Updated in May 2025.
This course now features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
This course offers an in-depth exploration of AI and deep learning, starting with foundational concepts and progressing to neural networks and deep learning with Keras. You'll learn how neural networks process data, predict outcomes, and solve complex problems.
In the second part of the course, you'll dive into Generative Adversarial Networks (GANs), learning how they generate realistic data by using two competing neural networks: the generator and discriminator. You'll build GAN models with the MNIST dataset, explore their inner workings, and fine-tune them for optimal performance.
By the course's conclusion, you'll be adept at handling various AI and deep learning libraries, training models using large datasets, and deploying deep learning solutions. Whether you're working on image generation or data augmentation, this course will provide you with the expertise needed to excel in today’s AI-driven world.
This course is ideal for intermediate learners with basic Python programming skills and some familiarity with AI or machine learning concepts. You should be comfortable with Python basics, including data structures like lists and dictionaries, and have some experience with data libraries such as NumPy.
Applied Learning Project
The included projects focus on practical applications such as predicting house prices, classifying heart disease, and assessing wine quality, enabling learners to apply deep learning and GAN techniques to real-world problems. These projects provide hands-on experience in data analysis, model building, and deployment, ensuring learners can solve authentic challenges in various domains.
