Course Machine Learning for Time Series Data in Python
This course focuses on feature engineering and machine learning for time series data.
This online course about Machine Learning for Time Series Data in Python covers a key part of what a future data analyst would require.
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.
Enroll now in this Machine Learning for Time Series Data in Python course, and don’t miss the opportunity of learning with the best, as Chris Holdgraf is. With 53 enriching exercises, 13 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.
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.