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| [CourseClub.NET].url |
123б |
| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| 1. BI Intro,definition.mp4 |
51.19Мб |
| 1. BI Intro,definition.vtt |
10.41Кб |
| 1. Intoroduction to NLP.mp4 |
32.70Мб |
| 1. Intoroduction to NLP.vtt |
7.20Кб |
| 1. Introduction to Amazon Machine Learning (AML).mp4 |
38.18Мб |
| 1. Introduction to Amazon Machine Learning (AML).vtt |
8.32Кб |
| 1. Introduction to Banking System.mp4 |
17.11Мб |
| 1. Introduction to Banking System.vtt |
6.44Кб |
| 1. Introduction to Bayesian Machine Learning.mp4 |
36.82Мб |
| 1. Introduction to Bayesian Machine Learning.vtt |
9.88Кб |
| 1. Introduction to BIP.mp4 |
21.00Мб |
| 1. Introduction to BIP.vtt |
4.89Кб |
| 1. Introduction to Deep Learning.mp4 |
28.08Мб |
| 1. Introduction to Deep Learning.vtt |
4.96Кб |
| 1. Introduction to Fraud Detection in Credit Payments.mp4 |
17.35Мб |
| 1. Introduction to Fraud Detection in Credit Payments.vtt |
6.40Кб |
| 1. Introduction to Machine Learning with Python.mp4 |
58.25Мб |
| 1. Introduction to Machine Learning with Python.vtt |
9.18Кб |
| 1. Introduction to Machine Learning with Tensorflow.mp4 |
17.82Мб |
| 1. Introduction to Machine Learning with Tensorflow.vtt |
5.38Кб |
| 1. Introduction to Predicting Prices Using Regression.mp4 |
58.91Мб |
| 1. Introduction to Predicting Prices Using Regression.vtt |
10.76Кб |
| 1. Introduction to Shipping and pricing.mp4 |
25.71Мб |
| 1. Introduction to Shipping and pricing.vtt |
3.80Кб |
| 1. Introduction to Supply Chain.mp4 |
100.34Мб |
| 1. Introduction to Supply Chain.vtt |
6.43Кб |
| 1. Machine Learning Introduction.mp4 |
20.06Мб |
| 1. Machine Learning Introduction.vtt |
4.36Кб |
| 10. 2.10 Problem and Solution.mp4 |
79.67Мб |
| 10. 2.10 Problem and Solution.vtt |
7.61Кб |
| 10. Data for Classifier.mp4 |
49.77Мб |
| 10. Data for Classifier.vtt |
5.24Кб |
| 10. Demand Forecasting.mp4 |
85.19Мб |
| 10. Demand Forecasting.vtt |
8.67Кб |
| 10. Example of Data Insight In AML.mp4 |
66.85Мб |
| 10. Example of Data Insight In AML.vtt |
11.22Кб |
| 10. planning deliverables,stage 3.mp4 |
34.10Мб |
| 10. planning deliverables,stage 3.vtt |
6.73Кб |
| 10. Replacing Features with Values.mp4 |
83.71Мб |
| 10. Replacing Features with Values.vtt |
8.82Кб |
| 10. Siebel Applets ‚ Business Obejct and Business Components Part 2.mp4 |
90.35Мб |
| 10. Siebel Applets ‚ Business Obejct and Business Components Part 2.vtt |
9.86Кб |
| 10. Stemming and Lemmatization Continues.mp4 |
61.22Мб |
| 10. Stemming and Lemmatization Continues.vtt |
7.30Кб |
| 10. Technical Terminology.mp4 |
54.82Мб |
| 10. Technical Terminology.vtt |
11.05Кб |
| 10. Understanding what Anaconda cloud is.mp4 |
80.82Мб |
| 10. Understanding what Anaconda cloud is.vtt |
8.47Кб |
| 10. VRS.mp4 |
107.46Мб |
| 10. VRS.vtt |
6.87Кб |
| 100. Diagnostic Checking.mp4 |
60.55Мб |
| 100. Diagnostic Checking.vtt |
8.71Кб |
| 100. Regression Model(Continues).mp4 |
44.73Мб |
| 100. Regression Model(Continues).vtt |
10.40Кб |
| 100. Run Optimizer.mp4 |
13.63Мб |
| 100. Run Optimizer.vtt |
1.85Кб |
| 101. Create a Range.mp4 |
51.40Мб |
| 101. Create a Range.vtt |
6.30Кб |
| 101. Forecasting Using Stock Price.mp4 |
108.90Мб |
| 101. Forecasting Using Stock Price.vtt |
9.65Кб |
| 101. Market Basket Analysis Applications.mp4 |
46.71Мб |
| 101. Market Basket Analysis Applications.vtt |
11.40Кб |
| 102. Introduction to Neural Networks.mp4 |
5.61Мб |
| 102. Introduction to Neural Networks.vtt |
1.31Кб |
| 102. Market Basket Analysis Applications(Continues).mp4 |
35.93Мб |
| 102. Market Basket Analysis Applications(Continues).vtt |
10.40Кб |
| 102. Stock Price Index.mp4 |
99.87Мб |
| 102. Stock Price Index.vtt |
8.75Кб |
| 103. Basic-Concepts.mp4 |
101.53Мб |
| 103. Basic-Concepts.vtt |
12.84Кб |
| 103. Stock Price Index Continues.mp4 |
95.16Мб |
| 103. Stock Price Index Continues.vtt |
7.10Кб |
| 104. Activative Functions.mp4 |
99.94Мб |
| 104. Activative Functions.vtt |
10.80Кб |
| 104. Prophet Stock.mp4 |
50.06Мб |
| 104. Prophet Stock.vtt |
3.61Кб |
| 105. Activative Functions Input to Output.mp4 |
57.72Мб |
| 105. Activative Functions Input to Output.vtt |
57.73Мб |
| 105. Run Prophet Stock.mp4 |
78.21Мб |
| 105. Run Prophet Stock.vtt |
6.71Кб |
| 106. Classification Functions.mp4 |
61.74Мб |
| 106. Classification Functions.vtt |
8.37Кб |
| 106. Time Series Data Denationalization.mp4 |
101.32Мб |
| 106. Time Series Data Denationalization.vtt |
9.37Кб |
| 107. Tensorflow-Playground.mp4 |
136.67Мб |
| 107. Tensorflow-Playground.vtt |
14.16Кб |
| 107. Time Series Data Denationalization Continues.mp4 |
78.48Мб |
| 107. Time Series Data Denationalization Continues.vtt |
6.69Кб |
| 108. Average of Quarter Denationalization.mp4 |
126.96Мб |
| 108. Average of Quarter Denationalization.vtt |
9.37Кб |
| 108. Mnist-Dataset.mp4 |
54.38Мб |
| 108. Mnist-Dataset.vtt |
10.71Кб |
| 109. Mnist-Dataset Continue.mp4 |
93.75Мб |
| 109. Mnist-Dataset Continue.vtt |
12.10Кб |
| 109. Regression of Denationalization.mp4 |
103.77Мб |
| 109. Regression of Denationalization.vtt |
7.74Кб |
| 11. Assigning Quantatative Variables.mp4 |
45.85Мб |
| 11. Assigning Quantatative Variables.vtt |
5.32Кб |
| 11. Convert Token No Stopwords.mp4 |
62.67Мб |
| 11. Convert Token No Stopwords.vtt |
5.77Кб |
| 11. CRS Efficiency and Efficiency.mp4 |
53.09Мб |
| 11. CRS Efficiency and Efficiency.vtt |
5.71Кб |
| 11. Distribution of Attributes.mp4 |
63.71Мб |
| 11. Distribution of Attributes.vtt |
6.42Кб |
| 11. Error of Observation and Non Observation.mp4 |
31.16Мб |
| 11. Error of Observation and Non Observation.vtt |
31.17Мб |
| 11. Exponentiation Right to Left.mp4 |
53.17Мб |
| 11. Exponentiation Right to Left.vtt |
6.49Кб |
| 11. Implementing with Keras.mp4 |
45.54Мб |
| 11. Implementing with Keras.vtt |
3.73Кб |
| 11. Installation of Anaconda for Windows.mp4 |
50.24Мб |
| 11. Installation of Anaconda for Windows.vtt |
7.37Кб |
| 11. IntegrationObjectsANDIntegrationObjectComponents.mp4 |
101.06Мб |
| 11. IntegrationObjectsANDIntegrationObjectComponents.vtt |
10.88Кб |
| 11. More on Data Insight In AML.mp4 |
54.57Мб |
| 11. More on Data Insight In AML.vtt |
8.15Кб |
| 11. Project Requirement,Data Analysis,Application part 1.mp4 |
52.97Мб |
| 11. Project Requirement,Data Analysis,Application part 1.vtt |
10.71Кб |
| 110. Gradient Boosting Machines.mp4 |
67.00Мб |
| 110. Gradient Boosting Machines.vtt |
9.04Кб |
| 110. More on Mnist-Dataset.mp4 |
81.92Мб |
| 110. More on Mnist-Dataset.vtt |
7.68Кб |
| 111. Errors in Gradient Boosting Machines.mp4 |
57.60Мб |
| 111. Errors in Gradient Boosting Machines.vtt |
10.28Кб |
| 112. What is Error Rate in Gradient Boosting Machines.mp4 |
57.55Мб |
| 112. What is Error Rate in Gradient Boosting Machines.vtt |
6.83Кб |
| 113. Optimization Gradient Boosting Machines.mp4 |
51.82Мб |
| 113. Optimization Gradient Boosting Machines.vtt |
6.67Кб |
| 114. Gradient Boosting Trees (GBT).mp4 |
38.39Мб |
| 114. Gradient Boosting Trees (GBT).vtt |
5.43Кб |
| 115. Dataset Boosting in Gradient.mp4 |
96.43Мб |
| 115. Dataset Boosting in Gradient.vtt |
10.50Кб |
| 116. Example of Dataset Boosting in Gradient.mp4 |
95.79Мб |
| 116. Example of Dataset Boosting in Gradient.vtt |
10.83Кб |
| 117. Example of Dataset Boosting in Gradient Continues.mp4 |
113.25Мб |
| 117. Example of Dataset Boosting in Gradient Continues.vtt |
12.62Кб |
| 118. Market Basket Analysis Association Rules.mp4 |
98.38Мб |
| 118. Market Basket Analysis Association Rules.vtt |
10.69Кб |
| 119. Market Basket Analysis Association Rules Continues.mp4 |
78.30Мб |
| 119. Market Basket Analysis Association Rules Continues.vtt |
9.61Кб |
| 12. 2.13 Avoiding Some Common Mistakes.mp4 |
69.82Мб |
| 12. 2.13 Avoiding Some Common Mistakes.vtt |
7.15Кб |
| 12. Converting Columns to Cordinal Forms.mp4 |
48.87Мб |
| 12. Converting Columns to Cordinal Forms.vtt |
4.56Кб |
| 12. Installation of Anaconda in Linux.mp4 |
29.15Мб |
| 12. Installation of Anaconda in Linux.vtt |
