Machine Learning, incl. Deep Learning, with R Did you ever wonder how machines “*learn*” – in this course you will find out. We will cover all fields of *Machine Learning*: Regression and Classification

## 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.

Description

Did you ever wonder how machines “learn” – in this course you will find out.

We will cover **all fields **of **Machine Learning**: **Regression **and **Classification **techniques, **Clustering**, **Association Rules**, **Reinforcement Learning**, and, possibly most importantly, **Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, …**

For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples. With this knowledge you can clearly identify a problem at hand and develop a plan of attack