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Machine Learning for Time Series Data in Python

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

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4 Hours13 Videos53 Exercises30,680 Learners4550 XPMachine Learning Scientist TrackTime Series Track

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Course Description

Time series data is ubiquitous. Whether it be stock market fluctuations, sensor data recording climate change, or activity in the brain, any signal that changes over time can be described as a time series. Machine learning has emerged as a powerful method for leveraging complexity in data in order to generate predictions and insights into the problem one is trying to solve. 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.

  1. 1

    Time Series and Machine Learning Primer


    This chapter is an introduction to the basics of machine learning, time series data, and the intersection between the two.

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    Timeseries kinds and applications
    50 xp
    Identifying a time series
    50 xp
    Plotting a time series (I)
    100 xp
    Plotting a time series (II)
    100 xp
    Machine learning basics
    50 xp
    Fitting a simple model: classification
    100 xp
    Predicting using a classification model
    100 xp
    Fitting a simple model: regression
    100 xp
    Predicting using a regression model
    100 xp
    Machine learning and time series data
    50 xp
    Inspecting the classification data
    100 xp
    Inspecting the regression data
    100 xp
  2. 3

    Predicting Time Series Data

    If you want to predict patterns from data over time, there are special considerations to take in how you choose and construct your model. This chapter covers how to gain insights into the data before fitting your model, as well as best-practices in using predictive modeling for time series data.

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In the following tracks

Machine Learning ScientistTime Series




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Chris Holdgraf Headshot

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-source software, and scientific workflows. He's a core member of Project Jupyter and contributes to several other open source tools for data analytics and education.
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Lloyds Banking Group

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Harvard Business School

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Decision Science Analytics, USAA