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Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
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What Stands Out
Product Details
| 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?
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Data Scientists
Ideal for data scientists looking to enhance their skills in time series analysis with advanced machine learning techniques.
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Machine Learning Engineers
Perfect for professionals seeking to apply deep learning methodologies to time series forecasting projects in various industries.
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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.
Product Description
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
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Higher Education Editorial Review
Modern Time Series Forecasting With Python Industry-ready Machine Learning And Deep Learning Time Series Analysis With PyTorch And Pandas is an essential resource for anyone looking to dive into time series analysis. With 658 pages of comprehensive content published by Packt Publishing, this 2nd edition provides in-depth knowledge suitable for both beginners and experienced practitioners. The inclusion of PyTorch and Pandas enhances the application of machine learning and deep learning techniques in real-world scenarios, enabling users to effectively handle time series data. This guide is not only informative but also structured to facilitate learning through practical examples and thorough explanations.
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Pros
- Comprehensive coverage of time series analysis techniques
- Suitable for both beginners and experienced users
- Hands-on examples with PyTorch and Pandas
- Extensive print length for detailed exploration
- Latest edition includes updated methodologies
Cons
- A bit heavy for casual reading
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