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>Setup | FormatDataForMachineLearning.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 |