MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 753 MB | Duration: 3h 17m
Familiarity with secondary-school-level mathematics.
What you'll learn
The applications to Machine Learning
The Fundamentals of Linear Algebra
Operations on a single matrices and multiple matrices
How to perform elementary row operations
Learn how to find the inverse Matrix
Learn how to solve systems of linear equations
Understand matrices as vectors and vector spaces
Will also study Linear combinations and span
As well as subspaces, null-space, basis, standard basis and more
Requirements
Ability to perform basic mathematical operations on numbers and fractions.
Knowledge of how to solve linear equations.
Understanding of basic algebra concepts.
Description
Good data scientists are familiar with machine learning libraries and algorithms. It is akin to being an amazing pilot of an airplane, with skills that go beyond flying and borders an airplane mechanic. But to be a great data scientist, those skills will have to surpass the mechanics and thus require a greater understanding.
The great data scientist knows how those libraries and algorithms work under the hood. The great data scientist understands the mathematics behind the science. With the speed of technology, there may come a day when the algorithm itself replaces the data scientist. If we look at our original analogy, this would be akin to planes that truly fly themselves.
We are not there yet, but in this scenario the pilot becomes expensive and obsolete. However, the one person who is never obsolete is the eeer who designs the plane or the mechanic who fixes the plane. Linear Algebra is a cornerstone of machine learning. Linear Algebra not only helps improve an intuitive understanding of Machine learning. But Linear Algebra can help the machine learning eeer build better Machine Learning algorithms from Scratch or customize the parameters involved to optimize the algorithms. In this course you will learn about the Linear Algebra behind the Machine Learning Algorithm.
Who this course is for:
Students of Machine Learning
Students of Data Science
Students of Statistical Learning
Students of Linear Algebra
Students of Mathematics
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