Full Machine Learning Project — Coding a Fitness Tracker with Python (Part 1)

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 In this video series, we are going to build a fitness tracker in Python that can classify various barbell exercises based on accelerometer and gyroscope data. This will be a full machine learning project and new videos will be released weekly, so subscribe to stay tuned! 👉🏻 Source material for this week: https://docs.datalumina.io/xLAtq6PNUsMcfG ⏱️ Timestamps 00:00 Introduction 01:16 Project objective 01:58 Project background 07:46 The quantified self 10:40 Project overview (what you will learn) 17:37 Action items (complete these now) 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 at @daveebbelaar