v0.2.0

General update

v0.1.8

• Multiple updates to library

v0.1.6

• Added the ability to store machine learning models using nlpi.store_model
• Renamed nlpi.store to nlpi.store_data

v0.1.4

• Added new module sltree (DecisionTree models)
• Added new module slcatboost (Added CatBoost models)
• Both modules are utilised with base class eval_base

v0.1.3

• Added cross validation & train-test splitting evaluation class eval_base
• Added gradient boosting machine learning evaluation class slensemble

v0.1.2

• Added new module for linear regression and classification models sllinear
• Added new module for dimensionality reduction using decomposition and manifold learning approaches usldimred
• Added NER based sentence splitting for more accurate request interpretation
• Updated mllibs design (calmer colours)

v0.1.1

• Added new module for text normalisation mtextnorm
• Added FastText and negative sampling approach for skip-gram embedding generation in membedding

v0.0.9

• Added new modules for text encoding & embedding generation mencoding & membedding
• Added example notebook mllibs-encode-text.ipynb

v0.0.8

• Added new module for data outlier detection moutliers
• Added instance variable self.task_info which outputs a dataframe containing activation task information, including samples from the corpus, which allows user to know what content to write in order to activate function
• Added example notebook mllibs-sample-eda-notebook.ipynb

v0.0.6

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