Data Science and Machine Learning Series: Linear Algebra Made Simple

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 15m | 287 MB

Become proficient in linear algebra in this course in the Data Science and Machine Learning Series. Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to practice applying linear algebra concepts.

The following ten topics will be covered in this Data Science and Machine Learning course:

• Introducing Linear Algebra. Understand linear algebra concepts in this first topic in the Data Science and Machine Learning Series. Linear algebra is a continuous form of mathematics that allows us to model natural phenomena and compute them efficiently. Functional analysis is the application of linear algebra to spaces of functions. Be able to explain vectors which are ordered lists of numbers. Perform vector addition and multiplication.
• Creating Linear Transformations, Span, and Basis Vectors. Create linear transformations, span, and basis vectors in this second topic within this linear algebra course in the Data Science and Machine Learning Series.
• Using Linear Transformations and Matrices. Use linear transformations and matrices in this third topic within this linear algebra course in the Data Science and Machine Learning Series. See how linear transformations look in two dimensions and practice more advanced vector multiplication.
• Using Linear Transformations as Composition. Use linear transformations as composition in this fourth topic within this linear algebra course in the Data Science and Machine Learning Series. Practice matrix multiplication as composition including the use of the Shear Transformation. Apply transformations in a particular sequence.
• Creating Matrix Determinants. Create matrix determinants in this fifth topic within this linear algebra course in the Data Science and Machine Learning Series. The determinant is the scaling factor by which a linear transformation changes the area of any shape.
• Mastering Inverse Matrices, Linear Systems of Equations, Rank, Column Spaces, and Null Spaces. Master inverse matrices, linear systems of equations, rank, column spaces, and Null Spaces in this sixth topic within this linear algebra course in the Data Science and Machine Learning Series.
• Using Dot Products and Duality. Know all about dot products and duality in this seventh topic within this linear algebra course in the Data Science and Machine Learning Series.
• Practicing the Cross Product. Practice the cross product and their physical representations in this eighth topic within this linear algebra course in the Data Science and Machine Learning Series.
• Changing Basis Vectors. Change basis vectors in this ninth topic within this linear algebra course in the Data Science and Machine Learning Series.
• Applying Eigenvalues and Eigenvectors. Apply eigenvalues and eigenvectors in this tenth topic within this linear algebra course in the Data Science and Machine Learning Series.