Machine Learning & Data Science Foundations Masterclass
The Theoretical and Practical Foundations of Machine Learning. Master Matrices, Linear Algebra, and Tensors in Python
What you’ll learn
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Understand the fundamentals of linear algebra, a ubiquitous approach for solving for unknowns within high-dimensional spaces.
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Manipulate tensors using the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
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Possess an in-depth understanding of matrices, including their properties, key classes, and critical ML operations
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Develop a geometric intuition of what’s going on beneath the hood of ML and deep learning algorithms.
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Be able to more intimately grasp the details of cutting-edge machine learning papers
Requirements
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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.
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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.
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!)