The Machine Learning and AI Concept Kit is a starting place for the basics of predictive algorithms learned from historical data and inputs. This kit enables you to implement the idea that you had using the foundation of artificial intelligence to \'learn\' from what you teach it. With the Machine Learning and AI Concept Kit. You\'ll also be offered an open-ended approach to machine learning and artificial intelligence, giving you countless options to make more project ideas come to life with the provided peripherals that will enable you to process the incoming data against ML-trained datasets. The core of this kit is built around the Pico4ML TinyML Dev Kit from Arducam, which is a single board system powered by Raspberry Pi\'s RP2040 microcontroller. It has the power to run TensorFlow Lite for Microcontrollers, a scaled down machine learning software. The board features a QVGA camera module with ultra-low power consumption, configurable 1-bit video data serial interface with video frame and line sync, and the monochrome sensor makes image processing easy for most machine vision applications. The audio chip on the Pico4ML is capable of directly outputting PDM (Pulse-density modulation) signals; this integration allows the RP2040 to receive audio input. Motion tracking is also a built-in feature with a 2.5mW low-power 9-axis IMU. Meanwhile, the small TFT display at the back of Pico4ML is a 160×80 LCD. It’s connected to the board through the SPI interface, allowing you to see a live preview of the camera or display the results of any of your ML models in real-time. Along with the Arudcam we have included an IoT Power Relay. The IoT Power Relay is a controllable power relay equipped with four outputs that help you create an Internet of Things project with safe and reliable power control. With the IoT Power Relay you can easily control the power going to a device with an Arduino, Raspberry Pi or other single-board computer or microcontroller. It provides an alternative to the Power Switch Tail. The IoT Power Relay is designed to allow you to *safely* control an outlet device that operates at 3--48VDC or 12--120VAC. Each IoT Power Relay features a single input (from the included C13 power cable) to four outputs: one normally on, one always on, and two normally off. The durable SPDT control relay is rated at 30/40A, for 400,000 operations. Finally, the pan/tilt that comes with this kit is an easy-to-assemble bracket that utilizes servos to move on two axes fit for camera and helping-hand applications. **Includes:** * 1x [Arducam Pico4ML TinyML Dev Kit](https://www.sparkfun.com/products/18439) * 1x [IoT Power Relay](https://www.sparkfun.com/products/14236) * 1x [Pan/Tilt Bracket Kit](https://www.sparkfun.com/products/14391) **Documents:** * [Continuous Motion Recognition](https://docs.edgeimpulse.com/docs/tutorials/continuous-motion-recognition) * [How to Use Edge Impulse to train ML](https://www.arducam.com/docs/pico/arducam-pico4mltinymldevkit/how-to-use-edge-impulse-to-train-machine-learning-models-for-raspberry-pico/) * [Tensorflow Lite Micro GitHub](https://github.com/ArduCAM/pico-tflmicro/tree/main/examples) **Arducam** * [Pico4ML User Manual](https://www.arducam.com/downloads/b0302_pico4ml_user_manual.pdf) * [Pico & Pico4ML Arducam Examples](https://github.com/ArduCAM/RPI-Pico-Cam) * [Schematic](https://www.arducam.com/downloads/Arducam-Pico4ML-board-Shematics-UC-798_SCH.pdf) * [SPI Camera Driver for Pico](https://github.com/ArduCAM/PICO_SPI_CAM) * [Board 3D Model](https://www.arducam.com/downloads/UC-798-pico4ml-design-file.step) * [Enclosure 3D Model](https://www.arducam.com/downloads/pico4ml-enclosure-design-file.stp) **IoT Power Relay** * [Datasheet](https://cdn.sparkfun.com/assets/a/5/d/a/f/iot2spec.pdf) * [FAQ](http://www.digital-loggers.com/iot2faqs.html) **Pan/Tilt Bracket Kit** * [Assembly Guide](https://learn.sparkfun.com/tutorials/setting-up-the-pi-zero-wireless-pan-tilt-camera#hardware-assembly)
