Description

Data science is a multidisciplinary field, and a data scientist should acquire comprehensive skills for a flourishing career. Mathematics forms the backbone of machine learning algorithms. Therefore, having a sound knowledge of the fundamental concepts of Mathematics, such as Linear Algebra, Calculus, etc., is helpful. As a result, understanding the inner workings of machine learning algorithms is easy.

The Association of Data Scientists (ADaSci) is coming up with this opportunity to let data science aspirants dive deeply into Mathematics. In this course, you will understand the concept of Linear Algebra which is an integral part of the data science curriculum. Furthermore, the concepts of Linear Algebra will be discussed in this workshop in detail, along with hands-on experiments in Python.

Learning Outcomes

• In-depth understanding of the concepts of linear algebra used in data science
• Sound exposure to key linear algebra concepts, including vectors, matrices, and tensors.
• Strong mathematical understanding of using different linear algebraic operations in data science and machine learning.
• Complete hands-on understanding of Linear Algebra concepts in Python.

Outline

1. Vectors
   1. Introduction to Vectors
   2. Vector Addition
   3. Scalar Multiplication
   4. Vector Multiplication
   5. Norm
2. Matrices
   1. Introduction to Matrices
   2. Matrix Addition
   3. Matrix Multiplication
   4. Determinant of a Matrix
   5. Inverse of a Matrix
3. Eigenvalues and Eigenvectors
   1. Overview
   2. Eigenvalues Eigenvectors
4. Tensors
   1. Overview of Tensors
   2. Tensors properties
   3. Tensor operations
5. Decompositions
   1. Overview of decomposition
   2. Decomposition techniques
   3. Eigen decomposition
   4. Singular Value Decomposition (SVD)
   5. Rank Factorization