Linear Algebra and Feature Selection in Python is a training course on linear algebra and feature selection in the Python programming language, published by Udemy Academy. Linear algebra and feature selection are two very important and basic topics to start machine learning. These two topics form the theoretical and practical foundations of machine learning and are very important. By learning these two topics, machine learning will become easier and more conceptual for you, and you will make a meaningful connection with the nature of a huge part of this important science.
Linear algebra is one of the most important branches of algebra in the field of computer science and programming, and it is possible to trace this important knowledge in data science, machine learning, deep learning, data analysis and analysis, engineering and software development, and Statistics clearly observed.
All algorithms used in machine learning are based on linear algebra, and by learning algebra, you can easily optimize your algorithms.
What you will learn in the Linear Algebra and Feature Selection in Python training course:
- Familiarity with mathematics and its importance in the design and development of different machine-learning models
- Basic and advanced concepts of linear algebra
- Solving linear equations in an advanced way
- Determining the linear independence of a set of vectors
- Calculation of eigenvalues and eigenvectors
- Performing linear discriminant analysis (in English: Linear Discriminant Analysis, abbreviated as LDA)
- Performing dimension reduction operations in Python programming language
- Performing principal component analysis (PCA)
- Performance comparison of principal component analysis and linear differential analysis for classification with support vector machines (SVMs).
Instructor: 365 Careers
Education level: Introductory
Number of lessons: 30
Duration of training: 2 hours and 46 minutes
Course topics in 2022/3
Suitable for beginners. Some understanding of Python basics and math would be an advantage.
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