Maths for Data Science by DataTrained

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.