Python-Machine-Learning
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dlab-berkeley
D-Lab's 6 hour introduction to machine learning in Python. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn in Python.
1. I've noticed that the number of algorithms introduced in the classification and clustering sections is much less than in regression. For consistency, I would suggest including more examples of...
In the formula, instead of saying true positive over conditioned positives, Jose wanted it to be true positive + false negatives.
The seaborn package requires installation, but it is not mentioned in the notebooks.
Notebook 3 challenge 1 seems to have confusing language about fit and we wanna say we are precorssing the whole dataset. (Jose knows better)
The correlation didn't work for some participants because of their version and the fact that its default wasn't numeric_only=True.
Notebook 1 (regression): 1. data.corr() needs to be replaced by data.corr(numeric_only = True) 2. mean_squared_error(y_true,y_pred,squared = False) needs to be replaced by root_mean_squared_error. This includes importing the function, and replacing...
**ISSUE**: `sparse` is `sparse_output` for newer versions of sklearn's `OneHotEncoder` **LOCATION**: * [03_preprocessing notebook](https://github.com/dlab-berkeley/Python-Machine-Learning/blob/main/lessons/03_preprocessing.ipynb) * Code block in "Categorical Variable Encoding (One-hot & Dummy)" section **PROPOSED SOLUTION**: Replace `sparse` with...
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D-Lab's 6 hour introduction to machine learning in Python. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn in Python.