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Item #:
15847279
Machine Learning Pocket Reference: Working with Structured Data in Python 1st Edition
Item #:
15847279
NZD 65
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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.
Product Details
- 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?
-
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.
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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.
Product Description
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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Product Buying Guide
The Machine Learning Pocket Reference: Working with Structured Data in Python 1st Edition is a valuable resource for programmers, data scientists, and AI engineers. Written by author Matt Harrison, this book provides detailed notes, tables, and examples to help you navigate the basics of structured machine learning. Whether you are a beginner or an experienced professional, this pocket reference will serve as a handy guide during training and as a convenient resource for your next machine learning project.
Product Specifications
- Author: Matt Harrison
- Edition: 1st
- Language: English
- Format: Paperback
- Number of Pages: Varies by edition
Key Features
- Overview of the machine learning process
- Classification with structured data
- Cleaning and dealing with missing data
- Exploratory data analysis
- Preprocessing steps for feature selection
- Model selection and evaluation
- Regression examples with various algorithms
- Clustering and dimensionality reduction
- Usage of scikit-learn pipelines
Usage Scenarios
- Training resource for programmers and data scientists
- Guide for understanding machine learning concepts
- Reference for building and implementing machine learning models
- Supportive material for AI engineers during project development
Usage Scenarios
- Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
- Python Machine Learning by Sebastian Raschka and Vahid Mirjalili
- Pattern Recognition and Machine Learning by Christopher Bishop
Some User Review
- This pocket guide is a gem! It provides clear explanations, practical examples, and helpful references. It's a must-have for anyone working with structured data in machine learning.
- I found the book to be concise and well-organized. It covers important topics with sufficient detail. The examples provided are easy to follow and implement in Python.
- As a beginner in machine learning, I appreciate the format of this reference guide. It breaks down complex concepts into manageable sections and provides step-by-step explanations.
Competitors
- The price of the Machine Learning Pocket Reference may vary depending on the edition and the retailer. It is recommended to compare prices from different sources to find the best deal.
Buying Considerations
- Consider your level of experience in machine learning. This pocket reference is suitable for both beginners and experienced professionals, but some prior understanding of the topic is beneficial.
- Think about your specific needs and goals in machine learning. This book covers a range of topics, so ensure it aligns with your objectives.
- Check for discounts or bundle offers when purchasing this pocket reference to save money and get additional resources.
- Read user reviews and ratings to gather insights from other readers about the usefulness and quality of this reference guide.
Conclusion
The Machine Learning Pocket Reference is a valuable tool for programmers, data scientists, and AI engineers working with structured data in Python. With its comprehensive coverage of machine learning concepts, practical examples, and easy-to-follow explanations, it serves as an essential resource for both beginners and experienced professionals. Consider your specific needs and goals in machine learning when purchasing this pocket reference.
View LessThe Machine Learning Pocket Reference: Working with Structured Data in Python 1st Edition is a valuable resource for programmers, data scientists, and AI engineers. Written by author Matt Harrison, this book provides detailed notes, tables, and examples to help you navigate the basics of structured machine learning. Whether you are a beginner or an experienced professional, this pocket reference will serve as a handy guide during training and as a convenient resource for your next machine learning project. Continue Reading
Customer Questions & Answers
-
Question:
Who is the target audience for this book?
Answer: This book is ideal for programmers, data scientists, and AI engineers. -
Question:
What topics are covered in this book?
Answer: This book covers classification, cleaning data, exploratory data analysis, preprocessing steps, model selection, regression, clustering, dimensionality reduction, and scikit-learn pipelines. -
Question:
Is this book suitable for beginners?
Answer: 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
Cons
- Unreadable graphs and concerns about the binding have been noted
Product Price History
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NZD 65
This product is not Fulfilled by Ubuy and can take minimum 10 days in delivery. We might cancel the product from the order and refund you if any issue arise with the delivery of this product.
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