Applied Deep Learning and neural networks

About this course

Master applied deep learning, neural networks, TensorFlow, computer vision, and predictive modeling through practical AI projects. Build job-ready skills by learning how neural networks process data, recognize visual patterns, and generate predictions. This Specialization helps learners develop a strong foundation in deep learning while applying concepts through hands-on workflows. Learners will explore neural network architectures, activation functions, optimization methods, dataset preparation, model evaluation, and TensorFlow-based implementation. The learning path also extends into computer vision, covering image processing, feature extraction, object detection, segmentation, transfer learning, and image generation concepts. Learners will also complete a practical neural network project focused on car price prediction, where they will analyze structured datasets, prepare features, build regression models, evaluate results, and improve performance using regularization techniques. By completing this Specialization, learners will be able to design, build, analyze, and evaluate deep learning models for classification, computer vision, and predictive analytics use cases. This program is ideal for aspiring AI developers, data scientists, machine learning learners, and professionals seeking practical neural network skills.

Prices, discounts and availability are set by Coursera. We may earn a commission when you purchase through links on this site.