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15847279
Machine Learning Pocket Reference: Working with Structured Data in Python
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Stavka #:
15847279
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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.
Detalji o proizvodu
- 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?
-
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.
-
Beginners
May be overwhelming for those with no prior knowledge of programming or machine learning concepts and techniques.
OPIS PROIZVODA
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.
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Pros
- 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
Protiv
- Some graphs are difficult to read and understand
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