Linear Algebra for Data Science & Machine Learning - 2020

LINEAR ALGEBRA for DATA SCIENCE & MACHINE LEARNING COURSE DESCRIPTION

• Why Learn Linear Algebra?

• Sets

• Linear Equation Systems

• What is a Scalar?

• Scalar & Vector Arithmetic

• Vector Addition and Subtraction

• Scalar Multiplication of Vectors

• Dot & Cross Product

• Dot Product Linear Algebra Style

• Vector Subspace

• Linear Combinations of Vectors

• Span

• Linear Dependence and Independence

• Solving Systems of linear equations

• Linear Equation Example

• Generating Set and Basis

• Linear Mapping/Linear Transformation

• Additivity

• Homogeneity

• Kernel

• Matrices - Tensors

• Matrix Multiplication

• Range of a Matrix

• Kernel of a Matrix

• Determinant of a Matrix

• Identity , Transpose and Inverse Matrix

• Eigenvector and Eigenvalue

WHY LINEAR ALGEBRA?

• Linear algebra is absolutely key to understanding the calculus and statistics you need in machine learning and data science.

• If you can understand machine learning methods at the level of vectors and matrices you will improve your intuition for how and when they work.

• A deeper understanding of the algorithm and its constraints will allow you to customize its application and better understand the impact of tuning parameters on the results.

THE OPPORTUNITIES YOU WILL HAVE WITH THIS COURSE

• In-class support: We don't just give you video lessons. We have created a professional Python Programmer team and community to support you. This means that you will get answers to your questions within 24 hours.

WHO WE ARE: DATAI TEAM ACADEMY

DATAI TEAM is a team of Python Programmers and Data Scientists.

Let's register for the course and start to Linear Algebra for Data Science & Machine Learning.