Semantics Editorial Review

Machine Learning Pocket Reference: Working with Structured Data in Python offers a concise yet comprehensive examination of structured data handling in machine learning projects. While it's not designed for absolute beginners, it serves as an excellent guide for individuals with foundational knowledge of Python and data science concepts. The book is segmented well, allowing readers to easily locate topics such as missing data handling and model evaluation. Despite minor issues with some graphs and binding, the accessible layout and example-driven content provide valuable insights into tools like scikit-learn, making it a handy reference for those looking to apply machine learning effectively in real-world scenarios.

Customer Reviews & Ratings

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Добрите

  • Well-structured and easy to navigate
  • Great for quick reference and reminders
  • Example-driven approach aids understanding
  • Exposes readers to numerous Python libraries
  • Compact size perfect for carrying

Конс

  • Some graphs are difficult to read and understand

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MKD 1558

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