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Elementas #:
104729386
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Paperback
Elementas #:
104729386
€ 71
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This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models.
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What Stands Out
Comprehensive Coverage
This book offers an in-depth exploration of time series forecasting techniques, integrating both machine learning and deep learning approaches using PyTorch and pandas, catering to industry needs.
Hands-On Approach
Readers engage in practical, hands-on projects to solidify concepts, ensuring skills are directly applicable in real-world scenarios, making it invaluable for professionals looking to enhance their forecasting prowess.
Future-Proof Skills
By focusing on modern tools and methodologies in time series analysis, the book equips readers with relevant skills necessary for evolving industry demands, thereby enhancing their career prospects in data science.
Produkto Aprašymas
| Publisher | Packt Publishing |
| Publication date | 31 Oct. 2024 |
| Edition | 2nd |
| Language | English |
| Print length | 658 pages |
| ISBN-10 | 1835883184 |
| ISBN-13 | 978-1835883181 |
| Item weight | 1.12 kg |
| Dimensions | 19.05 x 3.78 x 23.5 cm |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to enhance their skills in time series analysis with advanced machine learning techniques.
-
Machine Learning Engineers
Perfect for professionals seeking to apply deep learning methodologies to time series forecasting projects in various industries.
-
Students and Learners
Beneficial for students studying data science who want practical knowledge of time series analysis using Python, PyTorch, and pandas.
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Beginners
Not suitable for complete beginners, as prior knowledge of Python and basic statistics is typically needed to grasp concepts.
PRODUKTO APRAŠYMAS
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Paperback – 31 Oct. 2024
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Klientų klausimai ir atsakymai
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klausimas:
What prior knowledge is needed to use this book?
Answer: A basic understanding of statistics and Python programming is recommended. -
klausimas:
Is this book suitable for beginners?
Answer: Yes, it starts with fundamental concepts before progressing to advanced topics. -
klausimas:
Can I use this book for practical applications in my job?
Answer: Absolutely, it provides industry-ready techniques and strategies for real-world forecasting problems.
Higher Education Editorial Review
**** "Modern Time Series Forecasting with Python" emerges as a highly recommended resource for those delving into machine learning and deep learning applications in time series analysis. Users have expressed their appreciation for the book's layered approach to presenting complex data analysis concepts, which facilitates understanding and practical application. Its usefulness is especially noted within the financial sector, where clients have sought the author's expertise in Python for numerical analysis and tick data handling. However, some users have pointed out limitations in the book's visual presentation, particularly noting the lack of color in charts and visual aids, which may hinder comprehension for readers who rely on graphical illustrations for better understanding. Despite this, the overwhelming sentiment among users is positive, highlighting the book's effectiveness in equipping readers with the necessary knowledge and skills to deliver results in time series forecasting. Overall, "Modern Time Series Forecasting with Python" serves as a valuable tool for both beginners and experienced practitioners in the field, with its comprehensive content significantly aiding users in their time series data projects. **
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Argumentai "už"
- Layered approach makes complex concepts easier to understand.
- Highly useful for financial projects and tick data analysis.
- Strong recommendation from users as an invaluable resource.
Minusai
- Lack of colors in charts may make them hard to read.
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Savybės ir privalumai
- Learn traditional and advanced techniques for time series forecasting.
- Hands-on practical examples to improve forecasting accuracy.
- Includes free eBook with print or Kindle purchase.
- Covers deep learning models including RNNs and transformers.
- Ideal for professionals and students in various industries.
- New edition features enhancements in transformer architectures and probabilistic forecasting.
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