3.93Кб |
| 12. Machine Learning Algorithms.mp4 |
60.01Мб |
| 12. Machine Learning Algorithms.vtt |
6.33Кб |
| 12. ML Model Example in Data Sources.mp4 |
90.66Мб |
| 12. ML Model Example in Data Sources.vtt |
12.46Кб |
| 12. Project Requirement,Data Analysis,Application part 2.mp4 |
65.11Мб |
| 12. Project Requirement,Data Analysis,Application part 2.vtt |
8.66Кб |
| 12. Siebel Views and View Associations to Reports.mp4 |
82.77Мб |
| 12. Siebel Views and View Associations to Reports.vtt |
7.44Кб |
| 12. Spending Distribution.mp4 |
82.72Мб |
| 12. Spending Distribution.vtt |
5.33Кб |
| 12. Systematic Sampling.mp4 |
54.93Мб |
| 12. Systematic Sampling.vtt |
7.93Кб |
| 12. Values in Data Set.mp4 |
72.42Мб |
| 12. Values in Data Set.vtt |
6.11Кб |
| 120. Market Basket Analysis Interpretation.mp4 |
50.28Мб |
| 120. Market Basket Analysis Interpretation.vtt |
6.75Кб |
| 121. Implementation of Market Basket Analysis.mp4 |
30.32Мб |
| 121. Implementation of Market Basket Analysis.vtt |
4.57Кб |
| 122. Example of Market Basket Analysis.mp4 |
84.78Мб |
| 122. Example of Market Basket Analysis.vtt |
9.12Кб |
| 123. Datamining in Market Basket Analysis.mp4 |
85.97Мб |
| 123. Datamining in Market Basket Analysis.vtt |
10.72Кб |
| 124. Market Basket Analysis Using Rstudio.mp4 |
78.53Мб |
| 124. Market Basket Analysis Using Rstudio.vtt |
9.39Кб |
| 125. Market Basket Analysis Using Rstudio Continues.mp4 |
94.13Мб |
| 125. Market Basket Analysis Using Rstudio Continues.vtt |
94.13Мб |
| 126. More on Rstudio in Market Analysis.mp4 |
122.52Мб |
| 126. More on Rstudio in Market Analysis.vtt |
11.22Кб |
| 127. New Development in Machine Learning.mp4 |
93.23Мб |
| 127. New Development in Machine Learning.vtt |
93.24Мб |
| 128. Data Scientist in Machine Learnirng.mp4 |
74.05Мб |
| 128. Data Scientist in Machine Learnirng.vtt |
10.06Кб |
| 129. Types of Detection in Machine Learning.mp4 |
102.86Мб |
| 129. Types of Detection in Machine Learning.vtt |
9.45Кб |
| 13. Cluster Sampling.mp4 |
58.79Мб |
| 13. Cluster Sampling.vtt |
9.99Кб |
| 13. Components in Data Set.mp4 |
79.14Мб |
| 13. Components in Data Set.vtt |
6.74Кб |
| 13. Creating ML Model Evaluating.mp4 |
84.60Мб |
| 13. Creating ML Model Evaluating.vtt |
8.44Кб |
| 13. Evaluating the Garage Finish Colummn.mp4 |
66.18Мб |
| 13. Evaluating the Garage Finish Colummn.vtt |
7.01Кб |
| 13. Normalization and Discretization.mp4 |
119.50Мб |
| 13. Normalization and Discretization.vtt |
5.63Кб |
| 13. Project Requirement,Data Analysis,Application part 3.mp4 |
46.98Мб |
| 13. Project Requirement,Data Analysis,Application part 3.vtt |
7.77Кб |
| 13. Siebel HI-OpenUI framworks for BIP Reports and demo of AddIn.mp4 |
42.71Мб |
| 13. Siebel HI-OpenUI framworks for BIP Reports and demo of AddIn.vtt |
6.16Кб |
| 13. Simple Linear Regression.mp4 |
68.16Мб |
| 13. Simple Linear Regression.vtt |
10.45Кб |
| 13. Using the Jupyter notebook.mp4 |
26.08Мб |
| 13. Using the Jupyter notebook.vtt |
3.33Кб |
| 130. Example of New Development in Machine Learning.mp4 |
79.23Мб |
| 130. Example of New Development in Machine Learning.vtt |
9.17Кб |
| 131. Example of New Development in Machine Learning Continues.mp4 |
53.15Мб |
| 131. Example of New Development in Machine Learning Continues.vtt |
4.45Кб |
| 14. Advanced Setting In ML Model.mp4 |
38.46Мб |
| 14. Advanced Setting In ML Model.vtt |
4.75Кб |
| 14. Checking Shape of Data Frame.mp4 |
14.86Мб |
| 14. Checking Shape of Data Frame.vtt |
2.11Кб |
| 14. Getting started with Anaconda.mp4 |
136.15Мб |
| 14. Getting started with Anaconda.vtt |
11.07Кб |
| 14. Meta Data.mp4 |
11.09Мб |
| 14. Meta Data.vtt |
2.01Кб |
| 14. Models in Data Set.mp4 |
62.07Мб |
| 14. Models in Data Set.vtt |
5.72Кб |
| 14. Process_Flow_Overview.mp4 |
51.80Мб |
| 14. Process_Flow_Overview.vtt |
8.32Кб |
| 14. Simple Linear Regression Continues.mp4 |
39.71Мб |
| 14. Simple Linear Regression Continues.vtt |
6.04Кб |
| 14. Statistics Data Types.mp4 |
25.59Мб |
| 14. Statistics Data Types.vtt |
25.59Мб |
| 15. Creating ML Model for Batch Prediction.mp4 |
84.89Мб |
| 15. Creating ML Model for Batch Prediction.vtt |
9.69Кб |
| 15. data standardisation,meta data,etl,business analysis part 1.mp4 |
61.46Мб |
| 15. data standardisation,meta data,etl,business analysis part 1.vtt |
10.73Кб |
| 15. Determining options for Cloudberry.mp4 |
39.51Мб |
| 15. Determining options for Cloudberry.vtt |
0б |
| 15. Process_Flow_ConnectedMode.mp4 |
42.29Мб |
| 15. Process_Flow_ConnectedMode.vtt |
6.12Кб |
| 15. Qualitative Data and Visualization.mp4 |
37.69Мб |
| 15. Qualitative Data and Visualization.vtt |
8.11Кб |
| 15. Spliting Data to Train and Test.mp4 |
68.83Мб |
| 15. Spliting Data to Train and Test.vtt |
8.58Кб |
| 15. What is Rsquare.mp4 |
77.58Мб |
| 15. What is Rsquare.vtt |
7.69Кб |
| 16. Algorithm for Predicting Test Values.mp4 |
28.00Мб |
| 16. Algorithm for Predicting Test Values.vtt |
4.08Кб |
| 16. Batch Prediction Result.mp4 |
47.61Мб |
| 16. Batch Prediction Result.vtt |
5.56Кб |
| 16. data standardisation,meta data,etl,business analysis part 2.mp4 |
51.14Мб |
| 16. data standardisation,meta data,etl,business analysis part 2.vtt |
9.46Кб |
| 16. Introduction to Third Party Libraries.mp4 |
8.19Мб |
| 16. Introduction to Third Party Libraries.vtt |
3.55Кб |
| 16. Machine Learning.mp4 |
56.07Мб |
| 16. Machine Learning.vtt |
8.45Кб |
| 16. Process_Flow_DisconnectedMode.mp4 |
42.12Мб |
| 16. Process_Flow_DisconnectedMode.vtt |
6.94Кб |
| 16. Standard Error.mp4 |
54.56Мб |
| 16. Standard Error.vtt |
7.37Кб |
| 17. data standardisation,meta data,etl,business analysis part 3.mp4 |
20.52Мб |
| 17. data standardisation,meta data,etl,business analysis part 3.vtt |
3.28Кб |
| 17. General Statistics.mp4 |
52.34Мб |
| 17. General Statistics.vtt |
3.96Кб |
| 17. Numpy-Array.mp4.mtd |
351б |
| 17. Numpy-Array.vtt |
13.27Кб |
| 17. Overvies of ML Model Handson.mp4 |
65.33Мб |
| 17. Overvies of ML Model Handson.vtt |
7.23Кб |
| 17. Relative Frequency Probability.mp4 |
62.98Мб |
| 17. Relative Frequency Probability.vtt |
9.13Кб |
| 17. Siebel Report Business Service.mp4 |
77.34Мб |
| 17. Siebel Report Business Service.vtt |
8.01Кб |
| 18. ETL Design,Meta DATA ,STAGE 5 CONSTRUCTION DEVELOPMENT RECONCILATION Part 1.mp4 |
47.14Мб |
| 18. ETL Design,Meta DATA ,STAGE 5 CONSTRUCTION DEVELOPMENT RECONCILATION Part 1.vtt |
9.91Кб |
| 18. General Statistics Continues.mp4 |
50.23Мб |
| 18. General Statistics Continues.vtt |
5.74Кб |
| 18. Joint Probability.mp4 |
85.96Мб |
| 18. Joint Probability.vtt |
9.81Кб |
| 18. ML objects Handson in ML.mp4 |
45.63Мб |
| 18. ML objects Handson in ML.vtt |
4.12Кб |
| 18. Numpy-Array Continue.mp4 |
84.07Мб |
| 18. Numpy-Array Continue.vtt |
9.18Кб |
| 19. Arrays.mp4 |
110.36Мб |
| 19. Arrays.vtt |
13.52Кб |
| 19. Conditional Probability.mp4 |
42.78Мб |
| 19. Conditional Probability.vtt |
8.09Кб |
| 19. ETL Design,Meta DATA ,STAGE 5 CONSTRUCTION DEVELOPMENT RECONCILATION Part 2.mp4 |
65.35Мб |
| 19. ETL Design,Meta DATA ,STAGE 5 CONSTRUCTION DEVELOPMENT RECONCILATION Part 2.vtt |
11.67Кб |
| 19. Simple Linear Regression and More of Statistics.mp4 |
69.26Мб |
| 19. Simple Linear Regression and More of Statistics.vtt |
10.70Кб |
| 2. Example of Bayesian Machine Learning.mp4 |
31.87Мб |
| 2. Example of Bayesian Machine Learning.vtt |
6.67Кб |
| 2. G Plot of Heatmap.mp4 |
78.74Мб |
| 2. G Plot of Heatmap.vtt |
4.73Кб |
| 2. How do Machine Learn.mp4 |
51.66Мб |
| 2. How do Machine Learn.vtt |
8.12Кб |
| 2. Installation of Packages.mp4 |
72.81Мб |
| 2. Installation of Packages.vtt |
7.24Кб |
| 2. Introduction to Machine Learning with Python.mp4 |
6.52Мб |
| 2. Introduction to Machine Learning with Python.vtt |
3.33Кб |
| 2. Inventory Status.mp4 |
104.65Мб |
| 2. Inventory Status.vtt |
7.97Кб |
| 2. Laon Status Grade.mp4 |
93.48Мб |
| 2. Laon Status Grade.vtt |
7.89Кб |
| 2. Lifecycle of AML.mp4 |
43.45Мб |
| 2. Lifecycle of AML.vtt |
11.58Кб |
| 2. multidimensional db part 1.mp4 |
50.11Мб |
