Mathematical Foundations of Machine Learning

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

4.60 (7248 reviews)

Udemy

platform

English

language

Data Science

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

Course Gallery

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Our Verdict

Mathematical Foundations of Machine Learning on Udemy presents a mostly well-rounded and accessible exploration of linear algebra and calculus essentials, boasting hands-on code examples and simple explanations. However, the course seems to be affected by potential inconsistencies in content completeness and pacing, leaving somewhat of a disjointed learning experience for some students—despite the overall positive response and valuable insights gained.

What We Liked

Comprehensive coverage of linear algebra and calculus fundamentals critical for machine learning
Hands-on code demonstrations using NumPy, TensorFlow, PyTorch, making concepts more tangible
Pacing and simplification of complex ideas make it accessible for learners without a strong math background
Explains calculus from first principles through interactive Python demos, strengthening understanding

Potential Drawbacks

Concerns regarding course content being incomplete or distributed across platforms, affecting comprehensiveness
Some linear algebra concepts seem rushed and may require additional resources for clarification
A few reviewers expressed the need for more theoretical exercises to reinforce understanding and retention