Mathematical Foundations of Machine Learning
Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch
4.59 (7152 reviews)
132,421
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16.5 hours
content
Nov 2024
last update
$119.99
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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
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3501832
udemy ID
9/15/2020
course created date
10/19/2020
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