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Machine Learning for Time Series Data in Python

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$12

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49% OFF

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Time series data is ubiquitous. A time series is any signal that changes over time, such as stock market fluctuations or data about climate change. Machine learning can be used to harness the complexity of data and create predictions. This course brings together the worlds of machine learning and time series data. This course covers features engineering, machine learning and spectograms.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

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Manipulating Time Series Data in Python

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Visualizing Time Series Data in Python

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Supervised Learning with scikit-learn

What You Will Learn

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This course focuses on feature engineering and machine learning for time series data

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This course is an intersection between these two worlds of machine learning and time series data, and covers feature engineering, spectograms, and other advanced techniques in order to classify heartbeat sounds and predict stock prices

Course Instructors

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Chris Holdgraf

Fellow at the Berkeley Institute for Data Science

Chris Holdgraf is a fellow at the Berkeley Institute for Data Science at UC Berkeley. He has a PhD in cognitive neuroscience from UC Berkeley. His work is at the boundary between technology, open-sou...

Course Reviews

Average Rating Based on 3 reviews

5.0

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Course Cover

$12

$6

49% OFF

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