Level Up: Python Data Modeling and Model Evaluation Metrics

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01 - Introduction

01 - Python data modeling.mp4

00:33

02 - Using GitHub Codespaces with this course.mp4

06:01

02 - 1. Model Evaluation Metrics

01 - Calculating accuracy.mp4

02:04

02 - Calculating an F-score and MCC.mp4

03:06

03 - Evaluating ROC curves.mp4

03:09

04 - Calculating RMSE and MAE.mp4

03:02

03 - 2. Modeling

01 - Imputing missing values.mp4

03:17

02 - Balancing data.mp4

04:16

03 - Partitioning data.mp4

03:06

04 - Saving data for models.mp4

02:56

05 - Tuning your models.mp4

03:51

06 - Using linear regression.mp4

03:53

07 - Using logistic regression.mp4

04:01

08 - Using decision trees.mp4

03:25

09 - Using random forest.mp4

03:28

10 - Using XGBoost and SHAP plots.mp4

03:57

11 - Classification with deep neural networks.mp4

04:17

12 - Saving and deploying models.mp4

03:36

Description

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace.

Each installment of the Level Up series offers at least 15 bite-sized opportunities to practice programming at various levels of difficulty, so you can challenge yourself and reinforce what you’ve learned. Check out the “Using GitHub Codespaces with this course” video to learn how to get a codespace up and running.

In this course, instructor Seth Berry presents 20 Python challenges, starting with a test of basic skills and moving on to more complex tests of your knowledge. Each video is self-contained, so you can pick and choose which challenges you want to try. Explore these practical exercises to work on your coding skills!

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Python

Data Modeling

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