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Deep Learning Prerequisites: The Numpy Stack in Python

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Deep Learning Prerequisites: The Numpy Stack in Python

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence

Details

Lecture: 35
Time Required: 3.5 hour
Downloadable Resources: 0
Access: Life Time
Access on mobile and TV
Certificate of Completion

Requirements

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

What you will 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!

Targeted Audience

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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