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  • Lesson.No : 27
  • 00:12:13

  • Multiple Linear Regression (MLP) 1/2

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Course Lessons

  1. 1- Overview of Data Mining and Predictive Modelling
  2. 2- What is Classification? What is a Classifier?
  3. 3- The ZeroR Classifier .. What it is and How it Works
  4. 4- The OneR Classifier .. What it is and How it Works
  5. 5- How Naive Bayes Classifier Works 1/2.. Understanding Naive Bayes and Example
  6. 6- Naive Bayes Classifier 2/2 .. Naive Bayes and Numerical Attributes
  7. 7- How Decision Trees Work 1/2 .. an Introduction + What is Entropy
  8. 8- How Decision Trees Work 2/2 .. An Example
  9. 9- How Linear Discriminant Analysis (LDA) Classifier Works 1/2
  10. 10- How Linear Discriminant Analysis (LDA) Classifier Works 2/2
  11. 11- How K-Nearest Neighbors (kNN) Classifier Works
  12. 12- Java Implementation of K-Nearest Neighbors (kNN) Classifier 1/2
  13. 13- Java Implementation of K-Nearest Neighbors (kNN) Classifier 2/2
  14. 14- Classification with Logistic Regression 1/2
  15. 15- Classification with Logistic Regression 2/2
  16. 16- How the Perceptron Algorithm Works 1/2
  17. 17- How the Perceptron Algorithm Works 2/2
  18. 18- Java Implementation of the Perceptron Algorithm
  19. 19- Transfer Functions in the Perceptron and Artificial Neural Networks
  20. 20- Understanding Multi-Layer Perceptron (MLP) .. How it Works
  21. 21- Feedforward and Feedback Artificial Neural Networks
  22. 22- Radial Basis Function Artificial Neural Networks
  23. 23- How Support Vector Machine (SVM) Works 1/2
  24. 24- How Support Vector Machine (SVM) Works 2/2
  25. 25- Nonlinear Support Vector Machine (SVM) .. The Kernel Trick
  26. 26- Regression with Decision Trees
  27. 27- Multiple Linear Regression (MLP) 1/2
  28. 28- Multiple Linear Regression (MLP) 2/2 ... with an example
  29. 29- Regression with the k-Nearest Neighbor (kNN) Algorithm
  30. 30- Clustering Algorithms .. What is Clustering?
  31. 31- Hierarchical Clustering (Agglomerative and Divisive Clustering)
  32. 32- Partitive Clustering (K-Means Clustering)
  33. 33- Partitive Clustering .. Self-Organizing Map (SOM)
  34. 34- An Overview of Association Rules
  35. 35- The Apriori Algorithm ... How The Apriori Algorithm Works
  36. 36- Generating Association Rules from Frequent Itemsets