# Mathematical Foundations of Machine Learning

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

To be a good data scientist, you need to know how to use data science and machine learning libraries and algorithms, such as Scikit-learn, TensorFlow, and PyTorch, to solve whatever problem you have at hand.

What you’ll learn

• Understand the fundamentals of linear algebra, a critical subject underlying all ML algorithms and data science models.
• 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).
• Be able to more intimately grasp the details of cutting-edge machine learning papers.

Course Content

• Data Structures for Linear Algebra –> 12 lectures • 1hr 44min.
• Tensor Operations –> 9 lectures • 55min.
• Matrix Properties –> 9 lectures • 1hr 24min.
• Eigenvectors and Eigenvalues –> 10 lectures • 2hr 12min.
• Matrix Operations for Machine Learning –> 8 lectures • 1hr 15min.
• Limits –> 8 lectures • 1hr 8min.
• Derivatives and Differentiation –> 15 lectures • 1hr 26min.

Requirements

• All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples..
• Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics..

To be a good data scientist, you need to know how to use data science and machine learning libraries and algorithms, such as Scikit-learn, TensorFlow, and PyTorch, to solve whatever problem you have at hand.

To be an excellent data scientist, you need to know how those libraries and algorithms work under the hood. This is where our Mathematical Foundations of Machine Learning comes in.

Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics — namely the linear algebra and calculus — that underlies machine learning algorithms and data science models.

The course is broken down into the following sections:

1. Linear Algebra Data Structures
2. Tensor Operations
3. Matrix Properties
4. Eigenvectors and Eigenvalues
5. Matrix Operations for Machine Learning
6. Limits
7. Derivatives and Differentiation

We have finished filming additional content on calculus (Sections 8 through 10), which will be edited and uploaded by Summer 2021. At that point, the Mathematical Foundations of Machine Learning course could be considered complete, but we will continue adding related bonus content — on probability, statistics, data structures, and optimization — as quickly as we can. Enrollment now includes free, unlimited access to all of this future course content — over 25 hours in total.

Throughout each of the sections, you’ll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game up to speed!

Are you ready to become an outstanding data scientist? See you in the classroom.

Course Prerequisites

Programming: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the code examples.

Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.

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