Beginner - Expert Linear Algebra, with Practice in Python | Udemy
Beginner - Expert Linear Algebra, with Practice in Python For two days completely 100% Free during everything you want to Master Linear Algebra, with clear and concise explanations, Practical Examples in various domains like Machine Learning.
This course is written by the very popular author from Udemy Neuralearn Dot AI The most recent update was August 3, 2021.The language of this course is English 🇺🇸, but also has subtitles (captions) in English [US] languages to better understand. This course is shared under the categories.....
More than 200,130 students had already enrolled. in the Beginner - Expert Linear Algebra, with Practice in Python | Udemy Which makes it one of the more popular courses on Udemy. You can free coupon Code the course from the registration link below. It has a rating of 4.2 given by (109 ratings), which also makes it one of the highest-rated courses at Udemy.
The Udemy Beginner - Expert Linear Algebra, with Practice in Python free coupons also 4 hours on-demand video, 3 articles, 10 downloadable, resources, full lifetime, access on mobile and television, assignments, completion certificate and many more.
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Is this course right for you?
If you are wondering what you will learn or what things this best Udemy courses will teach you after getting courses Udemy free coupon. Learn Beginner - Expert Linear Algebra, with Practice in Python | Udemy: Okay, here are a few things.
- Computer scientists, who want to gain a solid foundation in linear algebra and apply it in solving computer related problems
- Data scientists and Machine Learning Practitioners or Learners, who want to gain a solid foundation in linear algebra and apply it in solving problems in data science and machine learning.
- Mathematics students, who want to gain a solid foundation in linear algebra and apply it in their mathematics courses.
- Finance experts, who want to gain a solid foundation in linear algebra and apply it in solving real world problems
- Engineers and Engineering students, who want to gain a solid foundation in linear algebra and use it solving engineering related problems.
Requirements Course:
- Basic Algebra.
- No programming Experience Needed.
Description Course:
- Fundamentals of Linear Algebra
- Operations on a single Matrix
- Operations on two or more Matrices
- Performing Elementary row operations
- Finding Matrix Inverse
- Gaussian Elimination Method
- Vectors and Vector Spaces
- Fundamental Subspaces
- Matrix Decompositions
- Matrix Determinant and the trace operator
- Core Linear Algebra concepts used in Machine Learning and Datascience
- Hands on experience with applying Linear Algebra concepts using the computer with the Python Programming Language
- Apply Linear Algebra in real world problems
- Skills needed to pass any Linear Algebra exam
- Principal Component Analysis
- Linear Regression
- Lifetime access to This Course
- Friendly and Prompt support in the Q&A section
- Udemy Certificate of Completion available for download
- 30-day money back guarantee
- Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.
- Anyone who wants to master deep learning fundamentals and also practice deep learning using best practices in TensorFlow.
- Deep Learning Practitioners who want gain a mastery of how things work under the hood.
- Beginner Python Developers curious about Deep Learning.
What I am going to learn?
- Understanding Matrix Algebra and applying it in solving linear equations and transformations, with practical examples in Python.
- Mastering vectors, vector properties, vector spaces, sub spaces and application in coordinate systems. Fundamental sub spaces and how they can be computed.
- Mastery of Orthogonal and Orthonormal vectors and orthogonal projections. Then computing minimal distances & Gram Schmidt orthogonalization.
- Matrix Decompositions like eigen, cholesky and singular value decompositions. Mastery of Diagonalization, full rank approximation and low rank approximation.
- Matrix inverses, least square and normal equation. Linear Regression and Kaggle House Prediction Practice.
- Explaining and deducing Principal Component Analysis (PCA) from scratch and applying it to face recognition using the Eigen Faces algorithm.
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