Mathematics for Machine Learning Specialization
This specialization will help you develop a solid foundation in mathematics, statistics and probability theory that is required to understand the principles of machine learning. You will learn how to implement these principles through practical applications of ML algorithms.
Courses Mathematics For Machine Learning Specialization Imperial College London
Introduction
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Mathematics for Machine Learning: Linear Algebra
In mathematics, linear algebra is the branch of mathematics concerning the study of vector spaces and their associated operators. The name “linear” refers to the fact that if a vector space is equipped with a bilinear pairing between two elements x and y, then every element of the space can be expressed in terms of these pairings:
Linear algebra is fundamental to many parts of pure mathematics, such as geometry and analysis. However, it also has several applications in engineering, computer science and physics. Linear algebra lies at the core of data analysis methods such as principal component analysis (PCA) and clustering. It also forms part of most mathematical models used in physics, chemistry and economics
Mathematics for Machine Learning: Multivariate Calculus
In this course, you’ll learn about functions of several variables and how to differentiate them. This includes:
- The derivative of a function
- The derivative of a function at a point
- The chain rule
- The quotient rule and product rule (which together form the quotient rule)
- The chain rule and quotient rule together with the product rule (which forms the chain and quotient rules)
Mathematics for Machine Learning: Principal Component Analysis PCA
Principal Component Analysis (PCA) is a linear transformation that extracts the most important features of a dataset. PCA is used in data visualization and dimensionality reduction, data preprocessing, data classification, and data clustering.
Principal Components Analysis (Part 1)
What is principal component analysis? How does it work?
Introduction to Machine learning and its application in data science
Machine learning is a branch of artificial intelligence. It is also a method of data analysis that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data, analyze it and make decisions with minimal or no human intervention.
The aim of machine learning is to build algorithms based on functions that analyze data so they can be used in practical applications such as fraud detection, medical diagnosis and other tasks where humans currently excel but machines struggle.[1]
Machine learning algorithms are often inspired by biological systems such as brains or evolution. They can mimic these systems in order to find patterns in data without being explicitly programmed for them (e.g., through neural networks).[2][3]
Conclusion
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