Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Introduction to Time Series with Python
Top Free C++ Books 🌠 - 100% Free or Open Source!
  • Title Introduction to Time Series with Python
  • Author(s) Sadrach Pierre
  • Publisher: Built In
  • Paperback N/A
  • eBook HTML and PDF
  • Language: English
  • ISBN-10: N/A
  • ISBN-13: N/A
  • Share This:  

Book Description

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts. Perform time series analysis and forecasting confidently with Python.

About the Authors
  • N/A
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Forecasting: Principles and Practice (Rob J. Hyndman, et al.)

    This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

  • R for Time Series (Avril Coghlan)

    Build efficient forecasting models using traditional time series models and machine learning algorithms. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models.

  • A First Course on Time Series Analysis with SAS (Michael Falk, et al)

    A unique feature of this book is its integration with the statistical software package SAS® (Statistical Analysis System) computing environment. Basic applied statistics is assumed through multiple regression.

  • Data Analysis with Python (Numpy, Matplotlib and Pandas)

    Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide, using Python. Equipped with the skills to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

  • An Introduction to R and Python for Data Analysis

    This book helps teach students to code in both R and Python simultaneously. The book is written in an engaging, collaborative style that makes it enjoyable to read. It maintains its formality without creating a barrier between the reader and the content.

  • O'Reilly® Python Data Science Handbook: Essential Tools

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

  • Python for Everybody: Exploring Data in Python 3

    This book is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.

  • Think Stats, 2nd Edition: Exploratory Data Analysis in Python

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

  • Elements of Data Science using Python (Allen B. Downey)

    This book is an introduction to data science for people with no programming experience. The goal is to present a small, powerful subset of Python that allows you to do real work in data science as quickly as possible.

  • Mining Social Media using Python: Finding Stories in Data

    This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library.

  • Introduction to Probability for Data Science (Stanley Chan)

    This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.

  • Computational and Inferential: The Foundations of Data Science

    Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.

  • Data Science: Theories, Models, Algorithms, and Analytics

    It provides a bucket full of information regarding Data Science, covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. You'll explore the right approach to best practices to guide you along the way.

  • R for Data Science: Visualize, Model, Transform, Tidy, Import

    This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

Book Categories
:
Other Categories
Resources and Links