Who Can Take This Course?

Get an overview of the course’s intended audience, prerequisites, material, and duration.

This course is intended to benefit academia and the software industry, and it’s useful for undergraduate, graduate, and research learners interested in machine learning. The course focuses on the impact of mislabeled and imbalanced data in machine learning models, which is essential knowledge for professionals in the software industry. Whether you’re a data scientist, software engineer, or researcher, this course will equip you with the necessary skills to handle these challenges and effectively build robust machine learning models.

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Prerequisites

This course is designed to maximize ease of understanding and accessibility for a wide audience. However, knowledge of the following will help you understand the course more easily:

  • Basic understanding of Python programming.

  • Basic understanding of Python libraries such as Matplotlib, pandas, and NumPy.

  • Basic understanding of the convolutional neural network (CNN) architecture.

Course material

You will have access to a range of course materials, including the following:

  • Course notes and reference materials.

  • Animations.

  • Programming exercises.

  • Quizzes and assignments.

Course assessment

You will be assessed through a combination of quizzes and programming assignments. Upon completing the course, you will receive a certificate of completion.

Course duration

The course duration will depend on your experience with machine learning and availability to work on the course materials.

You will have access to a range of course materials, including the following:

  • Course notes and reference materials.

  • Animations.

  • Programming exercises.

  • Quizzes and assignments.