Beginner


 

Classification

Learn how to prepare the data for modeling, create a classification model, tune hyperparameters of a model, analyze the performance and consume the model for predictions.

Anomaly Detection

Learn how to prepare the data for modeling, create an unsupervised anomaly detector, evaluate the results of trained model and consume the model for predictions on unseen data.

Regression

Learn how to prepare the data for modeling, create a regression model, tune hyperparameters of a model, evaluate model errors and consume the model for predictions.

Natural Language Processing

Learn how to perform text preprocessing, tune hyperparameters of a topic model and consume the results of a topic model in supervised experiment i.e. Classification or Regression. 

Clustering

Learn how to prepare the data for modeling, create a K-Means clustering model, assign the labels, analyze results and consume trained model for predictions on unseen data.

Association Rule Mining

Learn how to prepare data for association rule mining. Create an apriori model, examine rules and analyze results.

Intermediate


 

Classification

Learn how to apply several preprocessing techniques such as normalization, transformation, tackle multicollinearity. Ensemble models through Bagging, Boosting, Voting Classifier and Generalized Stacking.

Regression

Learn how to apply several preprocessing techniques such as normalization, transformation, target transformation and numeric binning. Ensemble models through Bagging, Boosting, Voting Regressor and Stacking.

Natural Language Processing

Learn how to remove custom stopwords from your text data, tune hyperparameters of a topic model and consume the results of a topic model in supervised experiment i.e. Classification or Regression. 

Video Tutorials