Description
Linear Algebra for Machine Learning is a training course on the application of linear algebra in data science and machine learning, published by the Informit Academy. In this training course, you will get acquainted with the theoretical and practical issues of linear algebra and you will implement it in a completely practical way in projects related to machine learning. Machine learning and data science are two of the most widely used disciplines in today’s digital world, and learning them can bring you many career opportunities.
What you will learn in Linear Algebra for Machine Learning:
- Familiarity with the application of algebra and the principles of mathematics in the field of machine learning
- Familiarity with the basics of linear algebra
- Familiarity with different approaches to developing machine learning-based solutions
- In-depth understanding of the working process of machine learning-based algorithms
- Improve the skills of mathematical intuition
- In-depth understanding of other topics related to machine learning such as calculus, statistics, optimization algorithms, and…
Course specifications
Publisher: InformIT
Instructor: Jon Krohn
Language: English
Level: Medium
Courses: 58
Duration: 6 hours and 32 minutes
Course topics
Lesson 1: Orientation to Linear Algebra
Lesson 2: Data Structures for Algebra
Lesson 3: Common Tensor Operations
Lesson 4: Solving Linear Systems
Lesson 5: Matrix Multiplication
Lesson 6: Special Matrices and Matrix Operations
Lesson 7: Eigenvectors and Eigenvalues
Lesson 8: Matrix Determinants and Decomposition
Lesson 9: Machine Learning with Linear Algebra
Prerequisites for Linear Algebra for Machine Learning
Mathematics: Familiarity with secondary school-level mathematics will make the course easier to follow. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.
Programming: All code demos are in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.
Course pictures
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 720p
Course side files: Github
download link
Download Part 1 – 2 GB
Download Part 2 – 2 GB
Download Section 3 – 2 GB
Download section 4 – 2 GB
Download section 5 – 2 GB
Download Section 6 – 1.54 GB
file password link
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