Python Machine Learning with Scikit Learn - Regression || Python Machine Learning PT.3
The modelling phase of the data science lifecycle is without a doubt the most fun part, it's where you get to select a bunch of algorithms and train them to detect patterns within your data.
If you want, you can take it one step further and begin to automate some of the decisions that traditionally as a data scientist you'd have to make. In this video, we'll go through how to you can take the guess work out of selecting the right machine learning algorithm using Python Scikit Learn.
In this video you'll learn how to:
Train Python machine learning models with Scikit Learn
Perform automated hyperparameter tuning using GridSearchCV
Evaluate model performance with sklearn.metrics
Get the dataset in the GitHub repository: https://github.com/nicknochnack/Sciki...
Stuff mentioned in the video:
Ridge: https://scikit-learn.org/stable/modul...
Lasso: https://scikit-learn.org/stable/modul...
ElasticNet: https://scikit-learn.org/stable/modul...
RandomForestRegressor: https://scikit-learn.org/stable/modul...
GradientBoostingRegressor: https://scikit-learn.org/stable/modul...
GridSearchCV: https://scikit-learn.org/stable/modul...
Oh, and don't forget to connect with me!
LinkedIn: / nicholasrenotte
Facebook: / nickrenotte
GitHub: https://github.com/nicknochnack
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
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