Tutorials and Guides

A useful media library of SensiML AutoML IoT information

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Learn how to use the SensiML Analytics Toolkit via easy-to-follow video segments that break down the process into the steps involved and highlight other topics of interest for getting the most from SensiML.
Session 1 - Jan 27, 2021
SensiML-QORC Web Tutorial Series
Connecting New I2C Sensors to QuickFeather and Capturing the Data in SensiML
Session 2 - Feb 3, 2021
SensiML-QORC Web Tutorial Series
Incorporating TensorFlow Lite NN Into Your SensiML Machine Learning Algorithm
Session 3 - Feb 24, 2021
SensiML-QORC Web Tutorial Series
Programming QuickFeather's FPGA Using SymbiFlow
Session 4 - Mar 3, 2021
SensiML-QORC Web Tutorial Series
Connecting QuickFeather and Data Capture Lab over WiFi Using the ESP32
Session 5 - Mar 10, 2021
SensiML-QORC Web Tutorial Series
QuickFeather FPGA + MCU Application Examples
Session 6 - Mar 17, 2021
SensiML-QORC Web Tutorial Series
ML101 – A Survey of ML Classifier Types and Their Use
Chapter 1
Introduction to SensiML Analytics Toolkit
The goal of this guide is to provide a step-by-step tutorial on how to use the SensiML Toolkit. We will walk through a Hello World style project for sensor applications.
Chapter 2
Fundamentals of Model Building
Before you build an application, it is important to create a data collection plan.
Chapter 3
Available Quick Start Projects
This project showcases the continuous event type described previously in Data Collection Planning.
Chapter 4
Getting Started with Data Capture Lab
The first step of creating a sensor application is going to be collecting and labeling raw sensor data into events of interest through the Data Capture Lab (DCL).
Chapter 5
Capturing Event Sensor Data
Now it’s time to collect some examples of the event you are trying to detect.
Chapter 6
Labeling Your Data
Once you have collected examples of the events you are trying to detect, it’s now time to label those events.
Chapter 7
Getting Started with Analytics Studio
The Analytics Studio uses AutoML to abstract the complexities of machine learning algorithms and translates them to a user-friendly interface.
Chapter 8
Building a Model with SensiML Dashboard
The Model Building part of the Analytics Studio uses SensiML’s AutoML to build a model that gives you control of the features you want in your device.
Chapter 9
Validating Your Results
The final step after creating a model is to validate your Knowledge Pack running on your edge device through the SensiML TestApp.
Chapter 10
Advanced Model Building
For data scientists we recommend looking within Analytics Studio Notebook. You can specify the advanced features, transforms, training algorithm, and validation methods that go into your model.