This talk (video here) was for a Data Science Go (DSGO) Virtual Workshop on October 25, 2020.
Topic | Resources | |
---|---|---|
1 | Introduction | Introduction.pdf |
2 | Setup python (Anaconda or Colab) | Setup |
3 | How to format data for scikit-learn | Format_Data_For_Machine_Learning.ipynb |
4 | Linear regression using scikit-learn | LinearRegression.ipynb |
5 | Train test split | TrainTestSplit.ipynb, BostonSplit.ipynb |
6 | Decision trees for classification | DecisionTreesClassification.ipynb |
7 | Decision trees for regression | DecisionTreesRegression.ipynb |
8 | Visualize decision trees using Python | DecisionTreesVisualization.ipynb |
9 | Bagged trees using Python | BaggedTrees.ipynb |
10 | Random Forests using Python | RandomForests.ipynb |
11 | Logistic Regression using Python | LogisticRegression.ipynb, LogisticOneVsAll.ipynb |
12 | Conclusion | Conclusion.pdf |
13 | Bonus Content (not in presentation) | How to Speed Up Scikit-Learn Model Training |