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Mathematical Foundations of Machine Learning

machine-learning-data-science-foundations-masterclass

Mathematical Foundations of Machine Learning, Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch

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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

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 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.

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!)

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