What do I need to know?

Need expert advice on the best statistical method for your case? Is your training or inference slower than expected? Want a confidential discussion about your data? Need fast responses without waiting months?

Machine learning concepts

- 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

Model building and evaluation

- 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

Interpretation of results & communication

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

Enhanced Security

OSS security services to track and monitor dependancies and minimize risks using open source libraries

Data preprocessing

- 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

Model selection and validation

- 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