Introduction: (1). Basic concepts of machine learning; and (2) Weka Workbench as our machine learning environment.
Data mining in Weka: (1) Panels in Weka; and (2) Knowing our data in the Weka panels.
Pre-analysis and pre-processing of data: (1) Classification of data; (2) Data set for machine learning; and (3) Pre-analysis of data.
Pre-processing of data for machine learning: (1) Normalization and standardization of data; (2) Transform the data; and (3) Handle lost values in the data.
Data analysis in machine learning: (1) Future Selection; (2) Use of machine learning algorithms; (3) Estimate the result of the algorithms; and (4) Estimate a baseline of the results.
Modeling phase in machine learning: (1) Classification algorithms; (2) Regression algorithms; and (3) Ensemble algorithms.
Phase 'Tuning' in machine learning: (1) Compare the performance of the algorithms; (2) 'Tunning' the parameters (hyperparameters) of the algorithms; and (3) Save our models and make predictions.
Projects in machine learning: (1) Work on a multiclass classification project; (2) Work on a binary classification project; and (3) Work on a regression project.