Linear Algebra and Feature Selection in Python

Partner: Udemy
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Description: Do you want to learn linear algebra?You have come to the right place!First and foremost, we want to congratulate you because you have realized the importance of obtaining this skill. Whether you want to pursue a career in data science, AI engineering, machine learning, data analysis, software engineering, or statistics, you will need to know how to apply linear algebra.This course will allow you to become a professional who understands the math on which algorithms are built, rather than someone who applies them blindly without knowing what happens behind the scenes.But let’s answer a pressing question you probably have at this point: “What can I expect from this course and how it will help my professional development?”In brief, we will provide you with the theoretical and practical foundations for two fundamental parts of data science and statistical analysis – linear algebra and dimensionality reduction.Linear algebra is often overlooked in data science and AI courses, despite being of paramount importance. Most instructors tend to focus on the practical application of specific frameworks rather than starting with the fundamentals, which leaves you with knowledge gaps and a lack of full understanding. In this course, we give you an opportunity to build a strong foundation that would allow you to grasp complex ML and AI topics.The course starts by introducing basic algebra notions such as vectors, matrices, identity matrices, the linear span of vectors, and more. We’ll use them to solve practical linear equations, determine linear independence of a random set of vectors, and calculate eigenvectors and eigenvalues, all preparing you for the second part of our learning journey - dimensionality reduction.The concept of dimensionality reduction is crucial in data science, statistical analysis, and machine learning. This isn’t surprising, as the ability to determine the importa
Category: Development > Data Science > Feature Selection
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Price: 199.99
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Source: Impact
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