Embark on a transformative journey through modern machine learning, computer vision, and generative AI with this comprehensive course. Explore industry-leading techniques like image classification, object detection, and cutting-edge generative models powered by KerasCV and Stable Diffusion. Gain hands-on experience with tools and frameworks that make these advanced topics approachable and actionable.
Machine Learning – Modern Computer Vision & Generative AI
Instructor: Packt - Course Instructors
Sponsored by PKO BP
Recommended experience
Recommended experience
What you'll learn
Understand how to fine-tune pre-trained models for image classification tasks.
Gain expertise in object detection techniques, including dataset formats and advanced loss functions.
Learn how to generate images using Stable Diffusion and explore its architecture.
Implement practical machine learning projects using KerasCV and Python in real-world scenarios.
Skills you'll gain
- Artificial Intelligence
- Machine Learning Software
- Artificial Neural Networks
- Generative AI
- Tensorflow
- Deep Learning
- Computer Vision
- Keras (Neural Network Library)
- Machine Learning
- Computer Programming
- Artificial Intelligence and Machine Learning (AI/ML)
- Applied Machine Learning
- Computer Science
- Python Programming
Details to know
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January 2025
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There are 8 modules in this course
In this module, we will introduce you to the structure of the course and provide you with the tools necessary to succeed. We will guide you through understanding the course layout and show you how to access all course materials and code through GitHub and Bitly links.
What's included
3 videos1 reading
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.
What's included
7 videos1 assignment
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.
What's included
13 videos1 assignment
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.
What's included
6 videos1 assignment
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.
What's included
2 videos1 assignment
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.
What's included
4 videos1 assignment
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.
What's included
2 videos1 assignment
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.
What's included
1 video1 assignment
Instructor
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