- Home Page /
- Japanese Books /
- Science & Technology /
- Electricity & Communications /
- Start Machine Learning with Python ―Learn Feature Engin...
Item #:
87921355
Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017
Item #:
87921355
SAR 132
Price Details
Excluding Shipping & Custom charges ( Shipping and custom charges will be calculated on checkout )
*All items will import from JP
0 ratings
Write a review
In stock
Imported from Japan store
QTY:
Order now and get it around Sunday, June 28
Secured transaction
Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
Our Top Logistics Partners
Start your journey into machine learning with step-by-step instructions from an expert on the classic scikit-learn library.
Buy Now Pay Later
Fast
Shipping
Free
Return*
Secure Packaging
100% Original Products
PCI DSS Compliance
ISO 27001 Certified
Note: Step Down Voltage Transformer required for using electronics products of JAPAN store (100 V). Recommended power converters Buy Now.
What Stands Out
Beginner-Friendly Approach
This book offers a clear and concise introduction for beginners, making complex concepts in machine learning accessible and easy to understand with practical examples using Python and scikit-learn.
Focus on Feature Engineering
Emphasizing feature engineering, the book provides essential techniques and strategies, equipping readers with skills to enhance model performance and tackle real-world data challenges effectively.
Hands-on learning
With practical exercises and real-world projects, readers can apply their knowledge immediately, reinforcing learning and boosting confidence in applying machine learning techniques in various scenarios.
Product Details
| Publisher | u30aau30e9u30a4u30eau30fcu30b8u30e3u30d1u30f3 |
| Publication date | May 25, 2017 |
| Edition | First Edition |
| Language | Japanese |
| Print length | 373 pages |
| ISBN-10 | 4873117984 |
| ISBN-13 | 978-4873117980 |
| Item Weight | 680 g |
| Dimensions | 9.45 x 7.48 x 0.98 inches (24 x 19 x 2.5 cm) |
Who Should Buy?
-
Beginner Programmers
Ideal for those new to programming who want to grasp machine learning fundamentals using Python.
-
Data Enthusiasts
Perfect for individuals interested in exploring data science and machine learning applications through hands-on experience.
-
Self-Learners
Great for independent learners seeking structured material for understanding feature engineering and scikit-learn.
-
Advanced Users
Not suitable for experienced practitioners already familiar with machine learning concepts and scikit-learn.
-
Academic Researchers
May not meet the advanced theoretical knowledge demands typical of academic research in machine learning.
-
Busy Professionals
Not ideal for individuals with limited time who require concise, high-level machine learning over detailed tutorials.
Product Description
Start Machine Learning with Python ―Learn Feature Engineering and Machine Learning Basics with scikit-learn Tankobon Softcover – May 25, 2017
Have any Query? Chat with us
Customer Questions & Answers
-
Question:
Who is the author of this book?
Answer: The author is a seasoned expert and release manager for scikit-learn. -
Question:
What topics are covered in this book?
Answer: The book covers machine learning basics, feature engineering, and model evaluation. -
Question:
Is this book featureable for beginners?
Answer: Yes, it provides a solid foundation for individuals starting their machine learning journey.
Andreas C. Muller , Sarah Guido , 中田秀基 & 0 Electricity & Communications Editorial Review
The book, "Start Machine Learning with Python," has received positive reception from readers, particularly those who are new to machine learning and want to learn through practical examples using the scikit-learn library. Customers appreciate the way the author explains complex topics, particularly unsupervised learning and feature engineering, without heavy relationship on mathematical formulas. The book appears to be accessible yet comprehensive, covering key topics such as supervised and unsupervised learning, model evaluation, and the usage of Python code examples, which many found helpful in their learning process. Readers have noted that the practical approaches and sample codes provided through the chapters significantly enhance the learning experience. The chapter on model evaluation and improvement has been highlighted as a key strength, with many expressing that the techniques discussed are valuable for anyone facing challenges in evaluating models. Additionally, the explanations of the scikit-learn pipeline feature are praised for their usability. However, some users point out areas of improvement. Certain readers found the sections on unsupervised learning and text data handling a bit challenging, particularly if they did not have prior knowledge of these subjects. There were also comments regarding the relationship on the author's custom library, "mglearn," which some found to be too opaque, making it difficult to understand the examples fully. Additionally, the presence of the matplotlib library in sample code without sufficient background explanations left some readers confused. Overall, "Start Machine Learning with Python" is Considered a strong resource for those looking to grasp the fundamentals of machine learning, especially if they already possess some basic understanding of the subject. It is best suited for individuals who are eager to dive into practical applications with scikit-learn rather than complete beginners in programming or machine learning. **
Customer Reviews & Ratings
1 customers ratings
-
5 Star
100%
-
4 Star
0%
-
3 Star
0%
-
2 Star
0%
-
1 Star
0%
Review this product
Share your thoughts with other customers
Pros
- Clear explanations of complex topics, especially in unsupervised learning.
- Practical examples and Python code using scikit-learn.
- Strong focus on model evaluation and improvement.
- Useful information on scikit-learn's pipeline feature.
Cons
- Some chapters may be challenging for absolute beginners without prior knowledge.
Product Price History
Important information
- Limitations : For products shipped internationally, please note that any manufacturer warranty may not be valid; manufacturer service options may not be available; product manuals, instructions, and safety warnings may not be in destination country languages; the products (and accompanying materials) may not be designed in accordance with destination country standards, specifications, and labeling requirements; and the products may not conform to destination country voltage and other electrical standards (requiring use of an adapter or converter if appropriate). The recipient is responsible for assuring that the product can be lawfully imported to the destination country. When ordering from Ubuy or its affiliates, the recipient is the importer of record and must comply with all laws and regulations of the destination country.
- Not all the products listed on Ubuy are for sale, as Ubuy is a global search engine. Products are subject to export/trade regulations.
SAR 132
Order now and get it around Sunday, June 28
This item is not restrict in my country.(Please click on above link if this item is not restrict in your country, So our team will review and allow.)
QTY:
Secured transaction
Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
Our Top Logistics Partners
Fastest cross-border delivery
Features & Benefits
- Expert guidance from a seasoned scikit-learn release manager.
- Step-by-step instructions for mastering machine learning basics.
- In-depth coverage of feature engineering and model evaluation.
- Build highly accurate predictive models with practical techniques.
- Unique features not found in conventional machine learning manuals.
- Perfect for beginners looking to establish a solid foundation.