Description

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.
One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.
Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.
This course is designed to remove that obstacle – to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.


Who this course is for:

    Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later
    Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code

Requirements

    Understand linear algebra and the Gaussian distribution
    Be comfortable with coding in Python
    You should already know “why” things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for

Last updated 10/2018