The Theoretical and Practical Foundations of Machine Learning. Master Matrices, Linear Algebra, and Tensors in Python.

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.

https://www.udemy.com/course/machine-learning-data-science-foundations-masterclass/

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