Linear Algebra for Data Science & Machine learning. Linear Algebra is a field of mathematics that deals with the structure of linear operators on vector spaces. This can be used for data science and machine learning tasks, as it allows for the analysis and understanding of data. Linear which provides tools for performing linear algebra operations.
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When working with Linux, it is important to be aware of the different types of privilege escalation available. In this guide, we will be covering two types of privilege escalation: inear operators and vector operations.
Inear operators are a type of operator that can be used on vector spaces. They allow for the analysis and understanding of data. Python provides a module called scipy.linear which provides tools for performing linear algebra operations. This can be useful for tasks such as data science and machine learning.
Vector operations are a type of operator that can also be used on vector spaces. They allow for the manipulation of vectors. Vector which provides tools for performing vector operations. This can be useful for tasks such as data science and machine learning.
- Complete Understanding of Python from Scratch
- NumPy Array, NumPy Operations
- Pandasn and Numpy for Data Analysis
- DataFrames, Pandas Series, Pandas Matrix
- Learn Numpy and Pandas Library
- Understand The Basics of Linear Algebra And Have A Solid Foundation In Linear Algebra
- Understand how to use Python to do linear algebra operations
Linear Algebra for Data Science & Machine learning in Python Course Requirements
- Basic High School Math
Linear Algebra for Data Science & Machine learning in Python Course Description
In this course, We will explain the essentials of Linear Algebra and everything that you need to understand the basics of linear algebra as well as explain Python from Scratch.
We will cover content such as:
- Python 3 Basics to Advanced Level
- Numpy Library and Pandas Library
- Matrices and Linear System of Equations
- Linear Regression with Python Numpy Library
- Matrix Operations using Python Numpy Library
- Gaussian Elimination
- Reduced Echelon Form and RREF
- Matrix Algebra
- Special Matrices, Diagonal Matrices, and Inverse Matrices
- Inverse Matrices and The Inverses of Transposed Matrices
- Determinants and computing the Determinant
- Much more!
By the end of this course, you should very comfortable with Python, Linear algebra, and be able to follow throw any Math which uses the Linear Algebra notation in Machine learning algorithms.
You will also get answers to any questions that you might have for life.
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Who this course is for:
- Computer Science Students Who Want To Learn More Linear Algebra
- Students Who Want To Learn The Linear Algebra For Machine Learning and Deep Learning
- Anyone Who Is Interested In Math And Wants To Study Linear Algebra
- Data scientists who want to review their linear algebra
- Anyone Who wants to learn Python for Data Science
Source: https://www.udemy.com/course/linear-algebra-for-data-science-machine-learning-in-python/
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