Machine Learning for Time Series Forecasting with Python
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Machine Learning for Time Series Forecasting with Python
Machine Learning for Time Series Forecasting with Python is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
Machine Learning for Time Series Forecasting with Python
Numéro d'article: 211350451

Machine Learning for Time Series Forecasting with Python

84% des répondants recommanderaient ceci à un ami

Numéro d'article: 211350451

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Machine Learning for Time Series Forecasting with Python is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
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Ce qui se démarque

Practical Insights
Offers hands-on techniques for applying machine learning to time series data, enabling readers to grasp complex concepts quickly and implement them effectively in real-world scenarios.
Python-Centric Approach
Utilizes Python programming to provide clear examples and code snippets, making it accessible for programmers and data analysts seeking to enhance their forecasting skills with modern tools.
Comprehensive Coverage
Covers a wide range of algorithms and techniques, ensuring readers are equipped with diverse methods for tackling various time series forecasting challenges within different industries.

Détails du produit

Learn time series forecasting with Python using machine learning techniques. Get the 1st edition book for expert guidance on predictive analysis. Shop now at Ubuy Haiti.
  • Learn how to apply machine learning to time series modeling
  • Comprehensive explanation and treatment of machine learning for time series forecasting
  • Suitable for readers with little to no experience in time series modeling or machine learning
  • Covers topics such as stationarity, trend, horizon, and seasonality
  • Includes real-world examples and practical strategies for data transformation and forecasting
  • Ideal for entry-level data scientists, business analysts, developers, and researchers
Publisher Wiley
Publication date December 15, 2020
Edition 1st
Language English
Print length 224 pages
ISBN-10 1119682363
ISBN-13 978-1119682363
Item Weight 13.4 ounces (379.89 grams)
Dimensions 7.3 x 0.6 x 9.1 inches (18.5 x 1.5 x 23.1 cm)

À qui est-ce destiné ?

Suitable For
  • Data Scientists

    Ideal for data scientists seeking to enhance their skills in forecasting using machine learning techniques with Python.

  • Students

    Great for university students studying data analytics or machine learning who need practical examples and applications.

  • Business Analysts

    Helpful for business analysts who want to leverage time series forecasting for data-driven decision-making in industries.

Not Suitable For
  • Complete Beginners

    Not suitable for beginners with no prior knowledge of Python or machine learning concepts and terminologies.

DESCRIPTION DU PRODUIT

Machine Learning for Time Series Forecasting with Python

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Questions et réponses des clients

  • question: What is 'Machine Learning for Time Series Forecasting with Python 1st Edition' about?

    répondre: This book provides a comprehensive guide to utilizing machine learning techniques specifically for time series forecasting using Python. It covers foundational concepts of time series data and guides readers through implementing various algorithms to predict future values in datasets. Through practical examples, readers will learn how to preprocess time series data, select appropriate models, and evaluate forecasting performance, making it a valuable resource for data scientists and analysts.
  • question: Who is the intended audience for this book?

    répondre: The primary audience consists of data scientists, machine learning practitioners, and statisticians who seek to enhance their skills in time series analysis. Additionally, it serves students and professionals in fields such as finance, economics, and operations research. The book's blend of theoretical foundations and practical applications makes it suitable for both beginners and those who have some experience in machine learning.
  • question: What programming knowledge is required to effectively use this book?

    répondre: Readers should have a basic understanding of Python programming and familiarity with essential libraries like NumPy, Pandas, and Matplotlib. While the book gradually introduces machine learning concepts, having a foundational knowledge of programming will help in comprehending the coding examples and implementation techniques presented throughout the text, facilitating a more fruitful learning experience.
  • question: What machine learning algorithms are covered in this edition?

    répondre: The book covers a variety of machine learning algorithms suited for time series forecasting, including linear regression, decision trees, and ensemble methods. In addition, it delves into the application of advanced techniques like LSTM (Long Short-Term Memory) neural networks, which are particularly effective in capturing temporal dependencies in sequential data. These algorithmic insights enable readers to select and apply the most suitable method for their specific forecasting needs.
  • question: How can I apply the concepts from this book in real-world scenarios?

    répondre: Readers can apply the techniques learned from this book to various real-world problems such as stock price predictions, demand forecasting, and weather forecasting. By implementing the algorithms discussed, they can analyze historical data to predict future trends, leading to better decision-making in business, finance, and environmental planning, among other fields.
  • question: Does this book include hands-on projects or examples?

    répondre: Yes, 'Machine Learning for Time Series Forecasting with Python' is packed with hands-on projects and practical examples that walk readers through real datasets. These projects are designed to implement theoretical concepts in practice, allowing readers to experiment with algorithms and see their effects on prediction accuracy and performance, which reinforces the learning experience.
  • question: What tools or libraries are recommended for the practices in this book?

    répondre: The book primarily utilizes Python and its libraries such as Pandas for data manipulation, NumPy for numerical computations, and Scikit-learn for machine learning functions. Additionally, readers are encouraged to explore TensorFlow or Keras when working with deep learning models for time series forecasting, providing a comprehensive toolkit for tackling diverse forecasting tasks.
  • question: Can this book help me improve my forecasting skills?

    répondre: Absolutely! This book not only teaches machine learning algorithms, but also focuses on improving forecasting skills by providing insights into model selection, evaluation metrics, and distinguishing between different forecasting scenarios. Readers will gain a structured approach to analyzing time series data and enhancing their decision-making capabilities while creating accurate forecasts.
  • question: Is this book suitable for self-learners?

    répondre: Yes, this book is designed with self-learners in mind. It features clear explanations of concepts, progressively builds on complexities, and includes practical examples that reinforce learning. This makes it an excellent resource for individuals looking to enhance their skills outside of formal education settings, empowering them to independently navigate time series forecasting.
  • question: Where can I buy 'Machine Learning for Time Series Forecasting with Python 1st Edition'?

    répondre: You can purchase 'Machine Learning for Time Series Forecasting with Python 1st Edition' on Ubuy in Haiti. Ubuy offers a reliable platform to find this book along with other advanced resources on machine learning and data science to aid in your professional development.

Probability & Statistics Editorial Review

Machine Learning for Time Series Forecasting with Python is a comprehensive guide published by Wiley that delves into the nuances of time series analysis. This book, with its straightforward explanations and practical examples, aims to simplify complex concepts for readers. Although some users found it to have a deep understanding of the subject matter, they noted that the content sometimes lacked sufficient real-world case studies and clarity on critical aspects such as interpreting graphs. For those new to machine learning, the book serves as an accessible introduction to forecasting and even to learning Python. However, while some appreciated the structure, others felt that repetitive content left them looking for more depth and actionable insights.

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Avantages

  • Clear explanations of time series concepts
  • Ideal for beginners in time series forecasting
  • Practical and actionable examples provided
  • Good introduction to Python programming language
  • Helpful for immediate deployment strategies

Les inconvénients

  • Content may feel repetitive to some readers

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Caractéristiques et avantages

  • Learn how to apply machine learning to time series modeling
  • Comprehensive explanation of machine learning principles
  • Real-world examples and concrete strategies
  • Ideal for data scientists, analysts, developers, and researchers

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