In this module, we will explore key concepts in image classification, focusing on pre-trained models and transfer learning. You'll learn how to implement and fine-tune these models using Python, while reinforcing your skills with practical exercises.

In this module, we will dive deep into object detection, covering essential concepts and hands-on practices. You’ll learn how to use pre-trained models, enhance them with transfer learning, and handle datasets in popular formats like COCO and Pascal VOC.

In this module, we will introduce you to Stable Diffusion, focusing on its capabilities and real-world applications in generative AI. We’ll walk you through practical exercises on generating images, as well as optional deep dives into the inner workings of diffusion models and their architecture.

In this module, we will guide you through setting up your Python environment using Anaconda and installing the critical libraries necessary for machine learning. This will ensure you have the proper tools and setup to begin your journey in the course.

In this module, we will support beginners in learning Python by offering tips, explaining coding concepts, and guiding you through practical applications of TDD. You will also gain clarity on the use of Jupyter Notebooks in machine learning projects.

In this module, we will provide effective strategies for progressing through machine learning courses. You'll learn the best sequence to build your skills, helping you understand how different topics, such as text and image data handling, connect throughout your learning journey.

In this module, we will conclude the course by showing you where to find valuable free deep learning resources and discount coupons, ensuring your learning continues with accessible tools and materials at a reduced cost.