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 first entry in the Machine Learning Foundations series 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 models, 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
While the above sections constitute a standalone course all on their own, we're not stopping there! We have finished filming additional, intermediate-level linear algebra content (the remainder of Section 4 on Eigenvectors and Eigenvalues and Section 5 on Matrix Operations for Machine Learning). It will all be edited and uploaded in early 2021. Within 2021, we will release all remaining sections of the comprehensive Machine Learning Foundations series, which covers not only linear algebra, but also calculus, probability, statistics, algorithms, 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.