Deep Learning Prerequisites: The Numpy Stack in Python
The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligenceDetails
Lecture: 35Time Required: 3.5 hour
Downloadable Resources: 0
Access: Life Time
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Certificate of Completion
Requirements
Understand linear algebra and the Gaussian distributionBe 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
What you will learn
Understand supervised machine learning (classification and regression) with real-world examples using Scikit-LearnUnderstand 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!
Targeted Audience
Students and professionals with little Numpy experience who plan to learn deep learning and machine learning laterStudents and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code
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