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

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

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 el...

Hands-on coding environments

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Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

FunctionsFunctions in Data ScienceLinear FunctionsLinear CombinationsQuiz: Linear Functions and CombinationsChallenge: Linear Combination CheckerConvex CombinationsLinear IndependenceLinear SystemsQuiz: Convex Combinations and Linear IndependenceChallenge: Linear System Solver

MatricesMatrix MultiplicationMatrix OperationsQuiz: Matrices and Matrix OperationsChallenge: Matrix Multiplier

Solution Preserving OperationsEchelon MatricesGaussian EliminationGaussian Elimination Using PythonSolution Set of a Linear SystemGeometric Interpretation of InconsistencyVerifying Linear IndependenceRank of a MatrixChallenge: Check Linear IndependenceSolution Set and RankQuiz: Linear SystemsChallenge: Calculate the Largest Independent Subset

Elementary MatricesInverse of a Square MatrixDeterminant of a MatrixDeterminant by Pivots: A Python AlgorithmInverse of a Rectangular MatrixQuiz: Elementary and Invertible MatrixChallenge: Lower-upper Decomposition Algorithm

Approximate SolutionLeast Squared Error SolutionChallenge: Sum of Squared Error CalculatorLinear RegressionLinear Regression on Real DatasetMulti-target Linear RegressionPolynomial RegressionNon-linear RegressionClassification Through RegressionNeural NetworksQuiz: Regression and Classification

Vector GeneralizationSet and Closure PropertyGroup and FieldFinite FieldVector SpaceFinite Vector SpaceSpan, Basis, and DimensionsSubspaceDot Product vs. Inner ProductVector Space in Data ScienceQuiz: Vector Space, Span, and BasisChallenge: Subspace Dimension Finder

Column SpaceRow Space and Null SpaceOrthogonal Spaces and ComplementsEigenspaceQuiz: Null Space and Orthogonal Space

Mini Project

Learning to Find Discriminative Null Space for Face Recognition

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