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Official Arduino Tiny Machine Learning Kit AKX00028 Starter Bundle Nano 33 BLE Sense, OV7675 Camera & Custom Shield for Hands-On TinyML Projects — image 1
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Official Arduino Tiny Machine Learning Kit AKX00028 Starter Bundle Nano 33 BLE Sense, OV7675 Camera & Custom Shield for Hands-On TinyML Projects

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The Official Arduino Tiny Machine Learning Kit AKX00028 Starter Bundle Nano 33 BLE Sense, OV7675 Camera & Custom Shield for Hands-On TinyML Projects by Arduino is a premium official arduino kits designed for premium buyers. It features Item Type: Kit and Model No. / Name: AKX00028.

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Estimated delivery: 17 Aug31 Aug
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Quick Specs

Item Type: Kit
Model No. / Name: AKX00028
Length (mm): 45
Width (mm): 18
Weight (g): 5
Shipping Weight: 0.794 kg

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SKU: robu-957160Category: Official Arduino Kits

About This Product

The Official Arduino Tiny Machine Learning Kit AKX00028 Starter Bundle Nano 33 BLE Sense, OV7675 Camera & Custom Shield for Hands-On TinyML Projects by Arduino is a premium official arduino kits designed for premium buyers. It delivers reliable performance at an accessible price.

Key Specifications

  • Item Type: Kit
  • Model No. / Name: AKX00028
  • Length (mm): 45
  • Width (mm): 18
  • Weight (g): 5
  • Shipping Weight: 0.794 kg
  • Shipping Dimensions: 18 × 3 × 23 cm

Why Buy from Pricio?

Pricio sources this product directly, cutting out multiple middlemen. Every order includes free shipping, a 15-day return policy, and secure payment via Razorpay.

The Official Arduino Tiny Machine Learning Kit AKX00028 Starter Bundle Nano 33 BLE Sense, OV7675 Camera & Custom Shield for Hands-On TinyML Projects, combined with the exciting TinyML Applications and Deploying TinyML on Microcontrollers courses that are part of the Tiny Machine Learning (TinyML) specialization from EdX will equip you with all the tools you need to bring your ML visions to life!

The kit consists of a powerful board equipped with a microcontroller and a wide variety of sensors (Official Arduino Nano 33 BLE Sense LITE). The board can sense movement, acceleration, rotation, temperature, humidity, barometric pressure, sounds, gestures, proximity, color, and light intensity.

The kit also includes a camera module (OV7675) and a custom Official Arduino shield to make it easy to attach your components and create your very own unique TinyML project. You will be able to explore practical ML use cases using classical algorithms as well as deep neural networks powered by TensorFlow Lite Micro. The possibilities are limited only by your imagination!

With this kit combined with the power of Tiny Machine Learning (TinyML) you can build a small intelligent device that reacts to sounds like a keyword being spoken, recognizes gestures like waving a magic wand, or even recognize faces.

Note:  To explore more about this kit, you need ML examples. Please go to the Attachment section to download the library. 

Features:

  1. It Brings a Machine learning vision to your life.
  2. Equipped with a powerful microcontroller board.
  3. Equipped with a wide variety of sensors.
  4. Easy to attach your components and create your very own unique TinyML project.
  5. The board can sense movement, acceleration, rotation, temperature, humidity, barometric pressure, sounds, gestures, proximity, color, and light intensity, etc.

Useful Link:

https://blog.arduino.cc/2023/05/22/these-projects-from-cmu-incorporate-the-arduino-nano-33-ble-sense-in-clever-ways/

Package Includes:

1 x Official Arduino Nano 33 BLE Sense LITE board

1 x OV7675 Camera

1 x Official Arduino Tiny Machine Learning Kit AKX00028

1 x USB A to Micro USB Cable

 

Setup Guide

1
Identify the pins
Locate VCC (power), GND (ground), and data pins (SDA/SCL for I2C, MOSI/MISO for SPI, or TX/RX for UART). Refer to the pinout diagram on the product page.
2
Wire to your microcontroller
Connect VCC to 3.3V or 5V (check the module spec), GND to GND, and data pins to the appropriate GPIO pins on your Arduino or Raspberry Pi.
3
Install libraries
In Arduino IDE, go to Sketch → Include Library → Manage Libraries and search for the sensor library. Install the recommended version.
4
Upload example sketch
Open File → Examples → [library name] → Basic Example. Upload to your board and open Serial Monitor to view output.
5
Calibrate if needed
Some sensors (temperature, humidity, distance) may need a short warm-up period or calibration. Run the calibration sketch if provided.