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Machine Learning for Time-Series with Python - Forecast, predict, and detect anomalies with state-of-the-art machine learning methods (Paperback)
Loot Price: R1,369
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Machine Learning for Time-Series with Python - Forecast, predict, and detect anomalies with state-of-the-art machine learning methods (Paperback)
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Get better insights from time-series data and become proficient in
model performance analysis Key Features Explore popular and modern
machine learning methods including the latest online and deep
learning algorithms Learn to increase the accuracy of your
predictions by matching the right model with the right problem
Master time series via real-world case studies on operations
management, digital marketing, finance, and healthcare Book
DescriptionThe Python time-series ecosystem is huge and often quite
hard to get a good grasp on, especially for time-series since there
are so many new libraries and new models. This book aims to deepen
your understanding of time series by providing a comprehensive
overview of popular Python time-series packages and help you build
better predictive systems. Machine Learning for Time-Series with
Python starts by re-introducing the basics of time series and then
builds your understanding of traditional autoregressive models as
well as modern non-parametric models. By observing practical
examples and the theory behind them, you will become confident with
loading time-series datasets from any source, deep learning models
like recurrent neural networks and causal convolutional network
models, and gradient boosting with feature engineering. This book
will also guide you in matching the right model to the right
problem by explaining the theory behind several useful models.
You'll also have a look at real-world case studies covering
weather, traffic, biking, and stock market data. By the end of this
book, you should feel at home with effectively analyzing and
applying machine learning methods to time-series. What you will
learn Understand the main classes of time series and learn how to
detect outliers and patterns Choose the right method to solve
time-series problems Characterize seasonal and correlation patterns
through autocorrelation and statistical techniques Get to grips
with time-series data visualization Understand classical
time-series models like ARMA and ARIMA Implement deep learning
models, like Gaussian processes, transformers, and state-of-the-art
machine learning models Become familiar with many libraries like
Prophet, XGboost, and TensorFlow Who this book is forThis book is
ideal for data analysts, data scientists, and Python developers who
want instantly useful and practical recipes to implement today, and
a comprehensive reference book for tomorrow. Basic knowledge of the
Python Programming language is a must, while familiarity with
statistics will help you get the most out of this book.
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