Recognizing Handwritten Digits

Recognizing Handwritten Digits

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In this project, our primary objective will be to delve into the identification of handwritten digits through the application of machine learning algorithms. This endeavor aims to harness the power of machine learning to effectively interpret and understand handwritten digits, showcasing the potential of these algorithms in processing and analyzing complex visual information.

Index Distribution

Train Test Split

Classification Report

Confusion Matrix

Radius Neighbors Classifier

Course Content

Recognizing Handwritten Digits

Recognizing Handwritten Digits

Introduction Dataset Import Visualization Class Balance Index Distribution Train Test Split KNN Classification Report Confusion Matrix Radius Neighbors Classifier

Index Distribution

How is the distribution of category records within the data matrix?

It's crucial to ascertain if the data entries linked to a specific category are randomly scattered across row indices or if they exhibit any form of clustering.

Task

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Plot histograms for the distribution of indices for each digit (0-9) in the MNIST dataset. These histograms should be displayed on the individual subplots of the grid created.

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How is the distribution of category records within the data matrix?

It's crucial to ascertain if the data entries linked to a specific category are randomly scattered across row indices or if they exhibit any form of clustering.

Task

Swipe to show code editor

Plot histograms for the distribution of indices for each digit (0-9) in the MNIST dataset. These histograms should be displayed on the individual subplots of the grid created.

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 5

AVAILABLE TO ULTIMATE ONLY

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