Homework 1: Introduction to Machine Learning

Score: 9 = 7 (questions) + 1 (FAQ) + 1 (learning in public)

Homework URL: View submission

Learning in public links: Show

• https://github.com/nsriram/ml-zc-2025/blob/main/week1/notes.md

Homework 2: Machine Learning for Regression

Score: 7 = 3 (questions) + 1 (FAQ) + 3 (learning in public)

Homework URL: View submission

Learning in public links: Show

• https://medium.com/@amit25173/calculating-rmse-using-numpy-step-by-step-guide-a6006c9e30d9
• https://www.analyticsvidhya.com/blog/2021/10/evaluation-metric-for-regression-models/
• https://www.datacamp.com/tutorial/rmse

Homework 3: Machine Learning for Classification

Score: 11 = 5 (questions) + 1 (FAQ) + 5 (learning in public)

Homework URL: View submission

Learning in public links: Show

• https://medium.com/@RobuRishabh/logistic-regression-c2d2bac7afd8
• https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
• https://medium.com/data-and-beyond/mastering-exploratory-data-analysis-eda-everything-you-need-to-know-7e3b48d63a95
• https://miykael.github.io/blog/2022/advanced_eda/
• https://developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall

Homework 4: Evaluation Metrics for Classification

Score: 10 = 4 (questions) + 1 (FAQ) + 5 (learning in public)

Homework URL: View submission

Learning in public links: Show

• https://www.evidentlyai.com/classification-metrics/explain-roc-curve
• https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc
• https://www.datacamp.com/tutorial/auc
• https://www.evidentlyai.com/classification-metrics/confusion-matrix
• https://medium.com/@mehrdadhz.75/why-we-should-use-k-fold-cross-validation-63209c8046f9

Homework 5: Deploying Machine Learning Models

Score: 11 = 6 (questions) + 1 (FAQ) + 4 (learning in public)

Homework URL: View submission

Learning in public links: Show

• https://fastapi.tiangolo.com/tutorial/
• https://code.visualstudio.com/docs/python/tutorial-fastapi
• https://www.kaggle.com/code/tasnimniger/model-serialization-using-pickle-and-joblib
• https://www.digitalocean.com/community/conceptual-articles/uv-python-package-manager

Homework 6: Decision Trees and Ensemble Learning

Score: 14 = 6 (questions) + 1 (FAQ) + 7 (learning in public)

Homework URL: View submission

Learning in public links: Show

• https://scikit-learn.org/stable/modules/tree.html
• https://ken-hoffman.medium.com/decision-tree-hyperparameters-explained-49158ee1268e
• https://towardsdatascience.com/visualising-decision-trees/
• https://www.kaggle.com/code/prashant111/bagging-vs-boosting
• https://www.analyticsvidhya.com/blog/2023/01/ensemble-learning-methods-bagging-boosting-and-stacking/
• https://www.mygreatlearning.com/blog/bagging-boosting/
• https://sebastianraschka.com/faq/docs/bagging-boosting-rf.html

Midterm Project

Project score: 14 Passed

Score: 30 = 14 (project) + 9 (peer review) + 6 (learning in public / project) + 0 (learning in public / peer review) + 1 (FAQ)

Project URL: View project

Learning in public links: Show

• https://joblib.readthedocs.io/en/latest/
• https://scikit-learn.org/stable/modules/compose.html
• https://medium.com/@abhaysingh71711/building-smarter-ml-pipelines-with-column-transformers-895904e97254
• https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
• https://www.kaggle.com/code/imoore/intro-to-exploratory-data-analysis-eda-in-python
• https://medium.com/@tarangds/a-comprehensive-guide-to-data-imputation-techniques-strategies-and-best-practices-152a10fee543