| 2. multidimensional db part 1.vtt |
8.76Кб |
| 2. Proximity to Various Conditions.mp4 |
64.34Мб |
| 2. Proximity to Various Conditions.vtt |
10.80Кб |
| 2. Structure of Neural Network.mp4 |
35.22Мб |
| 2. Structure of Neural Network.vtt |
4.94Кб |
| 2. Text Preprocessing.mp4 |
39.41Мб |
| 2. Text Preprocessing.vtt |
6.29Кб |
| 2. Understanding Machine Learning.mp4 |
15.54Мб |
| 2. Understanding Machine Learning.vtt |
8.54Кб |
| 2. User Types.mp4 |
10.38Мб |
| 2. User Types.vtt |
3.85Кб |
| 20. Arrays Continue.mp4 |
55.40Мб |
| 20. Arrays Continue.vtt |
6.12Кб |
| 20. Concept of Independence.mp4 |
40.30Мб |
| 20. Concept of Independence.vtt |
5.78Кб |
| 20. ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 1.mp4 |
46.66Мб |
| 20. ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 1.vtt |
9.66Кб |
| 20. Open the Studio.mp4 |
40.40Мб |
| 20. Open the Studio.vtt |
5.82Кб |
| 21. ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 2.mp4 |
17.37Мб |
| 21. ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 2.vtt |
4.25Кб |
| 21. Indexing.mp4 |
63.28Мб |
| 21. Indexing.vtt |
7.40Кб |
| 21. Total Probability.mp4 |
57.65Мб |
| 21. Total Probability.vtt |
9.28Кб |
| 21. What is R Square.mp4 |
79.51Мб |
| 21. What is R Square.vtt |
8.01Кб |
| 22. ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 3.mp4 |
60.84Мб |
| 22. ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 3.vtt |
11.04Кб |
| 22. Indexing Continue.mp4 |
85.08Мб |
| 22. Indexing Continue.vtt |
9.14Кб |
| 22. Random Variable.mp4 |
46.71Мб |
| 22. Random Variable.vtt |
8.26Кб |
| 22. What is STD Error.mp4 |
55.37Мб |
| 22. What is STD Error.vtt |
6.71Кб |
| 23. database & recovery,release evaluation.mp4 |
30.53Мб |
| 23. database & recovery,release evaluation.vtt |
30.54Мб |
| 23. Probability Distribution.mp4 |
68.80Мб |
| 23. Probability Distribution.vtt |
10.40Кб |
| 23. Reject Null Hypothesis.mp4 |
84.23Мб |
| 23. Reject Null Hypothesis.vtt |
8.24Кб |
| 23. Universal Functions.mp4 |
109.59Мб |
| 23. Universal Functions.vtt |
10.75Кб |
| 24. Cumulative Probability Distribution.mp4 |
37.58Мб |
| 24. Cumulative Probability Distribution.vtt |
8.67Кб |
| 24. Introoduction to Pandas.mp4 |
26.06Мб |
| 24. Introoduction to Pandas.vtt |
4.64Кб |
| 24. post implementation review,toyota case.mp4 |
44.88Мб |
| 24. post implementation review,toyota case.vtt |
8.03Кб |
| 24. Variance Covariance and Correlation.mp4 |
81.89Мб |
| 24. Variance Covariance and Correlation.vtt |
11.48Кб |
| 25. Bernoulli Distribution.mp4 |
38.71Мб |
| 25. Bernoulli Distribution.vtt |
8.21Кб |
| 25. frame work for BI Part 1.mp4 |
58.71Мб |
| 25. frame work for BI Part 1.vtt |
11.63Кб |
| 25. Pandas Series.mp4 |
33.47Мб |
| 25. Pandas Series.vtt |
4.65Кб |
| 25. Root names and Types of Distribution Function.mp4 |
71.71Мб |
| 25. Root names and Types of Distribution Function.vtt |
10.59Кб |
| 26. frame work for BI Part 2.mp4 |
56.09Мб |
| 26. frame work for BI Part 2.vtt |
9.26Кб |
| 26. Gaussian Distribution.mp4 |
33.68Мб |
| 26. Gaussian Distribution.vtt |
8.05Кб |
| 26. Generating Random Numbers and Combination Function.mp4 |
63.10Мб |
| 26. Generating Random Numbers and Combination Function.vtt |
6.98Кб |
| 26. Pandas Series Continue.mp4 |
38.07Мб |
| 26. Pandas Series Continue.vtt |
5.57Кб |
| 27. frame work for BI Part 3.mp4 |
33.11Мб |
| 27. frame work for BI Part 3.vtt |
5.20Кб |
| 27. Geometric Distribution.mp4 |
33.20Мб |
| 27. Geometric Distribution.vtt |
7.25Кб |
| 27. Import Randin.mp4 |
64.96Мб |
| 27. Import Randin.vtt |
9.40Кб |
| 27. Probabilities for Discrete Distribution Function.mp4 |
83.57Мб |
| 27. Probabilities for Discrete Distribution Function.vtt |
8.91Кб |
| 28. Continuous and Normal Distribution.mp4 |
49.07Мб |
| 28. Continuous and Normal Distribution.vtt |
9.60Кб |
| 28. frame work for BI Part 4.mp4 |
38.18Мб |
| 28. frame work for BI Part 4.vtt |
4.96Кб |
| 28. Import Randin Continue.mp4 |
72.37Мб |
| 28. Import Randin Continue.vtt |
10.25Кб |
| 28. Quantile Function and Poison Distribution.mp4 |
77.31Мб |
| 28. Quantile Function and Poison Distribution.vtt |
8.40Кб |
| 29. Mathematical Expression and Computation.mp4 |
33.82Мб |
| 29. Mathematical Expression and Computation.vtt |
8.70Кб |
| 29. Paratmeters.mp4 |
87.75Мб |
| 29. Paratmeters.vtt |
10.35Кб |
| 29. strategic imperitive of BI Part 1.mp4 |
49.28Мб |
| 29. strategic imperitive of BI Part 1.vtt |
7.85Кб |
| 29. Students T Distribution, Hypothesis and Example.mp4 |
63.21Мб |
| 29. Students T Distribution, Hypothesis and Example.vtt |
7.18Кб |
| 3. Analytics in Machine Learning.mp4 |
47.03Мб |
| 3. Analytics in Machine Learning.vtt |
8.46Кб |
| 3. Checking the Function Argument.mp4 |
121.19Мб |
| 3. Checking the Function Argument.vtt |
3.93Кб |
| 3. Connecting to Data Source in AML.mp4 |
18.80Мб |
| 3. Connecting to Data Source in AML.vtt |
3.66Кб |
| 3. Defining Data Type.mp4 |
95.45Мб |
| 3. Defining Data Type.vtt |
6.70Кб |
| 3. Example of Bayesian Machine Learning Continues.mp4 |
36.13Мб |
| 3. Example of Bayesian Machine Learning Continues.vtt |
4.39Кб |
| 3. Feature Extraction.mp4 |
4.48Мб |
| 3. Feature Extraction.vtt |
1.57Кб |
| 3. How do Machines Learns.mp4 |
51.90Мб |
| 3. How do Machines Learns.vtt |
14.34Кб |
| 3. Logistic Regression and Logistic Question.mp4 |
64.52Мб |
| 3. Logistic Regression and Logistic Question.vtt |
5.54Кб |
| 3. Moving Through Neural Network.mp4 |
38.04Мб |
| 3. Moving Through Neural Network.vtt |
5.88Кб |
| 3. multidimensional db part 2.mp4 |
61.19Мб |
| 3. multidimensional db part 2.vtt |
10.75Кб |
| 3. Number of Fire Places.mp4 |
30.23Мб |
| 3. Number of Fire Places.vtt |
5.08Кб |
| 3. Risk Analytics.mp4 |
82.51Мб |
| 3. Risk Analytics.vtt |
10.26Кб |
| 3. Running Modes.mp4 |
33.52Мб |
| 3. Running Modes.vtt |
6.58Кб |
| 3. Steps to Apply Machine Learning.mp4 |
45.69Мб |
| 3. Steps to Apply Machine Learning.vtt |
7.24Кб |
| 30. Chai-Square Distribution.mp4 |
41.33Мб |
| 30. Chai-Square Distribution.vtt |
4.88Кб |
| 30. Indexing and Database.mp4 |
37.90Мб |
| 30. Indexing and Database.vtt |
4.55Кб |
| 30. strategic imperitive of BI Part 2.mp4 |
41.76Мб |
| 30. strategic imperitive of BI Part 2.vtt |
6.50Кб |
| 30. Transpose of Matrix.mp4 |
46.05Мб |
| 30. Transpose of Matrix.vtt |
8.39Кб |
| 31. Data Visualization.mp4 |
77.80Мб |
| 31. Data Visualization.vtt |
7.15Кб |
| 31. Missing Data.mp4 |
30.30Мб |
| 31. Missing Data.vtt |
5.19Кб |
| 31. Properties of Matrix.mp4 |
47.72Мб |
| 31. Properties of Matrix.vtt |
11.58Кб |
| 31. Target System.mp4 |
47.82Мб |
| 31. Target System.vtt |
7.70Кб |
| 32. Data warehouse and ETL.mp4 |
33.03Мб |
| 32. Data warehouse and ETL.vtt |
6.29Кб |
| 32. Determinants.mp4 |
47.64Мб |
| 32. Determinants.vtt |
8.57Кб |
| 32. Missing Data-Groupby.mp4 |
20.18Мб |
| 32. Missing Data-Groupby.vtt |
3.24Кб |
| 32. More on Data Visualization.mp4 |
70.53Мб |
| 32. More on Data Visualization.vtt |
6.73Кб |
| 33. Error Types.mp4 |
53.25Мб |
| 33. Error Types.vtt |
8.34Кб |
| 33. Facebook dataspace management with open source tools.mp4 |
38.35Мб |
| 33. Facebook dataspace management with open source tools.vtt |
6.06Кб |
| 33. Missing Data-Groupby Continue.mp4 |
26.92Мб |
| 33. Missing Data-Groupby Continue.vtt |
4.10Кб |
| 33. Multiple Linear Regression.mp4 |
90.37Мб |
| 33. Multiple Linear Regression.vtt |
7.84Кб |
| 34. Agile Development Process.mp4 |
34.55Мб |
| 34. Agile Development Process.vtt |
5.81Кб |
| 34. Concat-Merge-Join.mp4 |
79.93Мб |
| 34. Concat-Merge-Join.vtt |
11.05Кб |
| 34. Critical Value Approach.mp4 |
57.08Мб |
| 34. Critical Value Approach.vtt |
7.59Кб |
| 34. Multiple Linear Regression Continues.mp4 |
69.98Мб |
| 34. Multiple Linear Regression Continues.vtt |
6.33Кб |
| 35. Agile Development Process Continues.mp4 |
44.66Мб |
| 35. Agile Development Process Continues.vtt |
7.37Кб |
| 35. Operations.mp4 |
48.08Мб |
| 35. Operations.vtt |
6.13Кб |
| 35. Regression Variables.mp4 |
101.45Мб |
| 35. Regression Variables.vtt |
6.16Кб |
| 35. Right and Left Sided Critical Approach.mp4 |
