What you would learn in Mathematics for Machine Learning Specialization course?
In many higher-level programs that deal with Machine Learning and Data Science, it is necessary to refresh your knowledge of the fundamentals of mathematics - subjects you may have studied before university or school but were taught in a different setting or perhaps not in a way that you can comprehend. You're unable to understand how it's utilized to apply it in Computer Science. This program aims to bridge the gap by making you proficient in the mathematics that underlies it and gaining a clear understanding and connecting the subject in Machine Learning and Data Science.
The initial course covering Linear Algebra looks at the basics of linear algebra and its relationship to data. We then look at the different types of matrices and vectors and how to use them.
The second class, Multivariate Calculus, builds upon this to explore the best ways to optimize functions fitting to obtain an excellent fit to data. It begins with the basics of calculus, then employs the matrices and vectors learned from earlier courses to study the data fit.
This third class, Dimensionality Reduction using the Principal Component Analysis, uses the maths from the previous two courses to compress data with high dimensions. This course is intermediate in difficulty and will require Python and numpy expertise.
When you finish this specialization, you'll have acquired the math skills to go on your journey and to take other classes in machine learning.
Applied Learning Project
In the course in this specialization, you'll utilize the skills you've developed to develop mini-projects using Python using interactive notebooks. This easy-to-learn program will allow you to apply the skills to real-world problems. For instance, you can use linear algebra to determine the rank of pages on an internet that is small and simulated using multivariate calculus to build your neural network. You will also perform a non-linear least squares regression to adapt the model to a dataset and then use the principal component approach to identify the specifics of the MNIST numbers data set.
Content of the Course:
Apply mathematical concepts to real-world data
Derive PCA from projection perspective
Know how orthogonal projections work
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