Mathematical Foundations of Machine Learning
Deal Score+7
Mathematical Foundations of Machine Learning Course
Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch
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.
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!
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Master Math by Coding in Python
Master Math by Coding in Python Course
Use Python to learn algebra, calculus, graphing, trigonometry and more math …
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.
