Linear algebra is a fundamental pillar of data science. In advanced models in data science, like neural networks, the inputs and transformations are based upon vectors, matrices, and tensors which require a reasonable understanding of linear algebra to get the desired results. It is elegant and the most applied mathematics under the umbrella of data science.
This course teaches linear algebra with a focus on data science. This course encompasses several engaging illustrations, including static images and animations. Furthermore, this course presents mathematical modeling through programming in Python. This course contains several executable coding playgrounds on real data sets and a final project with practical applications.
Aside from theoretical implementations, the modern-day world needs its daunting calculations, trajectory mapping, and distance manipulation, all of which linear algebra provides. By the end of this course, you’ll have a working knowledge of all the necessary teachings in linear algebra.
Linear algebra is a fundamental pillar of data science. In advanced models in data science, like neural networks, the inputs and...Show More
WHAT YOU'LL LEARN
Learning the intricate concepts of linear algebra from scratch
Working knowledge of various linear algebra techniques using Python
A visual understanding of concepts such as vector space, spans, and subspace with animations
Familiarity with valuable concepts like fields, eigenspaces, diagonalization, and SVD
An understanding of how linear algebra concepts build the most useful tools in data science, such as neural networks
The ability to apply linear algebra concepts to real-world problems through coding exercises and practical projects
Learning the intricate concepts of linear algebra from scratch
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Learning Roadmap
2.
Linearity
Linearity
Get started with linear functions, linear combinations, and solving linear systems in data science.
3.
Matrices
Matrices
5 Lessons
5 Lessons
Master the steps to utilize matrices and perform matrix operations essential for data science.
4.
Solving Linear Systems
Solving Linear Systems
12 Lessons
12 Lessons
Grasp the fundamentals of solving linear systems, Gaussian elimination, and matrix rank.
5.
Singularity
Singularity
7 Lessons
7 Lessons
Map out the steps for working with matrices in data science using elementary transformations.
6.
Linear Regression and Least Squares
Linear Regression and Least Squares
11 Lessons
11 Lessons
Focus on linear and non-linear regression techniques, practical applications, multi-target regression, and neural networks.
7.
Vector Space
Vector Space
12 Lessons
12 Lessons
Build on vector properties, sets, fields, vector spaces, subspaces, and applications in data science.
8.
Vector Spaces of a Matrix
Vector Spaces of a Matrix
5 Lessons
5 Lessons
Step through vector spaces, null spaces, orthogonal complements, and eigenspaces in matrix algebra.
9.
Singular Value Decomposition: SVD
Singular Value Decomposition: SVD
3 Lessons
3 Lessons
Get started with orthogonal diagonalization and Singular Value Decomposition (SVD) for matrix factorization.
Certificate of Completion
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Course Author:
Developed by MAANG Engineers
Meet the Instructor
Khayyam Hashmi
Software Engineer at Educative
Every Educative resource is designed by our team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who've shipped production code at scale and taught the theory behind it. No videos, no fluff.
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