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Machine Learning, incl. Deep Learning, with R

Machine Learning, incl. Deep Learning, with R

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
English
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PREVIEW THIS COURSE - GET COUPON CODE

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.

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