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83% Off Mathematical Foundations of Machine Learning | Udemy Review & Coupon

83% Off Mathematical Foundations of Machine Learning | Udemy Review & Coupon

The course includes teachings on Linear Algebra and Calculus, with a focus on practical applications using NumPy, TensorFlow, and PyTorch.

This course covers:

This course on Mathematical Foundations of Machine Learning provides 16.5 hour ondemand video course designed to provide an in-depth exploration of the mathematical foundations behind machine learning algorithms. It covers topics such as linear algebra, calculus, probability theory, optimization, numerical analysis, and more. The course includes 1 article for further reading and research purposes. Additionally, it provides full lifetime access on mobile and TV , a certificate of completion, and a 30-day money-back guarantee.

What you'll learn

The Mathematical Foundations of Machine Learning course explores the fundamental topics in linear algebra and calculus that are essential for creating accurate and effective machine learning systems, as well as data science. Learners will be able to manipulate tensors using relevant Python libraries, such as NumPy, TensorFlow, and PyTorch. Not only will learners gain an understanding of various vector and matrix operations for machine learning and data science projects, they’ll also learn how to reduce dimensionality with techniques like eigenvectors, SVD, and PCA. Understanding all of these concepts is a prerequisite for solving unknowns using simple techniques like elimination and more advanced approaches like pseudoinversion.

This course provides invaluable insight into the structure of knowledge-based applications such as neural networks, which operate by processing data points derived from real-world observations. By demystifying the mathematics behind the often intimidatingly complex deep learning process of ML algorithms – featured heavily in industry big-data configurations – this course promises to provide students with an indispensable footing in precisely constructing efficient hardware implementations so that self-learning machines are capable of outperforming humans at particular difficulties tasks by many orders of magnitude.

About Instructor

Jon Krohn is a highly esteemed instructor in the field of machine learning and data science. He is the Chief Data Scientist at untapt, as well as the author of Deep Learning Illustrated, a best-selling book that has been translated into several languages. Jon has offered lectures in person at Columbia University and New York University, and also offers online courses through O’Reilly Learning Platforms and the SuperDataScience podcast.

In addition to his educational experience, Jon also holds a prestigious PhD from Oxford University. To date, he has published multiple papers on machine learning to multiple esteemed academic journals – and his papers continue to be cited by followers around the globe. His strong background in academia, combined with his engaging teaching style make him one of today’s most sought-after instructors in his field.


Having a thorough knowledge of mathematics is essential for data science and machine learning. A working understanding of the different mathematical concepts can open many possibilities for aspiring data scientists, ranging from identifying modeling issues to inventing new solutions. Scikit-learn and Keras provide high-level libraries that make getting started in data science a breeze; however, having a deeper conceptual understanding of the underlying math unlocks further potentials and boosts one's growth in the field. Led by expert deep learning guru Dr. Jon Krohn, this course provides invaluable insight into key topics such as linear algebra, probability theory, statistics, calculus with examples from the real world to heighten ones comprehension.

Innovative algorithmic techniques can be gained from understanding these concepts - especially useful when facing complex ML challenges. Possibilities include tackling computer vision classification issues or producing superior models through creative applications of mathematics which can deepen one's expanse in various areas related to AI and ML. From developing linear regression models using matrix multiplication algorithms to applying derivatives for deep learning model optimization—the opportunities provided by mathematics are near endless — elevating any budding data scientist’s arsenal significantly; no matter their goals or aspirations!

This course is intended for:

This course is designed for individuals who wish to gain a strong knowledge foundation in the fundamentals of machine learning. It is ideal for software developers and data scientists who would like to understand the underlying abstractions which drive machine learning algorithms. Through this course, participants will learn how to apply these tools with greater proficiency in order to expand their capabilities in utilizing big data and predictive analytics. Data analysts and AI enthusiasts wishing to enter the field of data science/machine learning engineering will also find this course highly beneficial as they receive an introduction into those topics from the ground-up.

The course curriculum focuses on essential topics such as supervised vs unsupervised learning algorithms, deep-learning techniques, neural networks, natural language processing, model evaluation & optimization and exploiting various machine learning models within production systems & cloud computing environments. With its comprehensive scope, participants can expect to gain a greater insight into how machine learning theory is applied practically through established industry frameworks & libraries such as scikit-learn, TensorFlow and Keras. By engaging each topic in depth along with intuitive examples, attendees can expect to be armed with the theoretical understanding and skillset required for success in integrating big data sets with industry standard solutions.


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Customer Reviews

Based on 2 reviews
The most suitable fundamental mathematics course for machine learning.

Understanding the applications of calculus and linear algebra in machine learning has proven to be useful. I would like to see additional courses offered, such as computer science and deep learning.

Jon Krohn is an excellent guide through this very complicated material.

The instructor provides both theoretical explanations and practical demonstrations of applying the concepts using Numpy, PyTorch, and Tensor Flow.

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