Proficiency in a broad range of machine learning algorithms and the ability to select appropriate models for specific problems.

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.

Proficiency in hyperparameter tuning, model selection, and ensemble methods to improve model performance.

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