Linear Algebra for Data Science & Machine Learning - 2020
LINEAR ALGEBRA for DATA SCIENCE & MACHINE LEARNING COURSE DESCRIPTION
Why Learn Linear Algebra?
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
Linear Combinations of Vectors
Linear Dependence and Independence
Solving Systems of linear equations
Linear Equation Example
Generating Set and Basis
Linear Mapping/Linear Transformation
Matrices - Tensors
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.