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Applied Machine Learning and Deep Learning with R
Introduction to Machine Learning
The Course Overview (4:45)
Supervised and Unsupervised Learning (6:13)
Feature Selection (2:39)
Model Evaluation Methods - Cross Validation (3:17)
Performance Metrics (3:39)
Clustering
K-Means Clustering (6:46)
Hierarchical Clustering (5:36)
DBSCAN Algorithm (4:09)
Clustering Exercises with R (6:33)
Dealing with Problems About Clustering (4:26)
Classification
k-NN Classification (7:25)
Logistic Regression (5:06)
Naive Bayes (3:02)
Decision Trees (3:20)
Classification Exercises with R (4:04)
Handling Problems About Classification (4:32)
Artificial Neural Networks
Introduction to Artificial Neural Networks (4:27)
Types of Artificial Neural Networks (3:11)
Back Propagation (3:06)
Artificial Neural Networks Exercises with R (3:43)
Tricks for ANN in R (2:52)
Introduction to Deep Learning
What Is Deep Learning? (5:25)
Elements of Deep Neural Networks (2:26)
Types of Deep Neural Networks (1:24)
Introduction to Deep Learning Frameworks (4:28)
Exercises with TensorFlow in R (8:01)
Tricks About Application of Deep Neural Nets (1:54)
Machine Learning with SparkR
Introduction to SparkR (1:07)
Installation of SparkR (3:11)
Writing First Script on SparkR (2:18)
Generalized Linear Models with SparkR (3:36)
Classification Exercises with SparkR (1:49)
Clustering Exercises with SparkR (2:50)
Naive Bayes with SparkR (1:21)
Tricks About SparkR (2:25)
k-NN Classification
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