Explore the application of key mathematical topics related to linear algebra with the Python programming language
What you will learn
Explore the application of key mathematical topics related to linear algebra with the Python programming language
Perform linear and logistic regressions in Python
Apply your skills to real-life business cases
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
Description
This course offers a comprehensive exploration of linear algebra, specifically tailored for application in data science and machine learning using Python. Upon completing this course, participants will gain proficiency in the following areas:
‘;
}});
- Mathematical Foundations for Data Science and Machine Learning: A foundational overview of essential mathematical concepts.
- Vector Operations in Python: Learning to manipulate vectors within the Python programming environment.
- Basis and Projection of Vectors: A deep dive into understanding and implementing vector basis and projection techniques in Python.
- Matrix Operations: Developing skills to handle matrix operations, including working with, multiplying, and dividing matrices in Python.
- Linear Transformations: Gaining an understanding of linear transformations and how to implement them using Python.
- Gaussian Elimination: Mastering the application of Gaussian elimination in problem-solving.
- Determinants: Exploring the calculation and application of determinants in Python.
- Orthogonal Matrices: Understanding and working with orthogonal matrices within the Python framework.
- Eigenvalues and Eigenvectors: Recognizing and computing eigenvalues and eigenvectors through eigendecomposition in Python.
- Pseudoinverse Computation: Learning to calculate pseudoinverse matrices in Python.
Each topic is designed to build upon the last, ensuring a thorough understanding of how linear algebraic concepts can be effectively applied in Python for data science and machine learning applications. By the end of the course, participants will have a robust set of skills to tackle real-world problems in these fields.
Math for Data Science Science and Machine Learning Introduction
Introduction
Understand how to work with vectors in Python
Understand the Basis and Projection of Vectors in Python
Work with Matrices
Matrix Multiplication
Matrix Division
Linear Transformations
Gaussian Elimination
Determinants
Orthogonal Matrices
Eigen values
Eigenvectors
PseudoInverse
