Time Series Classification in Python

Develop robust and performant classification models for time series data using machine learning and deep learning

What you'll learn
Build optimized time series classification models
Gain a deep understanding of algorithms and how they work
Use machine learning and deep learning for time series classification
Visualize complex time series classification data
Gain experience with real-life datasets in healthcare, IoT, spectroscopy and more!

Requirements
Familiarity with Python
Knowledge of common machine learning concepts like: train/test split, grid search.

Description
Master time series classification in Python! This course covers machine learning and deep learning techniques for classifying time series, all applied in guided hands-on projects in 100% Python.By the end of this course, you will:master time series classificationperform feature engineering and model optimization for classificationlearn and implement state-of-the-art machine learning and deep learning modelsget hands-on experience with real-life datasets in the fields of healthcare, IoT, sensor data, spectroscopy and moreThis is the most complete course on time series classification! We cover all types of models like:Distance-basedDictionary-basedEnsemble modelsFeature-basedInterval-basedKernel-basedShapelet modelsMeta classifiersWe first explore the theory and inner workings of each model before applying them in a hands-on project using Python.Plus, get an additional section covering deep learning models, giving you a blueprint to apply any deep learning architecture for time series classification. All functions are flexible such that you can handle series with any number of features, samples and time steps.Detailed outline:Introduction to time series classificationApplication of time series classificationBaseline classifiersDistance-based methodEuclidean distanceK-Nearest Neighbors classifierDynamic Time Warping (DTW) from scratchShapeDTWDictionary-based modelsBOSSWEASELTDEMUSECapstone project: Japanese vowels' speakers classificationEnsemble methodsBaggingWeighted classifierTime series forestFeature-based methodsSummary classifierMatrix profileCatch22TSFreshCapstone project: Classify equipment failure in a processing plantInterval-based methodRISECIFDrCIFKernel-based methodsSupport vector machineRocketArsenalCapstone project: Classify appliances by their electricity usageShapelet-based methodsShapelet transform classifierHybrid modelsHIVE-COTECapstone project: Beverage classification through spectroscopyEXTRA: Deep learning for time series classificationIn 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 KerasDeep learning blueprint with PyTorch

Who this course is for
Data scientists working with in healthcare, IoT, or equipment monitoring through sensor data
Practitioners who want to develop state-of-the-art classification models