Proficiency in fundamental machine learning algorithms and identifying when and how to use various models. This includes knowing when machine learning models are sufficient, as opposed to when deep learning might be overkill.

Proficiency in Python, particularly in using libraries such as scikit-learn, Pandas, and NumPy.

Ability to clean, manipulate, and preprocess data using Python libraries.

Leveraging Python plotting tools and interpreting results effectively to create robust data-driven solutions.

Basic understanding of statistics, probability, and hypothesis testing to interpret model results.

Familiarity with techniques for evaluating model performance, such as cross-validation, confusion matrices, and ROC curves.

Strong attention to detail to ensure data accuracy and model reliability.

Basic problem solving skills with a logical approach to analyzing and addressing issues. This includes making design choices for data pipelines and their evaluation.

- Types of Machine Learning: Supervised, Unsupervised, and Semi-supervised learning.
- Model Families: Tree-based, Linear, Ensemble, Neighbors.
- Key concepts: features, labels, training and test sets
- Model overfitting and underfitting
- Bias/variance trade-off

- Splitting datasets into training and testing sets using train_test_split
- Training ML models using the fit() method
- Making predictions using the predict() method
- Evaluating model performance with most common metrics (accuracy, precision, recall, F1 score, confusion matrix, mean squared error, R-squared)
- Interpreting score with respect to dummy models

- Visualizing model results using basic plotting techniques (matplotlib, seaborn)
- Interpreting and communicating model outputs and performance metrics to non-technical stakeholders

- Loading parquet datasets
- Visualizing data with basic plotting techniques (scatterplot, boxplot)
- Identify wrongly encoded predictive columns (e.g. float encoded as string)
- Handling missing values using imputation SimpleImputer
- Correct choice of feature scaling using StandardScaler, MinMaxScaler, etc
- Encoding categorical data using OrdinalEncoder and OneHotEncoder
- Combining preprocessing steps with ColumnTransformer

- Understanding and implementing cross-validation techniques (KFold, ShuffleSplit, etc)
- Learning and validation curves
- Performing hyperparameter tuning using GridSearchCV, RandomSearchCV
- Stability of learned coefficients across splits