Master time series classification techniques, from the basics to the state-of-the-art machine learning and deep learning methods.

By enrolling, you get:

• Lifetime access to the course and future updates
• 6h+ of video lessons and guided code projects
• Hands-on experience with real-life datasets in healthcare, IoT, spectroscopy and more
• All your questions answered by me

Course outline

• Introduction to time series classification
  
  • Application of time series classification
  • Baseline classifiers
• Distance-based method
  
  • Euclidean distance
  • K-Nearest Neighbors classifier
  • Dynamic Time Warping (DTW) from scratch
  • ShapeDTW
• Dictionary-based models
  • BOSS
  • WEASEL
  • TDE
  • MUSE
  • Capstone project: Japanese vowels' speakers classification
• Ensemble methods
  • Bagging
  • Weighted classifier
  • Time series forest

• Feature-based methods
  • Summary classifier
  • Matrix profile
  • Catch22
  • TSFresh
  • Capstone project: Classify equipment failure in a processing plant
• Interval-based method
  • RISE
  • CIF
  • DrCIF
• Kernel-based methods
  • Support vector machine
  • Rocket
  • Arsenal
  • Capstone project: Classify appliances by their electricity usage
• Shapelet-based methods
  • Shapelet transform classfiier
• Hybrid models
  • HIVE-COTE
  • Capstone project: Beverage classification through spectroscopy
• BONUS: Deep learning for time series classification

In this module, we develop a blueprint such that you can apply any deep learning architectures for time series classification. By the end, you will have built flexible functions that can adapt to series with any number of samples, features and time steps.

• Deep learning blueprint with Keras
• Deep learning blueprint with PyTorch