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    31.7 Decision Tree Regression Krish Naik ML
    21:21
    31.8 Decision Tree Implementation Krish Naik ML
    16:53
    31.9 Decision tree Prepruning Krish Naik ML
    8:27
    31.3 Information Gain Krish Naik ML
    9:07
    31.6 Post Pruning & Pre Pruning Krish Naik ML
    8:23
    31.4 Entropy vs Gini impurity Krish Naik ML
    2:48
    31.5 Decision Tree Split for Numerical Features Krish Naik ML
    4:58
    31.2 Entropy and Gini Impurity Krish Naik ML
    11:31
    31.1 Introduction TO Decision Tree Krish Naik ML
    12:42
    29.1 Understanding Baye's Theorem Krish Naik ML
    34:32
    29.3 Naive Baye's Practical Implementation Krish Naik ML
    10:10
    29.2 Variants Of Naive Baye's Krish Naik ML
    11:24
    28.9 Support Vector Regression Implementation Krish Naik ML
    21:00
    28.7 Support Vector Classifiers Krish Naik ML
    18:10
    28.8 SVM Kernels implementation Krish Naik ML
    13:34
    30.3 KNN Classifier And Regressor Classification Krish Naik ML
    6:15
    28.3 SVM Maths Intuition Krish Naik ML
    12:05
    28.5 Support Vector Regression Krish Naik ML
    10:22
    28.6 SVM Kernels Krish Naik ML
    10:41
    28.1 Introduction to support vector Machine Krish Naik ML
    8:55
    28.2 SoftMargin and Hard Margin Krish Naik ML
    2:29
    28.4 SVC Cost function Krish Naik ML
    6:50