Machine Learning & Data Science Foundations Masterclass - Freewebcart
Machine Learning & Data Science Foundations Masterclass The Theoretical and Practical Foundations of Machine Learning. Master Matrices, Linear Algebra, and
Course Provider: Organization
Course Provider Name: Jon Krohn
Course Provider URL: https://www.udemy.com/user/jon-krohn/
Machine Learning & Data Science Foundations Masterclass The Theoretical and Practical Foundations of Machine Learning. Master Matrices, Linear Algebra, and Tensors in Python.
What you’ll learn
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
Machine Learning & Data Science Foundations Masterclass Description
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
(Note that, as this is initially being offered as a free course, Udemy limits us to two hours of videos so we only get halfway through Section 2. For a sneak preview of what’s to come in the remaining sections, check out the course’s code repository in GitHub — a link is provided in the videos.)
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.