This course provides a comprehensive guide to building a gender classification model using deep learning techniques. Starting from the fundamentals of image processing to advanced concepts in neural networks, the course equips students with the knowledge to develop a model that classifies gender based on facial images. Students will learn how to preprocess data, design a neural network architecture, and train a model using TensorFlow/Keras.

Throughout the course, students will work on:

• Data Collection and Preprocessing: Learn how to handle large datasets of images, including techniques for face cropping, resizing, and augmentation to improve model accuracy.

• Convolutional Neural Networks (CNNs): Dive into CNNs, which are ideal for image-related tasks, and understand how layers such as convolution, pooling, and fully connected layers contribute to image classification.

• Model Training and Evaluation: Implement model training using TensorFlow/Keras, tune hyperparameters, and assess performance using accuracy, precision, recall, and F1 score.

• Custom Image Prediction: Work with real-time prediction by uploading custom images to the model and fine-tuning it for specific datasets.

• Error Handling and Model Adaptation: Explore how to create a self-learning model that adapts to incorrect predictions and improves over time by leveraging user feedback.

This course is designed for students and professionals with basic knowledge of deep learning, eager to apply these concepts in the exciting domain of gender classification. By the end of the course, learners will have a fully functional gender classification model and the skills to deploy it in real-world applications.

Who this course is for:

• Beginner