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3.77 GB | 17min 11s | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
FileName :1 -Introduction to the Course.mp4 | Size:  (10.54 MB)
FileName :2 -What is Machine Learning with Example.mp4 | Size:  (90.48 MB)
FileName :3 -Tom M  Mitchell Definition of Machine Learning.mp4 | Size:  (23.54 MB)
FileName :4 -Types of Machine Learning and List of most ML algorithms.mp4 | Size:  (55.64 MB)
FileName :1 -Hold Out Cross Validation Technique.mp4 | Size:  (23.34 MB)
FileName :10 -Parameters and Hyper-Parameters of the ML Algorithms.mp4 | Size:  (49.88 MB)
FileName :11 -GridSearchCV - Hyper-Parameter Tuning Method.mp4 | Size:  (44.85 MB)
FileName :2 -K-Fold Cross Validation Technique.mp4 | Size:  (26.39 MB)
FileName :3 -Stratified K-Fold Cross Validation Technique.mp4 | Size:  (66.16 MB)
FileName :4 -Leave P-Out Cross Validation Technique.mp4 | Size:  (31.76 MB)
FileName :5 -Leave One Out Cross Validation.mp4 | Size:  (10.4 MB)
FileName :6 -Imbalanced Dataset.mp4 | Size:  (26.31 MB)
FileName :7 -OverSampling and UnderSampling.mp4 | Size:  (25.42 MB)
FileName :8 -Synthetic Minority Oversampling Technique (SMOTE).mp4 | Size:  (18.58 MB)
FileName :9 -Use case using the SMOTE.mp4 | Size:  (37.39 MB)
FileName :1 -Introduction to Correlation and Regression.mp4 | Size:  (57.32 MB)
FileName :2 -Regression Algorithm Assumptions.mp4 | Size:  (52.3 MB)
FileName :3 -Simple and Multi Linear Regression (SLR) Algorithm.mp4 | Size:  (86.39 MB)
FileName :4 -Hypothesis Testing to evaluate the significance of regression line.mp4 | Size:  (41.76 MB)
FileName :5 -R-Square Performance Measure.mp4 | Size:  (45.76 MB)
FileName :6 -Simple Linear Regression Implementation using sklearn library.mp4 | Size:  (18.17 MB)
FileName :7 -Introduction to Use Case.mp4 | Size:  (19.52 MB)
FileName :8 -Use case discussion.mp4 | Size:  (73.86 MB)
FileName :1 -What is classification and regression.mp4 | Size:  (19.59 MB)
FileName :10 -Maximum Likelihood Estimation (MLE).mp4 | Size:  (77.56 MB)
FileName :11 -Solving Logistic Regression Example with MLE.mp4 | Size:  (23.24 MB)
FileName :2 -What is Logistic Regression, How it is different from linear regression and how.mp4 | Size:  (47.02 MB)
FileName :3 -Logistic Regression Explanation with Example.mp4 | Size:  (47.16 MB)
FileName :4 -Linear VS Logistic Regression.mp4 | Size:  (47.26 MB)
FileName :5 -Confusion Matrix.mp4 | Size:  (60.91 MB)
FileName :6 -Performance Metrics in Classification.mp4 | Size:  (44.89 MB)
FileName :7 -Difference between Probability and Odds.mp4 | Size:  (71.53 MB)
FileName :8 -Logistic Regression Derivation.mp4 | Size:  (21.11 MB)
FileName :9 -Difference between Probability and Likelihood.mp4 | Size:  (32.55 MB)
FileName :1 -Agenda.mp4 | Size:  (6.94 MB)
FileName :2 -What is DT, its intuition and Terminologies.mp4 | Size:  (98.56 MB)
FileName :3 -Impurity Measures - Entropy, Gini Index and Classification Error.mp4 | Size:  (125.26 MB)
FileName :4 -Decision Tree Algorithms and Lets learn ID3 DT.mp4 | Size:  (129.45 MB)
FileName :5 -CART Decision Tree Algorithm - wrt Classification.mp4 | Size:  (47.69 MB)
FileName :6 -CART Decision Tree Algorithm - wrt Regression.mp4 | Size:  (37.37 MB)
FileName :7 -Use case on Decision Tree - Prediction of Wine Quality.mp4 | Size:  (81.15 MB)
FileName :1 -Parametric and Non-Parametric ML Algorithms.mp4 | Size:  (51.29 MB)
