Full Machine Learning Project — Fourier Transformation & Clustering (Part 5b)

Want to get started with freelancing? Let me help: https://www.datalumina.com/data-freelancer Need help with a project? Work with me: https://www.datalumina.com/solutions Welcome back to the second half of Part 5 in this series! We'll be exploring temporal abstraction, frequency abstraction, and clustering. We'll discuss using rolling windows to compute statistical properties and how to handle overlapping windows. We'll also cover clustering and grouping similar data points together. Thanks for watching! 👉🏻 Source material for this week: https://docs.datalumina.io/tjGyJjXxfpChiL ⏱️ Timestamps 00:00 Introduction 01:03 Temporal abstraction 15:35 Frequency abstraction 27:49 Dealing with overlapping windows 32:05 Clustering 45:51 Export data Project overview (what you will learn) Part 1 — Introduction, goal, quantified self, MetaMotion sensor, dataset Part 2 — Converting raw data, reading CSV files, splitting data, cleaning Part 3 — Visualizing data, plotting time series data Part 4 — Outlier detection, Chauvenet’s criterion, local outlier factor Part 5 — Feature engineering, frequency, low pass filter, PCA, clustering Part 6 — Predictive modelling, Naive Bayes, SVMs, random forest, neural network Part 7 — Counting repetitions, creating a custom algorithm Link to playlist: https://youtube.com/playlist?list=PL-Y17yukoyy0sT2hoSQxn1TdV0J7-MX4K If you find these videos helpful, consider subscribing @daveebbelaar