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Video TutorialsMachine Learning with Regression in Python: With Ordinary Least Squares, Ridge, Decision Trees and Neural Networks



Machine Learning with Regression in Python: With Ordinary Least Squares, Ridge, Decision Trees and Neural Networks
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 10 Lessons (44m) | Size: 180.5 MB

In this video, you will learn regression techniques in Python using ordinary least squares, ridge, lasso, decision trees, and neural networks.


We start by exploring a census dataset that captures sales from a business in various counties across the United States. We briefly explore the dataset before moving onto model assumptions and feature eeering. We then implement a linear regression, which is a simple model that is easy to interpret, then move through more complex models to see what best makes predictions on our dataset. To avoid overfitting, we split our dataset and to optimize predictions, we tune hyperparameters with k-folds cross validation. We move through models that are more complex until we arrive at a neural network model. We then use the model with the lowest error metrics on the test dataset and make predictions on a new dataset. Using these predictions, we make a recommendation to the company's shareholders who want to expand the business about which counties to expand to next.

This modeling process will be done in Python 3 on a Jupyter notebook, so it's a good idea to have Anaconda installed on your computer so you can follow along. We will structure our notebook to be easy-to-read by others on our team who may want to expand on our analysis.

Explore a dataset with Pandas

Transform variables in a dataset to account for non-linearities and optimize predictions

Tune model hyperparameters and score model performance to detee the best model for a given dataset

Use statistical modeling to make recommendations to shareholders

Software professionals with knowledge of Python basics and data scientists looking to apply data science to industry.



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