59.27Мб |
| 35. Right and Left Sided Critical Approach.vtt |
9.42Кб |
| 36. Challenges on dash board.mp4 |
32.28Мб |
| 36. Challenges on dash board.vtt |
4.48Кб |
| 36. Generalized Linear Model.mp4 |
93.70Мб |
| 36. Generalized Linear Model.vtt |
10.56Кб |
| 36. Import-Export.mp4 |
104.66Мб |
| 36. Import-Export.vtt |
11.54Кб |
| 36. P-Value Approach.mp4 |
79.84Мб |
| 36. P-Value Approach.vtt |
8.82Кб |
| 37. Building Users Expert Profile.mp4 |
62.77Мб |
| 37. Building Users Expert Profile.vtt |
8.14Кб |
| 37. Generalized Least Square.mp4 |
88.79Мб |
| 37. Generalized Least Square.vtt |
7.40Кб |
| 37. P-Value Approach Continues.mp4 |
55.80Мб |
| 37. P-Value Approach Continues.vtt |
7.84Кб |
| 37. Python Visualisation.mp4 |
49.04Мб |
| 37. Python Visualisation.vtt |
4.75Кб |
| 38. Hypothesis Testing.mp4 |
43.49Мб |
| 38. Hypothesis Testing.vtt |
9.28Кб |
| 38. KNN- Various Methods of Distance Measurements.mp4 |
52.96Мб |
| 38. KNN- Various Methods of Distance Measurements.vtt |
9.00Кб |
| 38. Mat Plotting.mp4 |
63.42Мб |
| 38. Mat Plotting.vtt |
11.43Кб |
| 38. Semantic Technologies.mp4 |
53.11Мб |
| 38. Semantic Technologies.vtt |
7.05Кб |
| 39. Left Tail Test.mp4 |
22.90Мб |
| 39. Left Tail Test.vtt |
4.66Кб |
| 39. Multiple Plot Subsections.mp4 |
62.39Мб |
| 39. Multiple Plot Subsections.vtt |
7.59Кб |
| 39. Overview of KNN- (Steps involved).mp4 |
71.10Мб |
| 39. Overview of KNN- (Steps involved).vtt |
10.01Кб |
| 39. Semantic Tools.mp4 |
53.66Мб |
| 39. Semantic Tools.vtt |
6.75Кб |
| 4. Adding the Test Value.mp4 |
82.62Мб |
| 4. Adding the Test Value.vtt |
82.63Мб |
| 4. Beta Value.mp4 |
48.28Мб |
| 4. Beta Value.vtt |
2.87Кб |
| 4. Big Data Machine Learning.mp4 |
50.99Мб |
| 4. Big Data Machine Learning.vtt |
7.27Кб |
| 4. Creating Data Scheme in AML.mp4 |
26.88Мб |
| 4. Creating Data Scheme in AML.vtt |
6.13Кб |
| 4. Data for Validation.mp4 |
103.30Мб |
| 4. Data for Validation.vtt |
6.67Кб |
| 4. Heatmap for Discretized Dataset.mp4 |
88.57Мб |
| 4. Heatmap for Discretized Dataset.vtt |
7.93Кб |
| 4. Learning about BIP Add-Ins.mp4 |
59.81Мб |
| 4. Learning about BIP Add-Ins.vtt |
6.88Кб |
| 4. MCMC Module of PYMC Implementation.mp4 |
43.41Мб |
| 4. MCMC Module of PYMC Implementation.vtt |
4.11Кб |
| 4. multidimensional db part 3.mp4 |
56.10Мб |
| 4. multidimensional db part 3.vtt |
10.26Кб |
| 4. NLP Installation.mp4 |
59.69Мб |
| 4. NLP Installation.vtt |
9.86Кб |
| 4. Regression and Classification Problems.mp4 |
62.38Мб |
| 4. Regression and Classification Problems.vtt |
8.03Кб |
| 4. Trading Companies and Stocks.mp4 |
97.65Мб |
| 4. Trading Companies and Stocks.vtt |
9.16Кб |
| 4. Types of Activation Functions.mp4 |
20.42Мб |
| 4. Types of Activation Functions.vtt |
3.25Кб |
| 4. Uses of Machine Learning.mp4 |
30.21Мб |
| 4. Uses of Machine Learning.vtt |
9.97Кб |
| 40. API Functionality.mp4 |
64.75Мб |
| 40. API Functionality.vtt |
7.99Кб |
| 40. BI Algorithm By Example.mp4 |
42.01Мб |
| 40. BI Algorithm By Example.vtt |
8.51Кб |
| 40. Data normalization and prediction on Test Data.mp4 |
86.41Мб |
| 40. Data normalization and prediction on Test Data.vtt |
8.00Кб |
| 40. Two Tail Test.mp4 |
41.64Мб |
| 40. Two Tail Test.vtt |
8.80Кб |
| 41. Benefits of BI.mp4 |
27.67Мб |
| 41. Benefits of BI.vtt |
8.13Кб |
| 41. Confidence Interval.mp4 |
52.67Мб |
| 41. Confidence Interval.vtt |
8.07Кб |
| 41. Improvement of Model Performance and ROC.mp4 |
85.09Мб |
| 41. Improvement of Model Performance and ROC.vtt |
10.68Кб |
| 41. Title of the Plot.mp4 |
91.75Мб |
| 41. Title of the Plot.vtt |
172.50Мб |
| 42. Benefits of BI Continues.mp4 |
43.66Мб |
| 42. Benefits of BI Continues.vtt |
8.10Кб |
| 42. Change Size of Articles.mp4 |
59.75Мб |
| 42. Change Size of Articles.vtt |
7.88Кб |
| 42. Decision Tree Classifier.mp4 |
57.80Мб |
| 42. Decision Tree Classifier.vtt |
7.48Кб |
| 42. Example of Confidence Interval.mp4 |
62.96Мб |
| 42. Example of Confidence Interval.vtt |
10.37Кб |
| 43. Amazon.com and Net Flix.mp4 |
38.96Мб |
| 43. Amazon.com and Net Flix.vtt |
7.26Кб |
| 43. More on Decision Tree Classifier.mp4 |
81.50Мб |
| 43. More on Decision Tree Classifier.vtt |
6.64Кб |
| 43. Normal and Non Normal Distribution.mp4 |
32.45Мб |
| 43. Normal and Non Normal Distribution.vtt |
9.04Кб |
| 43. Two Different Crops.mp4 |
54.92Мб |
| 43. Two Different Crops.vtt |
7.53Кб |
| 44. Mat Plotting Label.mp4 |
49.35Мб |
| 44. Mat Plotting Label.vtt |
6.41Кб |
| 44. Normality Test.mp4 |
45.72Мб |
| 44. Normality Test.vtt |
8.69Кб |
| 44. Pruning of Decision Trees.mp4 |
83.84Мб |
| 44. Pruning of Decision Trees.vtt |
6.78Кб |
| 44. What is Information Governance.mp4 |
49.97Мб |
| 44. What is Information Governance.vtt |
7.76Кб |
| 45. Decision Tree Remaining.mp4 |
61.11Мб |
| 45. Decision Tree Remaining.vtt |
5.52Кб |
| 45. Marker Color.mp4 |
72.66Мб |
| 45. Marker Color.vtt |
9.17Кб |
| 45. Normality Test Continues.mp4 |
40.97Мб |
| 45. Normality Test Continues.vtt |
9.01Кб |
| 45. Other BI Applications are used to store.mp4 |
45.27Мб |
| 45. Other BI Applications are used to store.vtt |
6.95Кб |
| 46. Create a New Dataframe.mp4 |
40.21Мб |
| 46. Create a New Dataframe.vtt |
4.83Кб |
| 46. Decision Tree Remaining Continues.mp4 |
46.70Мб |
| 46. Decision Tree Remaining Continues.vtt |
5.25Кб |
| 46. Designing and Implementing BI Program.mp4 |
46.72Мб |
| 46. Designing and Implementing BI Program.vtt |
5.84Кб |
| 46. Determining the Transformation.mp4 |
22.14Мб |
| 46. Determining the Transformation.vtt |
5.70Кб |
| 47. Change the Style.mp4 |
44.45Мб |
| 47. Change the Style.vtt |
5.92Кб |
| 47. ETL.mp4 |
37.37Мб |
| 47. ETL.vtt |
7.52Кб |
| 47. General concept of Random Forest.mp4 |
64.30Мб |
| 47. General concept of Random Forest.vtt |
10.90Кб |
| 47. T-Test.mp4 |
45.20Мб |
| 47. T-Test.vtt |
9.71Кб |
| 48. Ada Boosting and Ensemble Learning.mp4 |
95.21Мб |
| 48. Ada Boosting and Ensemble Learning.vtt |
10.89Кб |
| 48. ETL Continues.mp4 |
30.78Мб |
| 48. ETL Continues.vtt |
6.17Кб |
| 48. Index and Value.mp4 |
41.35Мб |
| 48. Index and Value.vtt |
5.21Кб |
| 48. T-Test Continue.mp4 |
47.22Мб |
| 48. T-Test Continue.vtt |
7.11Кб |
| 49. Data Visualization and Preparation.mp4 |
89.28Мб |
| 49. Data Visualization and Preparation.vtt |
9.94Кб |
| 49. Loading.mp4 |
35.06Мб |
| 49. Loading.vtt |
7.04Кб |
| 49. More on T-Test.mp4 |
55.52Мб |
| 49. More on T-Test.vtt |
8.59Кб |
| 49. Seaborn-Statistical Data Visualization.mp4 |
59.06Мб |
| 49. Seaborn-Statistical Data Visualization.vtt |
7.44Кб |
| 5. Basic Data Manipulation in R.mp4 |
71.68Мб |
| 5. Basic Data Manipulation in R.vtt |
8.08Кб |
| 5. BIP_Into_5_BIP_AddIn2 and BIP_Into_6.mp4 |
64.35Мб |
| 5. BIP_Into_5_BIP_AddIn2 and BIP_Into_6.vtt |
7.05Кб |
| 5. dbms platform.mp4 |
11.80Мб |
| 5. dbms platform.vtt |
2.86Кб |
| 5. DEA with Input or Profit and Loss.mp4 |
87.78Мб |
| 5. DEA with Input or Profit and Loss.vtt |
7.36Кб |
| 5. Distinguished Methods with Single.mp4 |
43.58Мб |
| 5. Distinguished Methods with Single.vtt |
4.05Кб |
| 5. Emerging Trends Machine Learning.mp4 |
72.39Мб |
| 5. Emerging Trends Machine Learning.vtt |
7.85Кб |
| 5. Examples with tensorflow by Google.mp4 |
57.34Мб |
| 5. Examples with tensorflow by Google.vtt |
8.97Кб |
| 5. Finding the Corelation.mp4 |
72.68Мб |
| 5. Finding the Corelation.vtt |
7.68Кб |
| 5. Index to the ID Column.mp4 |
68.20Мб |
| 5. Index to the ID Column.vtt |
8.34Кб |
| 5. Invaild Value and Varible Target in AML.mp4 |
4.41Мб |
| 5. Invaild Value and Varible Target in AML.vtt |
1.04Кб |
| 5. NLP - Demo.mp4 |
89.44Мб |
| 5. NLP - Demo.vtt |
10.50Кб |
| 5. Optimizing Back Propagation.mp4 |
44.98Мб |
| 5. Optimizing Back Propagation.vtt |
6.86Кб |
| 5. Predict Value.mp4 |
77.82Мб |
| 5. Predict Value.vtt |
5.60Кб |
| 5. Running the MCMC Module.mp4 |
42.54Мб |
| 5. Running the MCMC Module.vtt |
4.76Кб |
| 50. seaborn library.mp4 |
95.97Мб |
| 50. seaborn library.vtt |
12.55Кб |
| 50. Test of Independence.mp4 |
53.96Мб |
| 50. Test of Independence.vtt |
