Machine Learning, incl. Deep Learning, with R

Machine Learning, incl. Deep Learning, with R

Machine Learning, incl. Deep Learning, with R Download

Statistical Machine Learning Techniques, and Deep Learning with Keras, and much more. (All R code included)


Have you ever wondered how to “learn” machines-you’ll find out in this course?

We will cover all areas of Machine Learning: Regression and Classification Techniques, Clustering, Association Rules, Reinforcement Learning, and, perhaps most importantly, Deep Regression Learning, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, etc.

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

You will know the benefits and disadvantages of various designs and when to use one. You’ll also be able to take your knowledge into the real world.

You will have access to an interactive learning platform to assist you better comprehend the ideas.

Code will never come out of thin air through copy/paste in this course. We’re going to develop every important code line together and I’m going to tell you why and how we’re implementing it.

Look at some lectures in the sample. Or visit some of my interactive boards of learning. In addition, there is a money-back guarantee of 30 days, so there is no risk for you to take the course now. Wait, don’t wait. See you on the path.

What you’ll learn
  • With R, you’ll learn how to construct state-of-the-art machine learning models.
  • Deep learning models for regression and classification tasks with Keras
  • Convolutional neural networks for the classification of images with Keras
  • Models of regression (e.g. univariate, multivariate, polynomial)
  • Models of classification (e.g. Confusion Matrix, ROC, Logistic Regression, Random Forests, SVM, Ensemble Learning)
  • Keras autoencoders
  • Pre-trained models and Keras transfer learning
  • Techniques of Regularization
  • Recurrent neural networks, in particular, LSTM
  • Rules of association (e.g. Apriori)
  • Knowledge of basic R programming is useful, but not necessary.
Who this course is for:
  • R beginners and professionals interested in learning machines and/or deep learning
Machine Learning, incl. Deep Learning, with R Direct Download

Google Drive | [7.2 GB]


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