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Machine Learning, incl. Deep Learning, with R

 Machine Learning, incl. Deep Learning, with R
best udemy course
Link : Machine Learning, incl. Deep Learning, with R
Statistical Machine Learning Techniques, and Deep Learning with Keras, and much more. (All R code included)
by Bert Gollnick

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.

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 to solve it.
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