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Getting Started with Natural Language Processing
88% of respondents would recommend this to a friend
ARS 150123
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What Stands Out
Detalles de producto
- An in-depth introduction to NLP fundamental concepts and algorithms
- Learn about useful Python libraries for NLP and hands-on projects
- Techniques for building a search algorithm and extracting information from raw text
- Practical applications like predicting sentiment, author profiling, and topic labeling
- Authored by Dr. Ekaterina Kochmar, a lecturer at the University of Bath
- Suitable for readers with basic Python skills and no prior NLP experience
| Publisher | Manning |
| Publication date | October 18, 2022 |
| Edition | 1st |
| Language | English |
| Print length | 456 pages |
| ISBN-10 | 1617296767 |
| ISBN-13 | 978-1617296765 |
| Item Weight | 1.45 pounds (660 grams) |
| Dimensions | 7.38 x 1.1 x 9.25 inches (18.7 x 2.8 x 23.5 cm) |
Who Should Buy?
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Beginner Programmers
Ideal for those new to programming, providing foundational concepts and hands-on examples in natural language processing.
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Data Science Students
Perfect for students pursuing data science who want to add NLP skills to their toolkit and enhance their studies.
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Research Enthusiasts
Useful for researchers interested in exploring NLP applications and improving their understanding of language models.
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Advanced Practitioners
Not suited for experienced professionals seeking in-depth technical insights or advanced NLP techniques beyond beginner level.
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Natural Language Processing Editorial Review
**** "Getting Started with Natural Language Processing" is an engaging and informative book that caters to individuals new to the field of NLP as well as those seeking a deeper understanding of the subject. Despite some criticisms regarding print quality, where much of the text appears in shades of grey, the content itself is richly Constructed. Readers praise the author, Prof. Ekaterina Kochmar, for her clear explanations and practical approach to complex concepts, which illuminate both the theoretical and applied aspects of natural language processing. The book’s structure—with task-driven chapters—provides a logical framework for readers, allowing them to grasp the material incrementally. The inclusion of hands-on examples ranging from Spam and Ham classification to Sentimental Analysis and Topic Analysis adds practicality to the theoretical concepts discussed. Furthermore, the text delves into related machine learning principles with the assistance of Scikit-learn, further enriching the learning experience. Users have found themselves revisiting the book and discovering new insights, often referred to as "aha moments," indicating that the author's teaching style resonates well with learners. This book is a valuable resource for anyone looking to start their journey in natural language processing or deepen their existing knowledge. **
Customer Reviews & Ratings
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5 estrella
63%
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4 estrella
11%
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3 estrella
26%
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2 estrella
0%
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1 estrella
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ventajas
- Clear and well-Constructed writing
- Practical, hands-on examples relevant to real-world tasks
- Task-wise chapter organization aids understanding
- Detailed explanation of both NLP and associated machine learning concepts
- Engaging for readers of varying expertise levels
Contras
- Low print quality with much text in shades of grey, affecting readability
Product Price History
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