10.35Кб |
| 50. Tuning Random Forest Model.mp4 |
63.40Мб |
| 50. Tuning Random Forest Model.vtt |
7.11Кб |
| 50. Type 2 Dimension.mp4 |
51.92Мб |
| 50. Type 2 Dimension.vtt |
8.55Кб |
| 51. Evaluation of Random Forest Model Performance.mp4 |
65.88Мб |
| 51. Evaluation of Random Forest Model Performance.vtt |
6.70Кб |
| 51. Example of Test of Independence.mp4 |
51.13Мб |
| 51. Example of Test of Independence.vtt |
9.14Кб |
| 51. Jointplot.mp4 |
81.97Мб |
| 51. Jointplot.vtt |
9.91Кб |
| 51. Loading Fact Tables.mp4 |
47.39Мб |
| 51. Loading Fact Tables.vtt |
7.37Кб |
| 52. Genearl Idea.mp4 |
41.40Мб |
| 52. Genearl Idea.vtt |
7.26Кб |
| 52. Goodness of Fit Test.mp4 |
40.22Мб |
| 52. Goodness of Fit Test.vtt |
6.49Кб |
| 52. Introduction to Kmeans Clustering.mp4 |
75.07Мб |
| 52. Introduction to Kmeans Clustering.vtt |
11.19Кб |
| 52. Pairplot.mp4 |
109.81Мб |
| 52. Pairplot.vtt |
11.65Кб |
| 53. Barplot.mp4 |
97.54Мб |
| 53. Barplot.vtt |
10.46Кб |
| 53. Conceptual Model.mp4 |
36.90Мб |
| 53. Conceptual Model.vtt |
7.77Кб |
| 53. Example of Goodness of Fit Test.mp4 |
37.93Мб |
| 53. Example of Goodness of Fit Test.vtt |
6.77Кб |
| 53. Kmeans Elbow Point and Dataset.mp4 |
90.51Мб |
| 53. Kmeans Elbow Point and Dataset.vtt |
10.16Кб |
| 54. Boxplot.mp4 |
49.36Мб |
| 54. Boxplot.vtt |
5.75Кб |
| 54. Conceptual Model Continues.mp4 |
49.97Мб |
| 54. Conceptual Model Continues.vtt |
8.36Кб |
| 54. Co-Variance.mp4 |
17.99Мб |
| 54. Co-Variance.vtt |
5.02Кб |
| 54. Example of Kmeans Dataset.mp4 |
122.65Мб |
| 54. Example of Kmeans Dataset.vtt |
10.86Кб |
| 55. Co-Variance Continues.mp4 |
24.04Мб |
| 55. Co-Variance Continues.vtt |
7.33Кб |
| 55. Creating a Graph for Kmeans Clustering.mp4 |
126.69Мб |
| 55. Creating a Graph for Kmeans Clustering.vtt |
10.47Кб |
| 55. On Going Or Future Works.mp4 |
51.43Мб |
| 55. On Going Or Future Works.vtt |
11.29Кб |
| 55. Stripplot.mp4 |
78.06Мб |
| 55. Stripplot.vtt |
7.15Кб |
| 56. Creating a Graph for Kmeans Clustering Continues.mp4 |
90.64Мб |
| 56. Creating a Graph for Kmeans Clustering Continues.vtt |
7.07Кб |
| 56. Matrix.mp4 |
92.83Мб |
| 56. Matrix.vtt |
0б |
| 56. Why Meta Data.mp4 |
42.77Мб |
| 56. Why Meta Data.vtt |
12.39Кб |
| 57. Aggregation Function of Clustering.mp4 |
78.46Мб |
| 57. Aggregation Function of Clustering.vtt |
8.33Кб |
| 57. Essentials Capabilities.mp4 |
27.42Мб |
| 57. Essentials Capabilities.vtt |
7.38Кб |
| 57. Matrix Continue.mp4 |
33.32Мб |
| 57. Matrix Continue.vtt |
3.18Кб |
| 58. Common Warehouse Metamodels.mp4 |
30.76Мб |
| 58. Common Warehouse Metamodels.vtt |
6.82Кб |
| 58. Conditional Probability with Bayes Algorithm.mp4 |
82.98Мб |
| 58. Conditional Probability with Bayes Algorithm.vtt |
9.51Кб |
| 58. Grid.mp4 |
110.79Мб |
| 58. Grid.vtt |
8.67Кб |
| 59. Data Advantage Group.mp4 |
54.00Мб |
| 59. Data Advantage Group.vtt |
10.12Кб |
| 59. Grid Continue.mp4 |
56.60Мб |
| 59. Grid Continue.vtt |
5.77Кб |
| 59. Venn Diagram Naive Bayes Classification.mp4 |
61.65Мб |
| 59. Venn Diagram Naive Bayes Classification.vtt |
8.74Кб |
| 6. Analyzing both the Plots.mp4 |
72.83Мб |
| 6. Analyzing both the Plots.vtt |
5.84Кб |
| 6. BIP_Into_7_Customized Reports Overview.mp4 |
59.34Мб |
| 6. BIP_Into_7_Customized Reports Overview.vtt |
6.62Кб |
| 6. Briefing on Tensor Flow.mp4 |
35.78Мб |
| 6. Briefing on Tensor Flow.vtt |
5.06Кб |
| 6. Data Mining.mp4 |
43.82Мб |
| 6. Data Mining.vtt |
7.88Кб |
| 6. Density for Numeric Attribute.mp4 |
87.96Мб |
| 6. Density for Numeric Attribute.vtt |
6.64Кб |
| 6. Efficiency Profit and Loss.mp4 |
64.71Мб |
| 6. Efficiency Profit and Loss.vtt |
5.28Кб |
| 6. ML Models in AML.mp4 |
58.71Мб |
| 6. ML Models in AML.vtt |
12.42Кб |
| 6. Model on Data Set.mp4 |
82.18Мб |
| 6. Model on Data Set.vtt |
8.68Кб |
| 6. More on Data Manipulation in R.mp4 |
62.14Мб |
| 6. More on Data Manipulation in R.vtt |
6.55Кб |
| 6. Multiple Variant Testing Using Hierarchial Model.mp4 |
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| 6. Multiple Variant Testing Using Hierarchial Model.vtt |
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| 6. Performance Value.mp4 |
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| 6. Replacing Contractions.mp4 |
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| 6. Setting up the Workstation.mp4 |
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| 6. Setting up the Workstation.vtt |
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| 6. technical non technical infrastructre part 1.mp4 |
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| 6. technical non technical infrastructre part 1.vtt |
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| 60. Component OF Bayes Theorem using Frequency Table.mp4 |
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| 60. Component OF Bayes Theorem using Frequency Table.vtt |
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| 60. DBMS Meta Data Tips.mp4 |
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| 60. DBMS Meta Data Tips.vtt |
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| 60. Style.mp4 |
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| 60. Style.vtt |
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| 61. For Building The Dataware house(Extraction Team).mp4 |
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| 61. For Building The Dataware house(Extraction Team).vtt |
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| 61. Naive Bayes Classification Algorithm and Laplace Estimator.mp4 |
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| 61. Naive Bayes Classification Algorithm and Laplace Estimator.vtt |
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| 61. Python Libraries Conclusion.mp4 |
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| 61. Python Libraries Conclusion.vtt |
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| 62. Example of Naive Bayes Classification.mp4 |
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| 62. Example of Naive Bayes Classification.vtt |
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| 62. Introduction To Conda Envirement.mp4 |
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| 62. Introduction To Conda Envirement.vtt |
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| 62. Meta Data Essentials For IT.mp4 |
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| 62. Meta Data Essentials For IT.vtt |
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| 63. Business Metadata.mp4 |
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| 63. Business Metadata.vtt |
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| 63. Example of Naive Bayes Classification Continues.mp4 |
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| 63. Example of Naive Bayes Classification Continues.vtt |
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| 63. Scikit Learn.mp4 |
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| 63. Scikit Learn.vtt |
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| 64. Business Meta Data (Continues).mp4 |
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| 64. Scikit Learn Continue.mp4 |
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| 64. Spam and Ham Messages in Word Cloud.mp4 |
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| 64. Spam and Ham Messages in Word Cloud.vtt |
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| 65. Datasets.mp4 |
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| 65. Datasets.vtt |
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| 65. Implementation of Dictionary and Document Term Matrix.mp4 |
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| 65. Implementation of Dictionary and Document Term Matrix.vtt |
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| 65. Project Planning.mp4 |
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| 65. Project Planning.vtt |
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| 66. California Dataset.mp4 |
