Mathematical Foundations of Machine Learning

Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch

Created by Dr Jon Krohn, SuperDataScience Team, Ligency Team | 16.5 hours on-demand video course

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 increase 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 linear algebra and calculus — that underlies machine learning algorithms and data science models.

This Mathematical Foundations of Machine Learning course is complete, but in the future, we intend on adding bonus content from related subjects beyond math, namely: probability, statistics, data structures, algorithms, and optimization. Enrollment now includes free, unlimited access to all of this future course content — over 25 hours in total.

What you’ll learn

• Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science
• 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)
• Appreciate how calculus works, from first principles, via interactive code demos in Python
• Intimately understand advanced differentiation rules like the chain rule
• Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch
• Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent
• Use integral calculus to determine the area under any given curve
• Be able to more intimately grasp the details of cutting-edge machine learning papers
• Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning

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