Opleiding: Machine Learning met R
De cursist leert de belangrijkste terminologie binnen de Machine Learning (ML) en leert de R libraries toe te passen bij machine learning.
Machine Learning is een onderdeel van artificial intelligence en gaat over algoritmes die zich automatisch verbeteren op basis van ervaring. Deze Machine learning algoritmes maken een model gebaseerd op training data en kunnen voorspellingen doen over nieuwe data.
Dag 1: Dag 1 Korte herhaling R programmeren De R Data Types en Data Frames Statistische functies in R R Data Files R Packages R Algoritmen Daarna gaan we aan de slag met Machine Learning. Wat is Machine Learning Building Models of Data Model Based Learning Tunable Parameters Supervised Learning Discrete Labels Continuous Labels Classification and Regression Unsupervised Learning Clustering and Dimensionality Reduction Dag 2: Dag 2 Check Model Using Summary Using Coefficients Correlation R R Squared F Test Check Model Graphically Check Residuals Polynomial Regression Gaussian Basis Functions Overfitting Compare with Linear Regression Explore with Graphics Logistic Function Checking Model Using Summary Using Coefficients Calculate Probabilities Making Predictions Confusion Matrix Accuracy Precision and Recall ROC Curve Functional R Solving Iteration purr package library tidyverse map Functions Parameters of map .x as placeholder map_lgl Function map_int and map_char map2 Function Other iteration functions Combine purr with dyplr walk Function Dag 3: Dag 3 MSpark Session Copy data into Spark File Setup Load data Spark SQL Store Data Using dplyr showquery() Spark DataFrame Functions sdf_pivot() Feature Transformers Distributed R Web Applications Shiny Architecture Shiny Server UI and Server Input Object Output Object Reactivity Render Options Shiny Functions Shiny Layout and Dashboard Shiny Performance Ensemble Learner Creating Decision Trees DecisionTreeClassifier Overfitting Decision Trees Ensembles of Estimator Random Forests Parallel Estimators Bagging Classifier Random Forest Regression RandomForestRegressor Non Parametric Model
U bent in staat de zelflerende machine te trainen en toe te passen. U bent in staat om zelf een zelflerende machine te ontwikkelen.
