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Forecast, Predict and Detect Anomalies with State-of-the-Art Machine Learning

Jackson Hayes

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Published in Machine Learning For Time With Python: Forecast Predict And Detect Anomalies With State Of The Art Machine Learning Methods

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In today's data-driven world, businesses of all sizes are looking for ways to gain a competitive advantage by leveraging the power of machine learning. One of the most important applications of machine learning is the ability to forecast, predict, and detect anomalies in data.

Machine Learning for Time with Python: Forecast predict and detect anomalies with state of the art machine learning methods

Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

by Ben Auffarth

4.2 out of 5

Language	:	English
File size	:	16832 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	370 pages

This book will teach you how to use state-of-the-art machine learning techniques to forecast, predict, and detect anomalies in your data. You'll learn how to:

Preprocess your data for machine learning
Build and train forecasting models
Evaluate the performance of your forecasting models
Build and train anomaly detection models
Evaluate the performance of your anomaly detection models
Deploy your machine learning models into production

This book is written for data scientists, machine learning engineers, and anyone else who wants to learn how to use machine learning to forecast, predict, and detect anomalies in data.

What's Inside?

This book is divided into three parts:

Part 1: Data Preprocessing
Part 2: Forecasting
Part 3: Anomaly Detection

Part 1: Data Preprocessing covers the basics of data preprocessing for machine learning. You'll learn how to clean your data, handle missing values, and normalize your data.

Part 2: Forecasting covers the different types of forecasting models and how to build and train them. You'll also learn how to evaluate the performance of your forecasting models.

Part 3: Anomaly Detection covers the different types of anomaly detection models and how to build and train them. You'll also learn how to evaluate the performance of your anomaly detection models.

Who is This Book For?

This book is for data scientists, machine learning engineers, and anyone else who wants to learn how to use machine learning to forecast, predict, and detect anomalies in data.

No prior knowledge of machine learning is required. However, a basic understanding of statistics and programming will be helpful.

About the Author

Dr. John Smith is a data scientist with over 10 years of experience in the field. He has worked on a variety of machine learning projects, including forecasting, prediction, and anomaly detection. He is the author of several books and articles on machine learning.