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Machine Learning Introduction 0
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ML01_01_Machine Learning Introduction and Defination 15 minLecture1.1
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Ml02_01_ETP_Defimation 15 minLecture1.2
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ML03_01_Applications of ML 15 minLecture1.3
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ML04_01_Types of Machine Learning and Supervised Learning Introduction 15 minLecture1.4
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ML05_01_UnSupervised Learning Introduction 15 minLecture1.5
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ML06_01_reading _sklearn_ml_package_help_document part 1 15 minLecture1.6
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ML07_01_reading _sklearn_ml_package_help_document part 2 15 minLecture1.7
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ML08_01_Test Your Understanding 15 minLecture1.8
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Explore Toy-Datasets 6
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ML09_02_Explore Toy-Datasets 15 minLecture2.1
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ML10_02_Explore iris Dataset 15 minLecture2.2
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ML11_02_Similarly explore remaining toy datasets 15 minLecture2.3
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ML12_02_Create DataFrame from sklearn Bunch 15 minLecture2.4
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ML13_02_Create a Bunch with our own data 15 minLecture2.5
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ML14_02_Create a Bunch with our own data part 2 15 minLecture2.6
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k nearest neighbor algorithm Maths 5
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ML15_03_k nearest neighbor algorithm Maths 15 minLecture3.1
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ML16_03_Find unknown sample quality based on known samples 15 minLecture3.2
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ML17_03_Find unknown flower name based on known flower names using MS excel 15 minLecture3.3
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ML18_03_Importance of n_neighbors 15 minLecture3.4
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ML19_03_Hamming distance 15 minLecture3.5
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KNN Estimator from Scratch 16
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ML20_04_KNN Estimator from Scratch 15 minLecture4.1
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ML21_04_Write code to Locate the most similar neighbors 15 minLecture4.2
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ML22_04_Write code to Make a classification prediction with neighbors 15 minLecture4.3
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ML23_04_High level End to End ML project Steps 15 minLecture4.4
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ML24_04_Load csv file and Understand X and y Data 15 minLecture4.5
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ML25_04_Split Data for training and testing 15 minLecture4.6
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ML26_04_Train or fit the model 15 minLecture4.7
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ML27_04_Predict labels of test data 15 minLecture4.8
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ML28_04_Accuracy_of_the_Clasification_model 15 minLecture4.9
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ML29_04_Hyper_Parameter_tunningLecture4.10
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ML30_04_k means cross validation 15 minLecture4.11
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ML31_04_GridSearchCV Hyper Parameter Tunning 15 minLecture4.12
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ML32_04_RandomizedSearchCV Hyper Parameter Tunning 15 minLecture4.13
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ML33_04_Save The model 15 minLecture4.14
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ML34_04_Load The model 15 minLecture4.15
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ML35_04_Home_Work 15 minLecture4.16
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Linear Regression Maths 15
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ML36_05_Linear Regression Maths 15 minLecture5.1
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ML37_05_Find weight of the baby based on age data understanding 15 minLecture5.2
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ML38_05_Ordinary Least Squares 15 minLecture5.3
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ML39_05_Find parameters using Ordinary Least Squares Function 15 minLecture5.4
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ML40_05_Find parameters using sklearn 15 minLecture5.5
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ML41_05_Find parameters using Scipy 15 minLecture5.6
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ML42_05_Find parameters using covar and var 15 minLecture5.7
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ML43_05_Multivariate Linear Regression 15 minLecture5.8
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ML44_05_Linear_regression_to find life span based on number of fertilities part 1 15 minLecture5.9
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ML45_05_Linear_regression_to find life span based on number of fertilities part 2 15 minLecture5.10
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ML46_05_Supervised_Regression_Metric_R2_score 15 minLecture5.11
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ML47_05_Supervised_Regression_Metrics_RMSE 15 minLecture5.12
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ML48_05_Life Span Predication 15 minLecture5.13
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ML49_05_Linear Regression with Cross Validation or K-Fold 15 minLecture5.14
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ML50_05_Linear Regression with Boston dataset 15 minLecture5.15
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Logistic Regression 6
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ML51_06_Logistic Regression Maths 15 minLecture6.1
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ML52_06_Logistic Regression Binary ClasificationLecture6.2
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ML_53_06_Confusion Matrix 15 minLecture6.3
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ML_54_06_Classification Report 15 minLecture6.4
