Step-by-Step Tutorial: Building a Machine Learning Model for Image Classification using TensorFlow
In this tutorial, I’ll walk you through the process of building a machine learning model for image classification using TensorFlow, a popular open-source machine learning framework. You’ll learn how to preprocess image data, create a neural network architecture, train the model, and make predictions. Let’s dive in!
Prerequisites:
- Basic understanding of Python programming.
- Python and pip installed on your machine.
Step 1: Setup and Installation
- Create a new directory for your project and navigate to it in your terminal.
- Set up a virtual environment:
python3 -m venv venv
- Activate the virtual environment:
source venv/bin/activate
- Install TensorFlow:
pip install tensorflow
Step 2: Prepare Image Data
- Download or collect a dataset of labeled images for classification.
- Organize the dataset into training and validation sets.
- Resize and preprocess the images to a consistent size using libraries like
PIL
(Python Imaging Library).