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| 66. California Dataset.vtt |
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| 66. Executes the Function Naive Bayes.mp4 |
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| 66. Project Planning (Continues).mp4 |
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| 67. Data Visualization.mp4 |
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| 67. Data Visualization.vtt |
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| 67. Deployment Process.mp4 |
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| 67. Deployment Process.vtt |
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| 67. Support Vector Machine with Black Box Method.mp4 |
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| 67. Support Vector Machine with Black Box Method.vtt |
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| 68. Chapter Outline.mp4 |
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| 68. Chapter Outline.vtt |
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| 68. Datavisualization Continue.mp4 |
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| 68. Datavisualization Continue.vtt |
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| 68. Linearly and Non- Linearly Support Vector Machine.mp4 |
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| 68. Linearly and Non- Linearly Support Vector Machine.vtt |
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| 69. Break-Even Analysis.mp4 |
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| 69. Break-Even Analysis.vtt |
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| 69. Downloading a Test Data.mp4 |
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| 69. Downloading a Test Data.vtt |
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| 69. Kernal Trick.mp4 |
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| 69. Kernal Trick.vtt |
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| 7. Basic Data Manipulation in R - Practical.mp4 |
71.74Мб |
| 7. Basic Data Manipulation in R - Practical.vtt |
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| 7. BIP_Into_8_Developing Reports Overview.mp4 |
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| 7. BIP_Into_8_Developing Reports Overview.vtt |
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| 7. Data Mining Continues.mp4 |
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| 7. Data Mining Continues.vtt |
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| 7. Defining the Lengths.mp4 |
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| 7. Defining the Lengths.vtt |
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| 7. Example of Multiple Variant Testing.mp4 |
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| 7. Example of Multiple Variant Testing.vtt |
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| 7. Fals Positive Rate.mp4 |
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| 7. Fals Positive Rate.vtt |
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| 7. Installation of Tensor Flow.mp4 |
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| 7. Manging ML Object in AML.mp4 |
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| 7. Method for Train Control.mp4 |
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| 7. Missing Value Imputation.mp4 |
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| 7. Missing Value Imputation.vtt |
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| 7. Rank Functions.mp4 |
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| 7. technical non technical infrastructre part 2.mp4 |
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| 7. Tokenize Dataset.mp4 |
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| 7. Understanding program languages.mp4 |
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| 7. Understanding program languages.vtt |
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| 70. Examples Of Break-Even Analysis.mp4 |
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| 70. Examples Of Break-Even Analysis.vtt |
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| 70. Gaussian RBF Kernal and OCR with SVMs.mp4 |
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| 70. Gaussian RBF Kernal and OCR with SVMs.vtt |
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| 70. Population Parameter.mp4 |
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| 70. Population Parameter.vtt |
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| 71. Examples of Gaussian RBF Kernal and OCR with SVMs.mp4 |
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| 71. Examples of Gaussian RBF Kernal and OCR with SVMs.vtt |
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| 71. Multivirate Analysis.mp4 |
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| 71. Multivirate Analysis.vtt |
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| 71. Processing.mp4 |
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| 72. Multivirate Analysis (Continues).mp4 |
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| 72. Multivirate Analysis (Continues).vtt |
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| 72. Null Values with Median Value.mp4 |
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| 72. Null Values with Median Value.vtt |
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| 72. Summary of Support Vector Machine.mp4 |
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| 72. Summary of Support Vector Machine.vtt |
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| 73. Feature Selection Dimension Reduction Technique.mp4 |
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| 73. Feature Selection Dimension Reduction Technique.vtt |
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| 73. Graphs.mp4 |
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| 73. Graphs.vtt |
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| 73. Replace Missing Values.mp4 |
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| 73. Replace Missing Values.vtt |
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| 74. Feature Extraction Dimension Reduction Technique.mp4 |
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| 74. Feature Extraction Dimension Reduction Technique.vtt |
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| 74. Label Enconder.mp4 |
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| 74. Label Enconder.vtt |
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| 74. Why Meta Data Is Important.mp4 |
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| 74. Why Meta Data Is Important.vtt |
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| 75. Dimension Reduction Technique Example.mp4 |
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| 75. Dimension Reduction Technique Example.vtt |
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| 75. Import Labelencoder.mp4 |
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| 75. Import Labelencoder.vtt |
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| 75. System Development.mp4 |
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| 75. System Development.vtt |
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| 76. Custom Transformation.mp4 |
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| 76. Custom Transformation.vtt |
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| 76. Dimension Reduction Technique Example Continues.mp4 |
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| 76. Dimension Reduction Technique Example Continues.vtt |
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| 76. Project Risk Assesment Factors.mp4 |
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| 76. Project Risk Assesment Factors.vtt |
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| 77. Introduction Principal Component Analysis.mp4 |
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| 77. Introduction Principal Component Analysis.vtt |
