Mathematical Foundations of Machine Learning

What you’ll learn

• Understand the fundamentals of linear algebra, a critical subject underlying all ML algorithms and data science models
• Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
• How to apply all of the essential vector and matrix operations for machine learning and data science
• Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA
• Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion)
• Be able to more intimately grasp the details of cutting-edge machine learning papers

Requirements

• All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.
• Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.

Description
Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math.

Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increasing the impact you can make over the course of your career.

Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics — namely the linear algebra and calculus — that underlies machine learning algorithms and data science models.

Course Sections

• Linear Algebra Data Structures
• Tensor Operations
• Matrix Properties
• Eigenvectors and Eigenvalues
• Matrix Operations for Machine Learning
• Limits
• Derivatives and Differentiation
• Automatic Differentiation
• Partial-Derivative Calculus

Throughout each of the sections, you’ll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game in top form!

We have finished filming a final, tenth section on calculus, which will be edited and uploaded in the summer of 2021. At that point, this Mathematical Foundations of Machine Learning course is complete, but in the future we intend on bonus content from related subjects — probability, statistics, data structures, and optimization. Enrollment now includes free, unlimited access to all of this future course content — over 25 hours in total.

Course Prerequisites

Programming: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the code examples.

Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.

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!)