Deep Learning with Real-World Projects
About this course
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 comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
75/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- understand the basics of perceptrons and neural networks
- apply convolutional neural networks (CNNs) in practical scenarios
- implement transfer learning techniques
- build and optimize neural networks using TensorFlow and Keras
- analyze medical images and classify natural images
Course objectives
- to provide a thorough understanding of deep learning techniques
- to equip students with practical skills through project-based learning
- to enhance problem-solving abilities through interactive coaching
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