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A problem-focused guide for tackling industrial machine learning issues with methods and frameworks chosen by experts. The book discusses how to apply machine learning to real-world problems by utilizing real-world data, while covering popular techniques, comprehensive tools, and numerous machine learning libraries.
제품번호 #:
52821289
Applied Machine Learning Solutions with Python Production-ready ML Projects Using Cutting-edge Libraries and Powerful Statistical Techniques English
제품번호 #:
52821289
KRW 46063
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A problem-focused guide for tackling industrial machine learning issues with methods and frameworks chosen by experts. The book discusses how to apply machine learning to real-world problems by utilizing real-world data, while covering popular techniques, comprehensive tools, and numerous machine learning libraries.
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What Stands Out
Practical Projects
Offers real-world machine learning projects, ensuring practical application of concepts. This hands-on approach aids learners in developing production-ready solutions, bridging the gap between theory and real-life implementation.
Comprehensive Libraries
Utilizes cutting-edge libraries like TensorFlow and Scikit-learn, providing readers with modern tools for effective model building. This equips learners with the latest techniques and improves their competitive edge in the field.
Statistical Techniques
Incorporates powerful statistical techniques, enhancing understanding of underlying data processes. This foundational knowledge empowers users to make informed decisions and optimize their machine learning models effectively.
제품 세부 정보
- Addresses industrial machine learning issues with expert-chosen methods and frameworks
- Covers popular techniques for problem formulation, data collection, and data cleaning
- Includes useful machine learning tools like MLFlow and Streamlit
- Features machine learning libraries such as Tensorflow, FastAI, Scikit-Learn, Pandas, and Numpy
- Discusses real-world machine learning application with data investigation, pipelines, and examples
- Recommended for beginners in machine learning, software engineers, and students desiring production-ready ML systems
| Publisher | BPB Publications |
| Publication date | September 1, 2021 |
| Language | English |
| Print length | 356 pages |
| ISBN-10 | 9391030432 |
| ISBN-13 | 978-9391030438 |
| Item Weight | 1.36 pounds (620 grams) |
| Dimensions | 7.5 x 0.81 x 9.25 inches (19.1 x 2.1 x 23.5 cm) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for beginners looking to build practical machine learning skills through hands-on projects and guided examples.
-
Intermediate Python Users
Great for users with Python knowledge who want to expand into machine learning with practical applications.
-
Industry Professionals
Beneficial for professionals in data-dependent fields who need to implement machine learning solutions effectively.
-
Complete Beginners
Not suitable for those who lack basic programming and machine learning knowledge as it assumes prior experience.
제품 설명
Applied Machine Learning Solutions with Python Production-ready ML Projects Using Cutting-edge Libraries and Powerful Statistical Techniques English Edition
질문이 있으십니까? 채팅하기
고객 질문 및 답변
-
의문:
What is 'Applied Machine Learning Solutions with Python' about?
답변: This book serves as a comprehensive guide to implementing machine learning projects using Python. It covers practical applications by leveraging cutting-edge libraries like TensorFlow and scikit-learn. Readers will learn to develop production-ready solutions that address real-world data challenges. This is particularly beneficial for software engineers, data scientists, and even enthusiasts looking to enhance their skills with practical, project-based knowledge. -
의문:
Who is the target audience for this book?
답변: The book is suited for a range of audiences, including data scientists, software engineers, and machine learning practitioners. It is ideal for individuals who have a basic understanding of Python and wish to dive deeper into applied machine learning. By focusing on practical applications and projects, it caters to both professionals seeking to polish their skills and students aiming for hands-on experience in real-world scenarios. -
의문:
What libraries and tools are covered in the book?
답변: The book introduces powerful libraries such as TensorFlow, Keras, and scikit-learn, among others. These libraries provide robust frameworks for building machine learning models efficiently. Additionally, readers will explore tools for data preprocessing, model evaluation, and deployment strategies to ensure their projects are not only functional but also production-ready, making the learning process highly applicable. -
의문:
Can beginners understand the concepts presented in this book?
답변: While the book is tailored for those with a foundational knowledge of Python, it is written in a manner that is accessible to motivated beginners. It gradually introduces complex concepts and solidifies understanding with practical projects. This hands-on approach encourages readers to experiment and apply what they learn, making it a suitable stepping stone for anyone eager to delve into machine learning. -
의문:
What types of projects can I expect to find in the book?
답변: The book includes a variety of projects that cater to different machine learning applications, such as classification, regression, and clustering tasks. Each project is designed to tackle specific challenges, allowing readers to apply theoretical knowledge to tangible data problems. This project-oriented format not only solidifies learning but also builds a diverse portfolio, showcasing practical skills to potential employers. -
의문:
Does the book address model deployment techniques?
답변: Yes, one of the key focuses of the book is on deploying machine learning models into production. It discusses best practices for integrating models into applications and how to maintain and update them post-deployment. This is crucial for anyone looking to convert their data science projects into robust, real-world applications that can be utilized by businesses and consumers alike. -
의문:
What statistical techniques are highlighted in the book?
답변: The book emphasizes various powerful statistical techniques, including regression analysis, hypothesis testing, and Bayesian methods. These techniques are fundamental to understanding the underlying data characteristics and creating models that provide meaningful insights. By integrating these statistical methods with machine learning practices, readers can develop more accurate predictive models that are grounded in sound analysis. -
의문:
Is there a digital version of this book available?
답변: Yes, the book is often available in both digital and print formats. The digital version can be a convenient option for readers who prefer to access the content through e-readers or tablets. This format also facilitates easy searching and highlighting, enhancing the learning experience as readers can quickly refer back to key concepts and examples during their project work. -
의문:
How can I apply the knowledge from this book in my career?
답변: Applying the knowledge gained from this book can significantly enhance your career prospects in various technology-driven fields. The practical skills developed through the projects make you more valuable to employers who seek candidates proficient in machine learning and data analysis. This knowledge can lead to roles such as data scientist, machine learning engineer, or business analyst, where you'll drive data-driven decision-making. -
의문:
Where can I buy 'Applied Machine Learning Solutions with Python'?
답변: You can purchase 'Applied Machine Learning Solutions with Python: Production-ready ML Projects Using Cutting-edge Libraries and Powerful Statistical Techniques' from Ubuy in South Korea. Ubuy offers a reliable platform to acquire this informative book, allowing you to explore the cutting-edge insights of applied machine learning effectively.
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장점
- Comprehensive coverage of ML techniques
- Practical, production-ready projects
- Utilizes cutting-edge libraries
- Excellent for intermediate learners
- Hands-on approach enhances understanding
단점
- Occasional complex explanations
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KRW 46063
지금 주문하시면 도착 예정일 토요일, 7월 25
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특징 및 장점
- Real-world problem solving using machine learning with real-world data
- Popular techniques, comprehensive tools, numerous ML libraries covered
- Problem formulation, data collection, data cleaning, model building, fine-tuning, deployment guidelines
- Use cases for data analytics, computer vision, natural language processing
- Suitable for beginners in ML, software engineers, and students
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