Expt 04 | Feature Selection in ML| MNIST Handwritten Digit Recognition & CIFAR Image Classification

In this video, we perform a Machine Learning practical experiment on Feature Selection, specially designed for Mechanical Engineering students and beginners in AI/ML. Feature selection is an important step in data preprocessing that helps improve model performance by selecting the most relevant features from a dataset.

This experiment demonstrates feature selection and implementation using two popular datasets:
MNIST Dataset for handwritten digit recognition
CIFAR Dataset for image classification

You will learn how to:
Understand the concept of Feature Selection in Machine Learning
Preprocess image datasets for ML models
Implement feature selection techniques using Python and Machine Learning libraries
Apply the process on MNIST handwritten digits dataset
Perform image classification using CIFAR dataset
Evaluate the performance of the model after feature selection

This practical is useful for engineering students, AI/ML beginners, and anyone learning machine learning experiments using Python and Jupyter Notebook.

📌 Topics Covered
Feature Selection concept in Machine Learning
Importance of feature reduction
MNIST dataset for handwritten digit recognition
CIFAR dataset for image classification
Implementation using Python

🎓 Ideal for:
Mechanical Engineering Students
Computer Engineering Students
AI/ML Beginners
Machine Learning Lab Experiments

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Видео Expt 04 | Feature Selection in ML| MNIST Handwritten Digit Recognition & CIFAR Image Classification канала DataLearnm