Programming is a great way to get practical insights about math theoretical concepts. The goal of this session is to show you that you can start learning the math needed for machine learning and data science using code. You'll learn about scalars, vectors, matrices and tensors, and see how to use linear algebra on your data. Don't worry if you don't have a math background, we'll explain the mathematical notations and conventions. At the end of the session, you'll know how to operate on vectors, matrices and tensors, use the norm of vectors, and apply the dot product to vectors. You'll also see more advanced concepts like matrices as linear transformations, linear combinations, basis, and how to use matrices to express systems of equations.


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Training Overview

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    ODSC Europe 2020: Introduction to Linear Algebra for Data Science and Machine Learning With Python

    • Training Overview and Author Bio

    • Before you get started: Prerequisites and Resources

    • Introduction to Linear Algebra for Data Science and Machine Learning With Python

Instructor Bio:

Hadrien Jean

Data and Machine Learning Scientist

Hadrien Jean, PhD

Hadrien Jean is a machine learning scientist. He's currently working on the book "Essential Math for Data Science" with O'Reilly (https://learning.oreilly.com/library/view/essential-math-for/9781098115555/). He previously worked at Ava on speech diarization. He also works on a bird detection project using deep learning. He completed his Ph.D. in cognitive science at the École Normale Supérieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https://hadrienj.github.io/posts/).