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| 77. Managing Project Time.mp4 |
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| 77. Managing Project Time.vtt |
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| 77. Transformer Custom Transformer.mp4 |
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| 77. Transformer Custom Transformer.vtt |
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| 78. Housing with Custom Colums.mp4 |
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| 78. Housing with Custom Colums.vtt |
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| 78. Prototyping Benefits.mp4 |
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| 78. Prototyping Benefits.vtt |
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| 78. Steps of PCA.mp4 |
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| 78. Steps of PCA.vtt |
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| 79. Incremental Development.mp4 |
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| 79. Incremental Development.vtt |
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| 79. Numeric Hosing Data.mp4 |
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| 79. Numeric Hosing Data.vtt |
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| 79. Steps of PCA Continues.mp4 |
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| 79. Steps of PCA Continues.vtt |
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| 8. Assigning a Training Set.mp4 |
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| 8. change control board part 1.mp4 |
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| 8. Create a Vector.mp4 |
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| 8. Create a Vector.vtt |
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| 8. Creating DataSource Handson.mp4 |
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| 8. Creating DataSource Handson.vtt |
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| 8. Example of Multiple Variant Testing Continues.mp4 |
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| 8. Example of Multiple Variant Testing Continues.vtt |
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| 8. Implementatiion on Neural Package.mp4 |
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| 8. Implementatiion on Neural Package.vtt |
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| 8. Remove Stopwords.mp4 |
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| 8. RHS Constaints.mp4 |
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| 8. RHS Constaints.vtt |
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| 8. Showing Report Views on Application.mp4 |
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| 8. Substituting Features with Value.mp4 |
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| 8. Substituting Features with Value.vtt |
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| 8. Supervised and Unsupervised.mp4 |
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| 8. Supervised and Unsupervised.vtt |
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| 8. Understanding and Functions of Jupyter.mp4 |
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| 8. Understanding and Functions of Jupyter.vtt |
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| 8. Using Different Clusters.mp4 |
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| 8. Using Different Clusters.vtt |
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| 80. Eigen Values.mp4 |
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| 80. Eigen Values.vtt |
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| 80. Incremental Development(Continues).mp4 |
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| 80. Incremental Development(Continues).vtt |
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| 80. Liner Regression.mp4 |
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| 80. Liner Regression.vtt |
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| 81. Eigen Vectors.mp4 |
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| 81. Eigen Vectors.vtt |
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| 81. Fine Tuning Model.mp4 |
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| 81. Fine Tuning Model.vtt |
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| 81. What is Cluster Analysis.mp4 |
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| 81. What is Cluster Analysis.vtt |
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| 82. Fine Tuning Model Continue.mp4 |
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| 82. Fine Tuning Model Continue.vtt |
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| 82. Principal Component Analysis using Pr-Comp.mp4 |
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| 82. Principal Component Analysis using Pr-Comp.vtt |
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| 82. Types Of Clusters.mp4 |
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| 82. Types Of Clusters.vtt |
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| 83. Cluster Benefits.mp4 |
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| 83. Cluster Benefits.vtt |
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| 83. Principal Component Analysis using Pr-Comp Continues.mp4 |
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| 83. Principal Component Analysis using Pr-Comp Continues.vtt |
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| 83. Quick-Recap.mp4 |
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| 83. Quick-Recap.vtt |
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| 84. C Bind Type in PCA.mp4 |
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| 84. C Bind Type in PCA.vtt |
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| 84. Kmeans Clustering Method.mp4 |
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| 84. Kmeans Clustering Method.vtt |
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| 84. Tensorflow.mp4 |
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| 84. Tensorflow.vtt |
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| 85. R Type Model.mp4 |
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| 85. R Type Model.vtt |
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| 85. Tensorflow-Hello-World.mp4 |
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| 85. Tensorflow-Hello-World.vtt |
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| 85. What Is The Problem With PAM.mp4 |
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| 85. What Is The Problem With PAM.vtt |
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| 86. Basic Ops.mp4 |
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| 86. Basic Ops.vtt |
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| 86. BIRCH (1996).mp4 |
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| 86. BIRCH (1996).vtt |
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| 86. Black Box Method in Neural Network.mp4 |
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| 86. Black Box Method in Neural Network.vtt |
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| 87. Basic Ops Continue.mp4 |
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| 87. Basic Ops Continue.vtt |
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| 87. Characteristics of a Neural Networks.mp4 |
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| 87. Characteristics of a Neural Networks.vtt |
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| 87. Density Rechable And Density Conected.mp4 |
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| 87. Density Rechable And Density Conected.vtt |
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| 88. Denclue Technical Issues.mp4 |
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| 88. Denclue Technical Issues.vtt |
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| 88. More on Basic Ops.mp4 |
67.83Мб |
| 88. More on Basic Ops.vtt |
