After completing this course, you can continue your machine learning journey by taking advanced courses in deep learning, natural language processing, or computer vision. You can also practice your skills by working on machine learning projects and contributing to open-source machine learning projects.

A basic understanding of probability theory and linear algebra will be helpful, but the course will cover the necessary mathematical concepts as part of the curriculum.

Yes, this course involves coding exercises and assignments that require you to implement machine learning algorithms using Python and essential libraries.

This course covers a range of machine learning techniques, including probability theory, ML modeling, supervised learning, unsupervised learning, feature engineering, model evaluation and selection, and ethics in machine learning: bias, fairness, and interpretability

Yes, this course is designed for students who are new to machine learning and want to gain a solid foundation in the concepts and techniques of machine learning.

Python programming, statistics, data processing with Pandas and NumPy, and data visualization are prerequisites for this course and are covered in separate crash courses.