Machine Learning, incl. Deep Learning, with R (Updated)

[ FreeCourseWeb.com ] Machine Learning, incl. Deep Learning, with R (Updated)



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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 15.5 hour | Size: 7.26 GB

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

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.



Requirements



Basic R Programming knowledge is helpful, but not required.



Use Winrar to Extract. And use a shorter path when extracting, such as C: drive



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