FeaturedUdemy
Machine Learning for Time Series Data in Python
4.8(181)53k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
This advanced course connects machine learning principles with time series data analysis. You will explore techniques tailored to time-varying signals across diverse domains, ranging from stock market data to health and climate sensors.
Who It's For
Advanced data scientists and machine learning engineers who want to analyze time-dependent signals and implement specialized predictive modeling workflows.
Key Takeaways
- Feature Engineering: Learn feature extraction and engineering strategies specific to complex time series signals.
- Spectral Analysis: Apply spectrograms and spectral methods to analyze frequency and time-domain features.
- Real-World Applications: Master advanced classification and forecasting by analyzing heartbeat audio sounds and predicting stock prices.
About this course
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.
Price
Free
This course is free to enrol.
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