FileName :2 -Distance Measures.mp4 | Size:  (50.84 MB)
FileName :3 -Introduction to KNN Algorithm.mp4 | Size:  (70.03 MB)
FileName :4 -How KNN Algorithm works.mp4 | Size:  (18.5 MB)
FileName :5 -How to find optimum K Value in KNN.mp4 | Size:  (32.11 MB)
FileName :6 -Use case explaining KNN implementation.mp4 | Size:  (24.68 MB)
FileName :7 -Example - How to find an optimum k value for KNN.mp4 | Size:  (26.77 MB)
FileName :1 -Partition Theorem.mp4 | Size:  (26.37 MB)
FileName :2 -Naïve Bayes Algorithm Pre-requisites.mp4 | Size:  (53.29 MB)
FileName :3 -Bayes Theorem With Example.mp4 | Size:  (59.21 MB)
FileName :4 -Bayes Theorem Formal Defination.mp4 | Size:  (12.94 MB)
FileName :5 -Naïve Bayes Classifier with example.mp4 | Size:  (66.11 MB)
FileName :1 -Recap of our learning.mp4 | Size:  (11.59 MB)
FileName :10 -Elbow Method.mp4 | Size:  (23.42 MB)
FileName :11 -Performance Metrics in Clustering.mp4 | Size:  (23.66 MB)
FileName :12 -Silhouette Score Example.mp4 | Size:  (25.36 MB)
FileName :13 -Use case using Silhouette score.mp4 | Size:  (28.39 MB)
FileName :2 -Agenda.mp4 | Size:  (6.78 MB)
FileName :3 -Distance Measures.mp4 | Size:  (49.59 MB)
FileName :4 -Distance Measures Use cases.mp4 | Size:  (73.96 MB)
FileName :5 -Use of Distance Measures in Machine Learning.mp4 | Size:  (23.79 MB)
FileName :6 -KMeans Clustering Algorithm.mp4 | Size:  (26.72 MB)
FileName :7 -Example - Clustering the data using KMeans Clustering Algorithm.mp4 | Size:  (22.36 MB)
FileName :8 -KMeans Cost Function.mp4 | Size:  (10.94 MB)
FileName :9 -KMeans Use cases.mp4 | Size:  (38.34 MB)
FileName :1 -tSNE Introduction.mp4 | Size:  (63.05 MB)
FileName :2 -tSNE Algorithm Steps.mp4 | Size:  (14.28 MB)
FileName :3 -tSNE use case.mp4 | Size:  (22.95 MB)
FileName :4 -tSNE Using the MINIST Dataset.mp4 | Size:  (42.37 MB)
FileName :1 -Introduction.mp4 | Size:  (18.61 MB)
FileName :10 -Random Forest.mp4 | Size:  (62.95 MB)
FileName :11 -Hyperparameters to tune Random Forest.mp4 | Size:  (53.63 MB)
FileName :12 -Stacking Ensemble Learning.mp4 | Size:  (77.13 MB)
FileName :13 -Use case On Stacking.mp4 | Size:  (41.3 MB)
FileName :14 -Boosting.mp4 | Size:  (83.99 MB)
FileName :15 -Boosting Algorithm Steps.mp4 | Size:  (45.47 MB)
FileName :16 -AdaBoosting Ensemble Learning Model.mp4 | Size:  (39.5 MB)
FileName :17 -AdaBoosting Ensemble Learning - Example.mp4 | Size:  (47.89 MB)
FileName :18 -Bagging and Boosting Comparison.mp4 | Size:  (23.66 MB)
FileName :19 -Gradient Boosting Algorithm.mp4 | Size:  (36.29 MB)
FileName :2 -What is Ensemble and Model Error.mp4 | Size:  (48.78 MB)
FileName :20 -Gradient Boosting Example.mp4 | Size:  (23.93 MB)
FileName :21 -XGBoost Ensemble Learning Method.mp4 | Size:  (22.47 MB)
FileName :3 -Bias and Variance Tradeoff.mp4 | Size:  (60.36 MB)
FileName :4 -Simple Ensemble Modeling Methods - Voting, Averaging and Weighted Averaging.mp4 | Size:  (63.34 MB)
FileName :5 -Random Sampling with Replacement.mp4 | Size:  (36.48 MB)
FileName :6 -Use case 1 - Random Sampling with Replacement using customer feedback data.mp4 | Size:  (18.74 MB)
FileName :7 -Use case 2 - Understanding the 63 21% Rule in Sampling with Replacement.mp4 | Size:  (40.7 MB)
FileName :8 -Bagging.mp4 | Size:  (16.69 MB)
FileName :9 -Vanilla Bagging Algorithm.mp4 | Size:  (44 MB)]
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