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    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
    27.5 Logistic Regression Implementation Krish Naik ML
    13:28
    27.3 Performance Metrics Krish Naik ML
    23:26
    27.2 Logistic Regression Indepth Math Intuition Krish Naik ML
    18:30
    26.7 End To End ML Project Implementation Krish Naik ML
    32:02
    26.1 Basic Simple Linear Regression Project Krish Naik ML
    41:23
    27.1 Can Linear Regression Solve Classifier Problem Krish Naik ML
    11:46
    27.6 Grid Search Hyper Parameter Krish Naik ML
    12:31
    27.10 Logistic Regression ROC Krish Naik ML
    18:40
    27.9 Logistic Imbalanced Dataset Krish Naik ML
    9:47
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