Project: Anomaly Detection in Time Series Data with Keras

In this hour-long, hands-on introduction to anomaly detection in time series data with Keras, you and I will build an anomaly detection model using deep learning. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price changes) in the S&P 500 index. We will also create interactive charts and plots using Plotly Python and Seaborn for data visualization and display our results in Jupyter notebooks.

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Project: Anomaly Detection in Time Series Data with Keras

Duration (mins)

Learners

NA / 5

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Task List


We will cover the following tasks in 1 hour and 3 minutes:


Project Overview and Import Libraries


Load and Inspect the S&P 500 Index Data


Data Preprocessing


Temporalize Data and Create Training and Test Splits


Build an LSTM Autoencoder


Train the Autoencoder


Plot Metrics and Evaluate the Model


Detect Anomalies in the S&P 500 Index Data

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Snehan Kekre

About the Host (Snehan Kekre)


Snehan Kekre is a Machine Learning and Data Science Instructor at Coursera. He studied Computer Science and Artificial Intelligence at Minerva Schools at KGI, based in San Francisco. His interests include AI safety, EdTech, and instructional design. He recognizes that building a deep, technical understanding of machine learning and AI among students and engineers is necessary in order to grow the AI safety community. This passion drives him to design hands-on, project-based machine learning courses on Rhyme.



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