Deep Learning Prerequisites: The Numpy Stack in Python
The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence
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158,352 students enrolled
Created by Lazy Programmer Inc.
English
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!
- 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
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
- linear algebra
- probability
- Python coding: if/else, loops, lists, dicts, sets
- 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
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
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