- 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
What you'll 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.
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About the instructors
- 3.8 Calificación
- 23034 Estudiantes
- 1 Cursos
Dt Evolve
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With experience and research of over a decade, our leadership experts and content managers found that the conventional training method missed essential Life Skills. While it catered to professional development skills to enhance Team Building, Leadership and Soft Skill, it needed the touch of emotions to achieve personal and professional equilibrium.
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Student feedback
Course Rating
Reviews
Topics are mentioned in a detailed manner it is juts that only a few linear algebra concepts are covered only those were also not covered in much detail. If you're considering Math for data, statistics, probability and many more topics are there don't confuse others while mentioning the title like this.
It was a good course. I learn a lot about matrix. You can do many other things with it besides adding, subtracting, multiply, or division. Thank you for this course.
I have no idea what you are doing and it's the first real teaching video.
A great refresher in data science and maths, thank you
Very little explanation about the actual linear algebra, so the instructor is assuming you already know LA... which is not listed as a prerequisite. This is just how to plug and chug LA into python. The notebooks used in this aren't even available.