On Education Deep Learning Prerequisites: The Numpy Stack in Python - all courses

Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn Understand and code using the Numpy stack Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More! 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 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.