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Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) 1st Edition
90% of respondents would recommend this to a friend
USD 38
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Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use.
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What Stands Out
Detalles de producto
- Comprehensive guide to understanding and building machine learning systems with Python
- Suitable for absolute beginners with basic Python coding skills and minimal college-level math knowledge
- Communicates machine learning ideas through plain-English stories, pictures, and Python examples
- Covers key mathematical and computational topics in an approachable manner
- Discusses building, training, and evaluating learning systems, and explores advanced techniques
- Includes practical insights applicable to various learning systems, and leverages Python scikit-learn library and other powerful tools
| Package Weight | 2.1000 Pound |
Who Should Buy?
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Beginners in ML
Ideal for newcomers to machine learning, providing foundational concepts and easy-to-understand examples.
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Python Enthusiasts
Beneficial for Python developers looking to understand machine learning applications and implementations within the language.
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Data Analysts
Great for data analysts wanting to expand their skill set with machine learning techniques and practical examples.
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Advanced Practitioners
Not suitable for experts seeking advanced algorithms or in-depth theoretical discussions on machine learning.
DESCRIPCIÓN DEL PRODUCTO
Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) 1st Edition
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Intelligence & Semantics Editorial Review
**** "Machine Learning with Python for Everyone" by Dr. Mark Fenner has been positively received by a diverse audience, particularly those with little formal training in machine learning. The book is crafted with clarity and accessibility, making complex topics more palatable for beginners. Dr. Fenner acknowledges the common hurdles that newcomers face in the realm of machine learning, especially when grappling with mathematical concepts. He intentionally simplifies the approach by using storytelling, graphical illustrations, and practical Python code, rather than overwhelming readers with heavy mathematics. While the book covers various aspects of developing predictive models, it's not structured as a comprehensive tutorial on setting up a development environment. It assumes that readers come with a fundamental knowledge of Python and are comfortable with basic charts and graphs. Some feedback points to a desire for a hands-on workbook that enables readers to run examples directly, which might limit its immediate applicability for those looking for a plug-and-play experience. Readers appreciate Dr. Fenner's informal writing style and the engaging manner in which he presents the material. Many commend the book for its practical examples, which allow readers to see real-world applications of the concepts discussed. There’s a Consensus among reviewers that the content is relevant and up to date, providing valuable insights into machine learning. Overall, "Machine Learning with Python for Everyone" has emerged as a commendable resource for individuals starting their journey in machine learning, particularly for those preferring an approachable introduction rather than an arithmetic-heavy discourse. **
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ventajas
- Clear and accessible writing for beginners.
- Minimizes complex mathematics while introducing concepts.
- Informal and engaging style that simplifies learning.
- Practical examples in Python make concepts relatable.
- Up-to-date information relevant to current data science scenarios.
- Responsive author engaging with readers for clarifications.
Contras
- Lacks a comprehensive tutorial on setting up a development environment.
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características y beneficios
- Master machine learning processes with Python
- Perfect for absolute beginners with minimal math knowledge
- Build, train, and evaluate learning systems step by step
- Learn core concepts, techniques, and sophisticated methods
- Applicable to every learning system for students, analysts, scientists, or hobbyists
- Includes access to downloads, updates, and corrections
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