What: Learning Outcomes

Course Objectives

  1. Describe fundamental machine learning concepts such as: supervised and unsupervised learning, regression and classification, overfitting, training/validation/testing error, parameters and hyperparameters, and the golden rule.

  2. Broadly explain how common machine learning algorithms work, including: naïve Bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression.

  3. Identify when and why to apply data pre-processing techniques such as scaling and one-hot encoding.

  4. Use Python and the scikit-learn package to develop an end-to-end supervised machine learning pipeline.

  5. Apply and interpret machine learning methods to carry out supervised learning projects and to answer business objectives.

Lecture 1

  • Explain motivation to study machine learning.

  • Differentiate between supervised and unsupervised learning.

  • Differentiate between classification and regression problems.

  • Explain machine learning terminology such as features, targets, training, and error.

  • Explain the .fit() and .predict() paradigm and use .score() method of ML models.

  • Broadly describe how decision trees make predictions.

  • Use DecisionTreeClassifier() and DecisionTreeRegressor() to build decision trees using scikit-learn.

  • Explain the difference between parameters and hyperparameters.

  • Explain how decision boundaries change with max_depth.

Lecture 2

  • Explain the concept of generalization.

  • Split a dataset into train and test sets using train_test_split function.

  • Explain the difference between train, validation, test, and “deployment” data.

  • Identify the difference between training error, validation error, and test error.

  • Explain cross-validation and use cross_val_score() and cross_validate() to calculate cross-validation error.

  • Explain overfitting, underfitting, and the fundamental tradeoff.

  • State the golden rule and identify the scenarios when it’s violated.

Lecture 3

  • Use DummyClassifier and DummyRegressor as baselines for machine learning problems.

  • Explain the notion of similarity-based algorithms .

  • Broadly describe how KNNs use distances.

  • Discuss the effect of using a small/large value of the hyperparameter \(K\) when using the KNN algorithm

  • Explain the general idea of SVMs with RBF kernel.

  • Describe the problem of the curse of dimensionality.

  • Broadly describe the relation of gamma and C hyperparameters and the fundamental tradeoff.

Lecture 4

  • Identify when to implement feature transformations such as imputation and scaling.

  • Describe the difference between normalizing and standardizing and be able to use scikit-learn’s MinMaxScaler() and StandardScaler() to pre-process numeric features.

  • Apply sklearn.pipeline.Pipeline to build a machine learning pipeline.

  • Use sklearn for applying numerical feature transformations to the data.

  • Discuss the golden rule in the context of feature transformations.

Lecture 5

  • Identify when it’s appropriate to apply ordinal encoding vs one-hot encoding.

  • Explain strategies to deal with categorical variables with too many categories.

  • Explain handle_unknown="ignore" hyperparameter of scikit-learn’s OneHotEncoder.

  • Use the scikit-learn ColumnTransformer function to implement preprocessing functions such as MinMaxScaler and OneHotEncoder to numeric and categorical features simultaneously.

  • Use ColumnTransformer to build all our transformations together into one object and use it with scikit-learn pipelines.

  • Explain why text data needs a different treatment than categorical variables.

  • Use scikit-learn’s CountVectorizer to encode text data.

  • Explain different hyperparameters of CountVectorizer.

Lecture 6

  • Identify when it’s appropriate to apply ordinal encoding vs one-hot encoding.

  • Explain strategies to deal with categorical variables with too many categories.

  • Explain handle_unknown="ignore" hyperparameter of scikit-learn’s OneHotEncoder.

  • Use the scikit-learn ColumnTransformer function to implement preprocessing functions such as MinMaxScaler and OneHotEncoder to numeric and categorical features simultaneously.

  • Use ColumnTransformer to build all our transformations together into one object and use it with scikit-learn pipelines.

  • Explain why text data needs a different treatment than categorical variables.

  • Use scikit-learn’s CountVectorizer to encode text data.

  • Explain different hyperparameters of CountVectorizer.

Lecture 7

  • Explain the general intuition behind linear models.

  • Explain the fit and predict paradigm of linear models.

  • Use scikit-learn’s LogisticRegression classifier.

    • Use fit, predict and predict_proba.

    • Use coef_ to interpret the model weights.

  • Explain the advantages and limitations of linear classifiers.

  • Apply scikit-learn regression model (e.g., Ridge) to regression problems.

  • Relate the Ridge hyperparameter alpha to the LogisticRegression hyperparameter C.

  • Compare logistic regression with naive Bayes.

Lecture 8

  • In the context of supervised learning, form statistical questions from business questions/objectives.

  • Understand the different forms your client may expect you to communicate results.

  • Explain the general concept of feature selection.

  • Discuss and compare different feature selection methods at a high level.

  • Use sklearn’s implementation of recursive feature elimination (RFE).

  • Implement the forward search algorithm.

Lecture 9

  • Explain why accuracy is not always the best metric in ML.

  • Explain components of a confusion matrix.

  • Define precision, recall, and f1-score and use them to evaluate different classifiers.

  • Identify whether there is class imbalance and whether you need to deal with it.

  • Explain class_weight and use it to deal with data imbalance.

  • Appropriately select a scoring metric given a regression problem.

  • Interpret and communicate the meanings of different scoring metrics on regression problems. MSE, RMSE, \(R^2\), MAPE.

  • Apply different scoring functions with cross_validate, GridSearchCV and RandomizedSearchCV.

Lecture 10

  • Explain ethical considerations in data science, relating to multiple phases of machine learning pipelines.

  • Be able to analyze a confusion matrix and think about how different scoring metrics affect diverse stakeholders.

  • Explain components of a confusion matrix with respect to multi-class classification.

  • Define precision, recall, and f1-score with multi-class classification

  • Carry out multi-class classification using OVR and OVO strategies.