Created by Bert Gollnick
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- 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.