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 machinelearning 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 cuttingedge machine learning papers
 Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning
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 highlevel libraries like Scikitlearn 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.
Course Sections

Linear Algebra Data Structures

Tensor Operations

Matrix Properties

Eigenvectors and Eigenvalues

Matrix Operations for Machine Learning

Limits

Derivatives and Differentiation

Automatic Differentiation

PartialDerivative Calculus

Integral Calculus
Throughout each of the sections, you’ll find plenty of handson assignments, Python code demos, and practical exercises to get your math game in top form!
This Mathematical Foundations of MachineLearning 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.
Are you ready to become an outstanding data scientist? See you in the classroom.