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| 88. Network Topology of a Neural Networks.mp4 |
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| 88. Network Topology of a Neural Networks.vtt |
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| 89. Eager-Mode.mp4 |
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| 89. Eager-Mode.vtt |
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| 89. The Wave Cluster Algorithm.mp4 |
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| 89. The Wave Cluster Algorithm.vtt |
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| 89. Weight Adjustment and Case Update.mp4 |
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| 89. Weight Adjustment and Case Update.vtt |
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| 9. 2.7 Problem and Solution.mp4 |
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| 9. 2.7 Problem and Solution.vtt |
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| 9. change control board part 2.mp4 |
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| 9. change control board part 2.vtt |
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| 9. Creating DataSource Handson Continues.mp4 |
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| 9. Creating DataSource Handson Continues.vtt |
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| 9. Implementatiion on Neural Package Continues.mp4 |
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| 9. Implementatiion on Neural Package Continues.vtt |
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| 9. Imputing a Row using at Command.mp4 |
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| 9. Imputing a Row using at Command.vtt |
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| 9. Learning of Jupyter installation.mp4 |
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| 9. Learning of Jupyter installation.vtt |
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| 9. Mean Absolute Error.mp4 |
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| 9. Mean Absolute Error.vtt |
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| 9. Profit and Loss Report.mp4 |
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| 9. Profit and Loss Report.vtt |
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| 9. Sampling Method in Machine Learning.mp4 |
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| 9. Sampling Method in Machine Learning.vtt |
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| 9. Siebel Applets ‚ Business Obejct and Business Components Part 1.mp4 |
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| 9. Siebel Applets ‚ Business Obejct and Business Components Part 1.vtt |
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| 9. Stemming and Lemmatization.mp4 |
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| 9. Stemming and Lemmatization.vtt |
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| 90. Concept.mp4 |
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| 90. Concept.vtt |
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| 90. Introduction Model Building in R.mp4 |
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| 90. Introduction Model Building in R.vtt |
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| 90. More On Conceptual Clustering.mp4 |
60.84Мб |
| 90. More On Conceptual Clustering.vtt |
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| 91. Clustering in Quest.mp4 |
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| 91. Clustering in Quest.vtt |
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| 91. Installing the Package of Model Building in R.mp4 |
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| 91. Installing the Package of Model Building in R.vtt |
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| 91. Linear-Regression.mp4 |
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| 91. Linear-Regression.vtt |
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| 92. Linear-Model.mp4 |
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| 92. Linear-Model.vtt |
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| 92. Nodes in Model Building in R.mp4 |
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| 92. Nodes in Model Building in R.vtt |
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| 92. Why Constraints Based Cluster Analysis.mp4 |
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| 92. Why Constraints Based Cluster Analysis.vtt |
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| 93. Example of Model Building in R.mp4 |
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| 93. Example of Model Building in R.vtt |
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| 93. Matrix Multiplication Function.mp4 |
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| 93. Matrix Multiplication Function.vtt |
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| 93. What Is Outlier Discovery.mp4 |
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| 93. What Is Outlier Discovery.vtt |
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| 94. Practice for a Simple Linear Model.mp4 |
24.42Мб |
| 94. Practice for a Simple Linear Model.vtt |
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| 94. Segmentation In Data Mining.mp4 |
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| 94. Segmentation In Data Mining.vtt |
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| 94. Time Series Analysis.mp4 |
68.49Мб |
| 94. Time Series Analysis.vtt |
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| 95. Bottle Neck Of GSP & Spade.mp4 |
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| 95. Bottle Neck Of GSP & Spade.vtt |
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| 95. Cost Function.mp4 |
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| 95. Cost Function.vtt |
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| 95. Pattern in Time Series Data.mp4 |
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| 95. Pattern in Time Series Data.vtt |
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| 96. Creative Optimizer.mp4 |
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| 96. Creative Optimizer.vtt |
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| 96. Time Series Modelling.mp4 |
55.94Мб |
| 96. Time Series Modelling.vtt |
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| 96. Why Deal with Sequential Data.mp4 |
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| 96. Why Deal with Sequential Data.vtt |
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| 97. Algorithm Definition.mp4 |
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| 97. Algorithm Definition.vtt |
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| 97. Moving Average Model.mp4 |
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| 97. Moving Average Model.vtt |
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| 97. RR Input and Output Value.mp4 |
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| 97. RR Input and Output Value.vtt |
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| 98. Auto Correlation Function.mp4 |
43.08Мб |
| 98. Auto Correlation Function.vtt |
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| 98. Introduction To Regression Analysis.mp4 |
35.34Мб |
| 98. Introduction To Regression Analysis.vtt |
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| 98. Logistic-Regression.mp4 |
51.55Мб |
| 98. Logistic-Regression.vtt |
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| 99. Global Variabales Initializer.mp4 |
36.50Мб |
| 99. Global Variabales Initializer.vtt |
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| 99. Inference of ACF and PFCF.mp4 |
45.22Мб |
| 99. Inference of ACF and PFCF.vtt |
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| 99. Regression Model.mp4 |
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| 99. Regression Model.vtt |
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