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Mathematical Foundations of Machine Learning

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Mathematical Foundations of Machine Learning Udemy Coupon

Mathematical Foundations of Machine Learning

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

Created by Dr Jon Krohn, SuperDataScience Team, Ligency Team | 16.5 hours on-demand video course

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.

What you’ll 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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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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