Part IV: Naive Bayes Numeric Attributes, Supervised, Classification, Data Mining, Machine Learning

This video explains how to solve problems when training data contains numeric attributes.Normal distribution, posterior, likelihood and prior probability Data Mining Playlists https://www.youtube.com/playlistlist=PLS5J_kYlArq7NnTLozsMLdyAq8apovSdb KNN Classification https://youtu.be/xk5Tppw8T-o https://youtu.be/dWpxMirfh2o K medoid https://youtu.be/kB6gJpAe42k https://youtu.be/NouxOecodUE https://youtu.be/WfyKH1tQoVQ Hierarchical Algorithms, Agglomerative Algorithm https://youtu.be/8rsJRbYnRR4 https://youtu.be/Y8gcg_PWAfc https://youtu.be/hYnXR_OYBA0 https://youtu.be/ZSc0VbPLVaM Hierarchical Algorithms, Divisive Algorithm https://youtu.be/vQEXvV5W7s0 https://youtu.be/4GbhWbMJLMY K means Algorithm https://youtu.be/aoE_rJCG744 https://youtu.be/pGNLEvGMytY https://youtu.be/n07rNm-jchw NN Clustering https://youtu.be/I0eWqR8KFBc https://youtu.be/mJWUdwl8dEM ECLAT ( Equivalence Class Transformation) Vertical Apriori https://youtu.be/IwbnylEzp0w Frequent, Closed, Maximal Itemset https://youtu.be/OEupexDlgv0 Apriori Algorithm https://youtu.be/d9xd1qV64Wo Apriori Algorithm Additional exercises https://youtu.be/hdgd1iBma-8 Improve Apriori Algorithm Efficiency https://youtu.be/asWqVHex9kY Naive Bayes Theorem https://youtu.be/rcv33FFTljY https://youtu.be/aD8z8Zl75Ug https://youtu.be/SHdBunw7Fok https://youtu.be/iISIGIPVBMw https://youtu.be/WT4mCV1h7BM Data Mining Introduction https://youtu.be/rlUiecEhEYQ Data Mining Variables https://youtu.be/xTiSPoDdHT8 https://youtu.be/ayBZwQKB_HQ DBSCAN clustering Algorithm https://youtu.be/jqKAAVEwX9M https://youtu.be/S5OvKmWIdZA Frequent Pattern Algorithm https://youtu.be/SzgNYFaBxHQ https://youtu.be/UDpK_pgsZZY https://youtu.be/uhWZxCGwi_0 Decision Tree https://youtu.be/FZdh2l-o4-g https://youtu.be/s3P1tn3V_eA https://youtu.be/47MOP240lZs https://youtu.be/LqOnsit20Dw Multidimensional Association Rule https://youtu.be/M3wyG3HKuNg Multilevel Association Rule https://youtu.be/1H8KM2s6ce0