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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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Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models.
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
Item #: 99291537

Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

87% of respondents would recommend this to a friend

Item #: 99291537

BZD 126

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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 Learning
Covers both machine learning and deep learning techniques, offering a deep dive into time series analysis, ensuring learners acquire industry-relevant skills for real-world applications.
Hands-on Approach
Utilizes PyTorch and pandas for practical coding examples, empowering readers to implement learned concepts effectively, making it suitable for both beginners and seasoned practitioners.
Updated Content
The 2nd edition includes the latest advancements in technology and methodologies, providing readers with current insights that enhance their understanding and execution of modern forecasting techniques.

Product Details

Shop Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas online at a best price in Belize. B0D6G3SHD6
Publisher Packt Publishing
Publication date October 31, 2024
Edition 2nd ed.
Language English
Print length 658 pages
ISBN-10 1835883184
ISBN-13 978-1835883181
Item Weight 2.46 pounds (1.12 kg)
Dimensions 7.5 x 1.49 x 9.25 inches (19.1 x 3.8 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists wanting to enhance their time series analysis skills using Python frameworks like PyTorch.

  • Machine Learning Engineers

    Suitable for ML engineers seeking industry-ready techniques to implement sophisticated time series forecasting models.

  • Students & Researchers

    Beneficial for students and researchers studying time series analysis with practical, hands-on examples in Python.

Not Suitable For
  • Beginners

    Not suitable for absolute beginners without prior knowledge of Python, machine learning concepts, or time series basics.

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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Customer Questions & Answers

  • Question: What topics are covered in the book 'Modern Time Series Forecasting with Python'?

    Answer: The book covers a wide range of topics including foundational concepts of time series analysis, various machine learning algorithms, and deep learning techniques applied to forecasting using Python, PyTorch, and pandas. Each chapter dives into practical applications, making it ideal for both beginners and advanced practitioners. The hands-on approach facilitates a thorough understanding of model development, evaluation, and deployment. For instance, you will learn how to apply LSTM networks to predict stock prices or analyze seasonal trends in sales data.
  • Question: Who is the target audience for this book?

    Answer: This book is tailored for data scientists, analysts, and developers who possess a foundational knowledge of Python and want to delve into time series forecasting. It's also great for business professionals looking to leverage data-driven decision-making through predictive analytics. With practical examples and accessible explanations, individuals seeking to enhance their skills in machine learning and deep learning will find it particularly beneficial. For instance, a marketing analyst can use the techniques outlined to predict sales trends based on historical customer behavior.
  • Question: Does the book provide hands-on projects?

    Answer: Yes, the book includes several hands-on projects that allow readers to apply what they've learned in practical scenarios. Each project is designed to reinforce the concepts of time series forecasting and machine learning techniques using real-world datasets. This is crucial for developing a deeper understanding of the subject. For example, you might work on a project that involves predicting the weather or stock market fluctuations, giving you direct experience with the tools and methods discussed in the book.
  • Question: Is prior knowledge of data science required to understand the content?

    Answer: While a basic understanding of Python programming and data analysis is beneficial, the book is structured to cater to various skill levels. It starts with the foundational principles of time series analysis and gradually progresses to complex topics. This ensures that readers with limited experience can grasp the essential concepts and apply them effectively. For instance, if you have worked with data in simple projects before, you will be able to adapt and implement advanced techniques showcased in the book.
  • Question: What are some practical applications of time series forecasting discussed in the book?

    Answer: The book explores multiple practical applications of time series forecasting across various industries. Examples include stock price predictions, energy consumption forecasting, sales forecasts, and demand planning for inventory management. By learning the methodologies, readers can implement solutions in their respective fields to optimize operations and inform strategies. For example, retail businesses can use forecasting to manage inventory levels during peak seasons effectively.
  • Question: What programming libraries will I learn to use in this book?

    Answer: This book emphasizes the use of popular Python libraries such as pandas for data manipulation, PyTorch for deep learning applications, and various machine learning frameworks. You will learn how to leverage these libraries to analyze time series data effectively and develop predictive models. By mastering these tools, you will be equipped to handle different datasets and create robust forecasting systems. For instance, using pandas, you can easily preprocess your data, while PyTorch will allow you to build and train complex neural networks.
  • Question: Can this book help me prepare for a data science interview?

    Answer: Absolutely! The book prepares you with key concepts and practical skills that are often relevant in data science interviews, especially those focusing on time series analysis and forecasting. It provides a comprehensive understanding of both theoretical and practical aspects, enabling you to discuss relevant projects and techniques confidently. For instance, you could explain your approach to building a forecasting model using LSTM networks, showcasing your technical knowledge and hands-on experience.
  • Question: What is the difference between machine learning and deep learning in the context of time series forecasting?

    Answer: Machine learning refers to traditional algorithms, while deep learning involves neural networks that can capture complex patterns in data. In time series forecasting, machine learning techniques such as ARIMA might be used for simpler datasets, whereas deep learning models like LSTMs can manage large datasets with intricate patterns. The book explores both methodologies, demonstrating when to apply each technique effectively. This is particularly useful when deciding how to approach forecasting tasks in fields like finance or climate science.
  • Question: Are there any prerequisites for this book?

    Answer: While there are no strict prerequisites, a fundamental understanding of Python and basic statistical concepts is recommended for optimal comprehension. Familiarity with data analysis concepts will also be advantageous. This prior knowledge will facilitate your learning process as you engage with the tools and methods introduced throughout the book. For example, if you already have experience with Python libraries like NumPy and pandas, you will find it easier to follow along with the practical examples provided.
  • Question: Where can I buy 'Modern Time Series Forecasting with Python' in Belize?

    Answer: You can purchase 'Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas 2nd ed.' from Ubuy in Belize. Ubuy offers an extensive selection of books and other products, ensuring a smooth shopping experience. You can easily navigate their platform to find this title and other related resources to support your learning in time series analysis and forecasting.

Machine Theory Editorial Review

**Editorial Review of "Modern Time Series Forecasting with Python"** "Modern Time Series Forecasting with Python" emerges as a vital resource for professionals and enthusiasts keen on mastering time series analysis and forecasting through modern programming tools. The second edition skillfully integrates contemporary machine learning and deep learning methods with traditional statistical techniques, making it an invaluable guide for practitioners in financial analytics, machine learning competitions, and other data-driven sectors. Readers have responded positively to the book's pedagogical approach, noting its layered presentation of information that simplifies complex topics. Several reviews highlighted the clear explanations paired with practical examples, allowing even those with some experience in Python and tools like Scikit-learn and PyTorch to navigate the material effectively. While the content is deemed advanced and not suitable for complete beginners, users with a foundation in programming can glean substantial insights and techniques from the text. The book’s structured build-up from foundational concepts to more intricate methods has also been praised. It provides readers with an array of applicable skills across diverse forecasting applications, from financial forecasting to demand predictions and even fraud detection. Many users reported that the book's methods made a significant impact on their real-world forecasting abilities and analytical skills, underscoring its relevance across various industries. In summary, "Modern Time Series Forecasting with Python" is positioned as a comprehensive and engaging resource, recognized for its clarity and practical focus. It promises to assist data scientists and engineers in addressing actual forecasting challenges, making it a valuable addition to any data-oriented professional's library. **

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Pros

  • Well-structured, layered presentation of topics
  • Clear explanations make complex concepts accessible
  • Integrates practical examples with theoretical foundations
  • Covers both statistical and machine learning techniques
  • Particularly useful for professionals in finance and analytics

Cons

  • Not suitable for complete beginners

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BZD 126

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Features & Benefits

  • 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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