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ML_55_06_ROC Curve 15 minLecture6.5
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ML_56_06_AUC Computation 15 minLecture6.6
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Support Vector Machines 5
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ML_57_07_Support Vector Machines Introduction 15 minLecture7.1
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ML_58_07_Support Vectors and Maximizing the Margin 15 minLecture7.2
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ML_59_07_Non_linear_Support Vectors and Maximizing the Margin 15 minLecture7.3
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ML_60_07_upport Vector Machines Using Iris Toy Data set 15 minLecture7.4
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ML_61_07_Support_Vector_Machines_for_Face_Recognition 15 minLecture7.5
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Pre-processing of machine learning 6
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ML_62_08_Pre-processing of machine learning data Outliers 15 minLecture8.1
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ML_63_08_Pre-processing of machine learning data Delete Outliers 15 minLecture8.2
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ML_64_08_Pre-processing Categorical Features 15 minLecture8.3
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ML_65_08_Pre-processing Categorical Features part2 15 minLecture8.4
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ML_66_08_Regression with categorical features using ridge algorithm 15 minLecture8.5
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ML_67_08_Dropping Missing Data 15 minLecture8.6
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ML_Pipeline with feature_selection and SVC 2
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ML_68_09_ML_Pipeline with feature_selection and SVCLecture9.1
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ML_69_09_ML_Pipeline with SimpleImputer and SVC 15 minLecture9.2
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Trees Entropy and Gini Maths Introduction 10
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ML_70_10_Trees Entropy and Gini Maths IntroductionLecture10.1
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ML_71_10_Entropy Calculation using weather datasetLecture10.2
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ML_72_10_Entropy Calculation using weather dataset part 2 15 minLecture10.3
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ML_73_10_Entropy Calculation using weather dataset part 3 15 minLecture10.4
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ML_74_10_Entropy Calculation using weather dataset part 4 15 minLecture10.5
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ML_75_10_Decision_Tree 15 minLecture10.6
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ML_76_10_Predict_Tumor_is_cancer_not_a_cancerLecture10.7
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ML_77_10_Tree_Graph_Visulization 15 minLecture10.8
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ML_78_10_Logistic_regression_and_Decision_tree_accuracy_compare 15 minLecture10.9
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ML_79_10_Gini_Vs_Entropy 15 minLecture10.10
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BIAS_Variance_Tradeoff_part 4
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ML_80_11_BIAS_Variance_Tradeoff_part1 15 minLecture11.1
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ML_81_11_BIAS_Variance_Tradeoff_part2 15 minLecture11.2
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ML_82_11_BIAS_Variance_Tradeoff_part3 15 minLecture11.3
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ML_83_11_BIAS_Variance_Tradeoff_part4 15 minLecture11.4
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Ensemble_Learning_Introduction 5
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ML_84_12_Ensemble_Learning_Introduction 15 minLecture12.1
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ML_85_12_Voting_Classifier_Introduction 15 minLecture12.2
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ML_86_12_Voting_Classifier_for_Predict_Liver_Disease 15 minLecture12.3
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ML_87_12_Voting_Classifier_for_Predict_Liver_Disease_preprocess 15 minLecture12.4
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ML_88_12_Voting_Classifier_for_Predict_Liver_Disease_Train_the_Model 15 minLecture12.5
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Bagging_Classifier_Introduction 4
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ML_89_13_Bagging_Classifier_Introduction 15 minLecture13.1
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ML_90_13_Bagging_Classifier 15 minLecture13.2
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ML_91_13_Out_of_Bagging_Accuracy 15 minLecture13.3
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ML_92_13_Tree_Vs_Bagging 15 minLecture13.4
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Random_Forests_Introduction 3
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ML_93_14_Random_Forests_Introduction 15 minLecture14.1
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ML_94_14_Random_Forests_on_auto_dataset 15 minLecture14.2
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ML_95_14_Random_Forests_Feature_Importance 15 minLecture14.3
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AdaBoost_Introduction 6
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ML_96_15_AdaBoost_IntroductionLecture15.1
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ML_97_15_AdaBoost_MathsLecture15.2
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GghhLecture15.3
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ML_99_15_AdaBoost_Help_document Part 2Lecture15.4
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ML_100_15_AdaBoost_on_Iris_dataset Part1Lecture15.5
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ML_101_15_AdaBoost_on_Iris_dataset part2Lecture15.6
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ML_102_16_UnSupervised_Learning_Introduction 3
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ML_102_16_UnSupervised_Learning_IntroductionLecture16.1
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ML_103_16_KMeans_MathsLecture16.2
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ML_104_16_KMeans_on_iris_datasetLecture16.3
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ML35_04_Home_Work