Mathematical Foundations of Machine Learning

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

To be an excellent data scientist, you need to know how those libraries and algorithms work under the hood. This is where our "Machine Learning & Data Science Foundations Masterclass" comes in.

Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the underlying mathematics, such as linear algebra, tensors, and eigenvectors, that operate behind the most important Python libraries, machine learning algorithms, and data science models.

The first steps in your journey into becoming an excellent data scientist are 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

While the above sections constitute a standalone, introductory course on linear algebra all on their own, we're not stopping there! We have finished filming additional content on calculus (Sections 6 through 10), which will be edited and uploaded in Spring 2021. We will release all remaining sections of the comprehensive Machine Learning Foundations series into the course as quickly as we can. Ultimately, the course will cover not only linear algebra and calculus, but also probability, statistics, data structures, and optimization. Enrollment now includes free, unlimited access to all of this future course content -- over 25 hours in total.

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

Are you ready to become an outstanding data scientist? See you in the classroom.