Applying Machine Learning on IoT data

Applying Machine Learning on IoT data
Featured

Applying Machine Learning on IoT data

Applying Machine Learning on IoT data :

Introduction:

In this tutorial we will be Applying Machine Learning on IoT data for data prediction which we will be collecting from our sensor. We will be using following components in the tutorial:

  1. Google Spreadsheet(To store data)
  2. Google Script editor(to create an api which will be used to insert data in Google spreadsheet)
  3. Jupyter Notebook (It is an interactive computational environment, in which you can combine code execution, rich text, mathematics, plots and rich media. Basically environment where we will apply machine learning on IoT (Internet of Things) Data)

Content:

  1. Creating an api to add data on Google spreadsheet using Google Scripts.Includes writing javascript to write the api and test it using postman
  2.  Making circuit with NodeMCU and sensor(DHT11) and writing C code to update data on Google spreadsheet using NodeMCU
    (Gap of 15 days so that you have enough data to apply Machine Learning on it. Machine learning requires a lot of data)
  3. Using Jupyter Notebook to use the collected data from NodeMCU to apply data prediction
  4. Bonus Content(Surprise  - Make sure you subscribe to stay updated here)

Requirements:

  1. Laptop/Computer
  2. Good Internet
  3. Half Size Breadboard :Solderless Breadboard with 400 Tie-Point (White)
  4. Micro USB Cable : Universal Micro USB Flat Data Cable -Rock Original-Grey
  5. ESP12E Node MCU development Board : ESP12E Node MCU development Board
  6. DHT 11 Temperature and Humidity Sensor : SMAKN DHT11 / DHT-11 Digital Temperature and Humidity Sensor

(See Also : Face Recognition using Kairos API )

Click on the below link to get started.

, , , , , , , , , , , , , , , , ,