**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.**