Deep Learning Prerequisites: The Numpy Stack in Python (V2+)
The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence
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.
What you’ll learn
- 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!
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