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# Mathematical Foundations of Machine Learning

## Mathematical Foundations of Machine Learning

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

#### What you’ll learn

Mathematical Foundations of Machine Learning

• Understand the principles of linear algebra and calculus, which are key mathematical concepts in machine learning and data analytics.
• Use all three of the most important Python tensor libraries to manipulate tensors: NumPy, TensorFlow, and PyTorch are three Python libraries.
• How to use all of the necessary vector and matrix operations in machine learning and data science.
• Using eigenvectors, SVD, and PCA, reduce the dimensionality of complex data to the most informative elements.
• Solve for unknowns using both simple and complicated strategies (e.g., elimination) (e.g., pseudo inversion)
• Learn how calculus works from the ground up with interactive Python code samples.
• Understand complex differentiation principles such as the chain rule in depth.
• Calculate the partial derivatives of machine-learning cost functions using TensorFlow and PyTorch as well as by hand.
• Understand what gradients are and why they are necessary for ML to work via gradient descent.
• Calculate the area under any curve using integral calculus.
• To be able to grasp the details of cutting-edge machine learning publications in greater depth.
• Develop a grasp of what goes on behind the scenes of machine learning algorithms, especially deep learning algorithms.

#### Requirements

• Because all of the code demos will be in Python or another object-oriented programming language, prior expertise with it or another object-oriented programming language will be beneficial in following along with the hands-on examples.
• It will be simpler to follow along with the class if you are familiar with secondary school mathematics. You should be able to follow along with all of the mathematics if you are familiar with dealing with quantitative information, such as comprehending charts and rearranging simple equations.

#### Description

Data science and machine learning are based on mathematics. As a result, in order to be the finest data scientist possible, you must have a solid knowledge of the most important arithmetic.

Thanks to high-level tools like Scikit-learn and Keras, getting started in data science is simple.

Understanding the mathematics behind the algorithms in these libraries, on the other hand, offers up an unlimited number of options. Understanding the arithmetic behind it all may greatly boost the effect you can have over the course of your career, from spotting modeling flaws to designing new and more powerful solutions.

This course, led by deep learning pioneer Dr. Jon Krohn, gives a solid understanding of the mathematics that underpins machine learning algorithms and data science models – specifically, linear algebra and calculus.

#### Sections of the Course

1. Data Structures in Linear Algebra
2. Tensor Operations are operations on tensors.
3. Matrix Characteristics
4. Eigenvalues and Eigenvectors
5. Machine Learning Matrix Operations
6. Limits
7. Differentiation and Derivatives
8. Differentiation by Machine
9. Calculus of partial derivatives
10. Calculus of Integrals

You’ll discover plenty of hands-on assignments, Python code demos, and practical exercises throughout each lesson to help you improve your math skills!

This course on Mathematical Foundations of Machine Learning is complete, but we want to add supplementary information from relevant areas outside of math in the future, such as probability, statistics, data structures, algorithms, and optimization.

Are you prepared to excel as a data scientist? I’ll see you in class.

#### Who this course is for:

• You train or deploy machine learning algorithms using high-level software libraries (e.g., scikit-learn, Keras, TensorFlow), and you’d like to understand the principles underlying the abstractions so that you can expand your skills.
• You’re a software developer who wants to lay the groundwork for integrating machine learning algorithms into production systems.
• Who data scientist and who wants to improve your knowledge of the topics that are at the heart of your career.
• You’re a data analyst or A.I. enthusiast who wants to work as a data scientist or data/ML engineer, and you’re eager to learn everything there is to know about the area (which is wise!).