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15847279
Machine Learning Pocket Reference: Working with Structured Data in Python 1st Edition
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
| Item Weight | 1.5 lbs (680 grams) |
Who Should Buy?
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Data Scientists
Provides concise guidance on handling structured data, quick reference for core machine learning concepts and Python applications.
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Students
Ideal for learners seeking a compact resource to assist with machine learning coursework and practical exercises in Python.
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Developers
Great for software developers looking to incorporate machine learning into their applications without deep theoretical knowledge.
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Beginners
May be overwhelming for those with no prior knowledge of programming or machine learning concepts and techniques.
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Theoretical Researchers
Focuses on practical applications and may lack the depth needed for advanced theoretical machine learning studies.
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Non-Python Users
Unsuitable for individuals not using Python or those requiring resources for different programming languages in machine learning.
OPIS PROIZVODA
Machine Learning Pocket Reference: Working with Structured Data in Python 1st Edition
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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Pitanja i odgovori kupaca
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pitanje:
Who is the target audience for this book?
odgovor: This book is ideal for programmers, data scientists, and AI engineers. -
pitanje:
What topics are covered in this book?
odgovor: This book covers classification, cleaning data, exploratory data analysis, preprocessing steps, model selection, regression, clustering, dimensionality reduction, and scikit-learn pipelines. -
pitanje:
Is this book suitable for beginners?
odgovor: Yes, this book is suitable for beginners as it provides a detailed overview of the machine learning process and walks readers through various topics.
Intelligence & Semantics Editorial Review
Machine Learning Pocket Reference: Working with Structured Data in Python 1st Edition offers a valuable compendium for individuals already familiar with machine learning and seeking a comprehensive reference guide. The book's emphasis on practical implications and examples makes it a handy tool for data science projects. It provides concise segments on individual topics, facilitating quick access to information and example code for processing structured data. Additionally, it introduces readers to various Python libraries commonly used in data science, such as Yellowbrick and Shapley. The reference offers an overview of classic ML techniques, including data cleansing, quality metrics, and visualization. Nevertheless, some readers have expressed dissatisfaction with the book's production quality, citing unreadable graphs and concerns about the binding. Despite being a valuable companion for experienced individuals working with smaller datasets, the reference does not offer in-depth academic insights into ML techniques, and it is not intended to serve as a primary learning resource for beginners in the field.
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Pros
- Valuable as a quick reference for individuals with foundational data science/ML knowledge and some Python proficiency
- Offers concise code samples and practical examples for traditional classification and regression problems
- Introduces readers to various Python libraries commonly used in the data science field
- Well segmented into individual topics, making it easy to locate specific information
Protiv
- Unreadable graphs and concerns about the binding have been noted
Product Price History
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