Image Classification with Keras: Build & Optimize

Overview

Learners will be able to set up deep learning environments, upload and prepare datasets, apply transfer learning, visualize CNN layers, create models with image augmentation, evaluate performance, and retrain models for improved accuracy. This course provides a complete, hands-on journey into image classification using Keras, guiding learners from the basics of project setup in Google Colab to advanced techniques such as intermediate layer visualization and retraining for optimization. By working step-by-step through real-world scenarios, participants will gain not only theoretical knowledge but also practical skills in building, training, and improving convolutional neural networks (CNNs). What makes this course unique is its project-based approach, integrating cloud-based tools, pretrained models, and visualization methods that help learners truly understand how deep learning works under the hood. By the end, learners will be empowered to apply best practices in image classification, enhance model performance, and confidently tackle similar projects in research, academia, or industry.

Syllabus

Mastering Image Classification with Keras

This module introduces learners to the foundations of image classification using Keras, starting with project setup in Google Colab, dataset preparation, and pretrained models. Learners will explore how convolutional neural networks (CNNs) extract features, apply augmentation techniques, compile and train models, evaluate loss values, and enhance performance through retraining and visualization. By the end, participants will have hands-on experience in building and improving deep learning image classification models.