Mathematical Foundations of Machine Learning

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 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

1. Linear Algebra Data Structures

2. Tensor Operations

3. Matrix Properties

4. Eigenvectors and Eigenvalues

5. Matrix Operations for Machine Learning

6. Limits

7. Derivatives and Differentiation

8. Automatic Differentiation

9. Partial-Derivative Calculus

10. Integral 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!

This Mathematical Foundations of Machine Learning course is complete, but in the future, we intend on adding extra 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.

What You Will 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

Who Should Attend!

• 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!)

TAKE THIS COURSE