Mathematical Foundations of Machine Learning, Essential Linear Algebra Hands-On in NumPy, TensorFlow, and PyTorch
- Created by Dr Jon Krohn, Ligency Team
- English [Auto]
Description
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.
Who this course is for:
You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities
You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline
You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)