Build practical image classification skills using Keras, convolutional neural networks (CNNs), transfer learning, and Google Colab. Designed for learners seeking hands-on experience with deep learning, this project-based course guides you from environment setup and dataset preparation to model training, evaluation, visualization, and optimization.

You’ll set up an image classification project in Google Colab, upload files, download datasets, and define the project scope. You’ll use pretrained models for transfer learning and visualize intermediate CNN layers to understand how networks extract image features. You’ll then create CNN architectures with image augmentation, compile and train models, evaluate loss values and performance, and retrain models to improve accuracy. What makes this course distinctive is its step-by-step integration of cloud-based tools, pretrained models, augmentation, and intermediate layer visualization. Rather than focusing only on theory, you’ll build and improve an image classification model through practical implementation. By the end, you’ll be prepared to apply image classification best practices and approach similar deep learning projects in research, academia, or industry with greater confidence.