Preparing Data for Machine Learning Models

Offered By
Coursera Project Network
In this Guided Project, you will:

Be Able to Select a Region of Interest and Extract Features from it, so it will be your Training Dataset.

Get Introduced to Several Numpy Functions

Label the Training Dataset

Clock1.55 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

By the end of this project, you will extract colors pixels as training dataset into a form where you can feed it to your Machine Learning Model using numpy arrays. In this project we will work with images, you will get introduced to computer vision basic concepts. Moreover, you will be able to properly handle arrays and preprocess your training dataset and label it. Extracting features and preparing data is a very crucial task as it influences your model. So you will start to learn the basics of handling the data into the format where it would be accepted by a Machine Learning algorithm as Training Dataset.

Skills you will develop

numpy arraysHandling Datasetextracting featuresLabel The DatasetComputer Vision

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction and Setup

  2. Selecting Region of Interest

  3. Features as Numpy arrays

  4. Concatenate the 2 Features Array and Label the Training Dataset.

  5. Final Training Dataset Preprocessing

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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