Machine Learning, incl. Deep Learning, with R

Machine Learning, incl. Deep Learning, with R, Statistical Machine Learning Techniques, and Deep Learning with Keras, and much more. (All R code included)
Created by Bert Gollnick
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What you'll learn

• You will learn to build state-of-the-art Machine Learning models with R.
• Deep Learning models with Keras for Regression and Classification tasks
• Convolutional Neural Networks with Keras for image classification
• Regression Models (e.g. univariate, polynomial, multivariate)
• Classification Models (e.g. Confusion Matrix, ROC, Logistic Regression, Decision Trees, Random Forests, SVM, Ensemble Learning)
• Autoencoders with Keras
• Pretrained Models and Transfer Learning with Keras
• Regularization Techniques
• Recurrent Neural Networks, especially LSTM
• Association Rules (e.g. Apriori)
• Clustering techniques (e.g. kmeans, hierarchical clustering, dbscan)
• Dimensionality Reduction techniques (e.g. Principal Component Analysis, Factor Analysis, t-SNE)
• Reinforcement Learning techniques (e.g. Upper Confidence Bound)
• You will know how to evaluate your model, what underfitting and overfitting is, why resampling techniques are important, and how you can split your dataset into parts (train/validation/test).
• We will understand the theory behind deep neural networks.
• We will understand and implement convolutional neural networks - the most powerful technique for image recognition.