Added resources

Author: josenino95

episodes/07-bootstrapping.md

@@ -41,7 +41,7 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.utils import resample
-from sklearn.metrics import accuracy_score
+from sklearn import metrics
 
 # Convert outcome to categorical type
 categories = ['ALIVE', 'EXPIRED']
@@ -83,12 +83,12 @@ for i in range(n_iterations):
 
     # Logistic Regression predictions
     y_hat_logreg = logreg.predict(X_bs)
-    score_logreg = accuracy_score(y_bs, y_hat_logreg)
+    score_logreg = metrics.accuracy_score(y_bs, y_hat_logreg)
     accuracy_logreg.append(score_logreg)
 
     # Decision Tree predictions
     y_hat_tree = tree.predict(X_bs)
-    score_tree = accuracy_score(y_bs, y_hat_tree)
+    score_tree = metrics.accuracy_score(y_bs, y_hat_tree)
     accuracy_tree.append(score_tree)
 ```
 
@@ -99,8 +99,8 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 
 # Plot distribution of bootstrap accuracy for both models
-sns.kdeplot(accuracy_logreg, label="Logistic Regression", fill=True, linewidth=2)
-sns.kdeplot(accuracy_tree, label="Decision Tree", fill=True linewidth=2)
+pd.Series(accuracy_logreg).plot.kde(label="Logistic Regression")
+pd.Series(accuracy_tree).plot.kde(label="Decision Tree")
 
 plt.title("Accuracy across 1000 bootstrap samples of the held-out test set")
 plt.xlabel("Accuracy")
@@ -153,8 +153,8 @@ Decision Tree: Median accuracy = 0.80, 95% CI = [0.70, 0.89]
 
 ```python
 # Plot distribution of bootstrap accuracy for both models
-sns.kdeplot(accuracy_logreg, label="Logistic Regression", linewidth=2)
-sns.kdeplot(accuracy_tree, label="Decision Tree", linewidth=2)
+pd.Series(accuracy_logreg).plot.kde(label="Logistic Regression")
+pd.Series(accuracy_tree).plot.kde(label="Decision Tree")
 
 # Plot median and confidence intervals for Logistic Regression
 plt.axvline(median_logreg, linestyle="--", color="red", label="LogReg Median")

learners/reference.md

@@ -2,6 +2,33 @@
 title: 'Resources'
 ---
 
+* For questions after the workshop:
+  - If you have questions about the analysis of the results or the statistics behind
+machine learning models, you can reach out to [PSTAT's DataLab](https://datascience.ucsb.edu/consulting)
+  - For questions about your project or specific questions on your code, you can email 
+the Library's DREAM Lab [[email protected]](mailto:[email protected])
+
+* Depending on the task of your project, you don't have to spend time training ML models from scratch!
+If your project involves text, images, or audio, you'll probably find a model ready to be used on
+[Hugging Face](https://huggingface.co/models)
+
+* For continued self-paced learning, we recommend:
+  - This lesson is [part 1 of 4](https://github.com/carpentries-incubator/machine-learning-novice-python?tab=readme-ov-file#introduction-to-machine-learning-in-python) from a curriculum on The Carpentries Incubator. The other lessons are
+    - Introduction to Tree Models in Python ([Lesson materials](https://carpentries-incubator.github.io/machine-learning-trees-python/))
+    - Introduction to artificial neural networks in Python ([Lesson materials](https://carpentries-incubator.github.io/machine-learning-neural-python/))
+    - Responsible machine learning in Python ([Lesson materials](https://carpentries-incubator.github.io/machine-learning-responsible-python/))
+    
+  - Google's [Intro to ML concepts](https://developers.google.com/machine-learning/intro-to-ml) and [Crash Course](https://developers.google.com/machine-learning/crash-course)
+
+* Online asynchronous courses we've enjoyed and you can audit for free:
+  - [StanfordOnline: Statistical Learning with R](https://www.edx.org/learn/statistics/stanford-university-statistical-learning)
+  - [MITx: Machine Learning with Python: from Linear Models to Deep Learning](https://www.edx.org/learn/data-analysis/massachusetts-institute-of-technology-data-analysis-statistical-modeling-and-computation-in-applications)
+
+* Other Python resources:
+  - [The Hitchhiker’s Guide to Python](https://docs.python-guide.org/)
+  - [Scalable and Computationally Reproducible Approaches to Arctic Research](https://learning.nceas.ucsb.edu/2024-03-arctic/)
+
+
 ## Glossary
 
 FIXME