4.48 out of 5
4.48
748 reviews on Udemy

Machine Learning & Data Science Foundations Masterclass

The Theoretical and Practical Foundations of Machine Learning. Master Matrices, Linear Algebra, and Tensors in Python
Instructor:
Jon Krohn
43,776 students enrolled
English [Auto]
Understand the fundamentals of linear algebra, a ubiquitous approach for solving for unknowns within high-dimensional spaces.
Manipulate tensors using the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
Possess an in-depth understanding of matrices, including their properties, key classes, and critical ML operations
Develop a geometric intuition of what’s going on beneath the hood of ML and deep learning algorithms.
Be able to more intimately grasp the details of cutting-edge machine learning papers

To be a good data scientist, you need to know how to use data science and machine learning libraries and algorithms, such as NumPy, TensorFlow and PyTorch, to solve whichever problem you have at hand.

To be an excellent data scientist, you need to know how those libraries and algorithms work.

This is where our course “Machine Learning & Data Science Foundations Masterclass” comes in. Led by deep learning guru Dr. Jon Krohn, this first entry in the Machine Learning Foundations series will give you the basics of the mathematics such as linear algebra, matrices and tensor manipulation, that operate behind the most important Python libraries and machine learning and data science algorithms.

The first step in your journey into becoming an excellent data scientist is broken down as follows:

  • Section 1: Linear Algebra Data Structures

  • Section 2: Tensor Operations

  • Section 3: Matrix Properties

  • Section 4: Eigenvectors and Eigenvalues

  • Section 5: Matrix Operations for Machine Learning

Throughout each of the sections, you’ll find plenty of hands-on assignments and practical exercises to get your math game up to speed!

Are you ready to become an excellent data scientist? Enroll now!

See you in the classroom.

Data Structures for Algebra

1
Introduction
2
What Linear Algebra Is
3
Linear Algebra Exercise
4
Tensors
5
Scalars
6
Vectors and Vector Transposition
7
Norms and Unit Vectors
8
Basis, Orthogonal, and Orthonormal Vectors
9
Matrix Tensors
10
Generic Tensor Notation
11
Exercises on Algebra Data Structures

Common Tensor Operations

1
Segment Intro
2
Tensor Transposition
3
Basic Tensor Arithmetic, incl. the Hadamard Product
4
Tensor Reduction
5
The Dot Product
6
Exercises on Tensor Operations
7
Tensor Substitution
8
Tensor Elimination

Matrix Properties

1
Segment Intro
2
Frobenius Norm
3
Matrix Multiplication
4
Symmetric Matrices
5
Exercises on Matrices
6
Matrix Inversion
7
Diagonal Matrices
8
Orthogonal Matrices
You can view and review the lecture materials indefinitely, like an on-demand channel.
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Course available for 2 days
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Includes

3 hours on-demand video
1 article
Full lifetime access
Access on mobile and TV
Certificate of Completion

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