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Data Science Prerequisites: The Numpy Stack In Python :
Data Science Prerequisites: The Numpy Stack In Python : 
If you’ve taken a deep learning or machine learning course, and you understand the theory, and you can see the code, but you can’t make the connection between how to turn those algorithms into actual running code, this course is for you.
If you know some basic coding, but you want to learn how to visualize data and make plots, create data frames from data files and manipulate data frames, and do scientific calculations like statistical testing, then this course is for you.
If you've taken one of my more advanced courses but found that you didn't understand a lot of the code, then this course is for you.
Goals
- 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!
Prerequisites
- linear algebra
- probability
- Python coding: if/else, loops, lists, dicts, sets
