Learn to Use Mathematics in Machine Learning with this Coursera Program

This Coursera Mathematics in Machine Learning program provides a comprehensive introduction to linear algebra concepts and fundamentals used in Machine Learning (ML). The course is designed to help learners develop a solid foundation in linear algebra and understand its application in ML algorithms. It will assist you in gaining insightful knowledge about the top skills required to become a machine learning expert.

This course is known for its practicality and organization. The course materials are presented logically and easily, making it easy for learners to understand the concepts. The instructors emphasize intuition, providing practical examples and sufficient theoretical explanations to help learners understand the material easily.

Quizzes and assignments play a critical role in reinforcing the concepts taught in the course. They allow learners to test their knowledge and apply the concepts learned in practical situations. These quizzes and assignments are designed to thoroughly test the learners' understanding of the concepts taught in the course.

In addition, the practice exercises provided in the course build the base for doing the graded assignments. These exercises allow learners to practice the concepts they have learned in the lectures and apply them to real-world problems. By doing the practice exercises, learners can gain confidence in their ability to apply the concepts they have learned, making it easier to complete the graded assignments.

The instructors, David Dye and Sam Cooper, teach at the Imperial College of Engineering. They have a very practical and intuitive approach towards difficult mathematical concepts and make them enjoyable to follow.

As we know, math is used in Machine learning, it provides the theoretical foundation and algorithms necessary for understanding and developing models. The course covers various topics, including vectors and matrices, linear equations, matrix factorization, eigenvalues and eigenvectors, and singular value decomposition. These concepts are explored in the context of ML applications, such as regression analysis, principal component analysis, and support vector machines.

The course consists of video lectures, quizzes, and programming assignments that allow learners to practically apply the concepts covered in the lectures. Learners also have access to a discussion forum to interact with instructors and fellow learners. 

In the beginning, the course deals with basic concepts of vectors and matrices. As the course progresses, the student learns more about vector properties. In the mid-week, the concepts of matrix operations and coding special matrices through Python are introduced. The pre-final week introduces a matrix as a projection space where a vector can be transformed, and it paves the way to an important concept of the Gram-Schmidt process of making the matrix orthonormal and reflection of an object in a reference plane.

This is introduced as a programming exercise. The final week deals with the concept of eigenvalues, eigenvectors and Diagonalization through eigenvectors and eigen values and the introduction of page rank, which is the foundation algorithm of the Google Search engine. The same is presented as a programming assignment.

By the end of the course, learners will have a deep understanding of linear algebra and its application in ML algorithms. They will be able to implement ML algorithms using linear algebra concepts, analyze and interpret the results of these algorithms, and apply these techniques to solve real-world problems.

I am currently working as a Software Engineer in Big Data ETL pipelines. The course has helped me understand some of the core concepts and algorithms of ML through a practical and intuitive based approach. It also helps in building the programming logic of difficult mathematical scenarios. I was pursuing a Master of Data Science from Coursera and had this subject in the curriculum. I took this course to supplement and enhance my understanding in this area.

One of the main reasons why this course is good is because it provides learners with a solid foundation in machine learning mathematics topics like linear algebra. The course covers various topics, including vectors and matrices, linear equations, matrix factorization, eigenvalues and eigenvectors, and singular value decomposition. By the end of the course, learners have a comprehensive understanding of these concepts and can apply them to solve real-world problems.

Another reason why this course is good is that it emphasizes the application of linear algebra concepts in ML. It is a progressing domain and there are numerous advantages to Machine Learning. The course teaches learners how to apply linear algebra to solve problems in ML, such as regression analysis, principal component analysis, and support vector machines. By working on programming assignments, learners gain hands-on experience in applying linear algebra to solve real-world problems, which is critical for success in ML.