B Tech / M Tech Project Evaluation: Getting to know the value addition correctly and concisely.
Analytics with Snow
B Tech / M Tech Project Evaluation: Getting to know the value addition correctly and concisely.
2:45
Practical Data Science [Tips] - Minimize the Lie Factor
Analytics with Snow
Practical Data Science [Tips] - Minimize the Lie Factor
3:44
Practical Data Science [Tips] - Enhance the Data-Ink ratio
Analytics with Snow
Practical Data Science [Tips] - Enhance the Data-Ink ratio
3:17
Augmented Analytics - #Shorts Video
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Augmented Analytics - #Shorts Video
1:13
Randomized Algorithms: 1.6 Median Finding Algorithm - Select(S,k) routine in more detail.
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Randomized Algorithms: 1.6 Median Finding Algorithm - Select(S,k) routine in more detail.
2:27
Randomized Algorithms: 1.5 Median Finding Algorithm - Avoid choosing off-cutters as splitters
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Randomized Algorithms: 1.5 Median Finding Algorithm - Avoid choosing off-cutters as splitters
3:11
Randomized Algorithms: 1.4 Median Finding Algorithm - Select a good splitter in a practical setting.
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Randomized Algorithms: 1.4 Median Finding Algorithm - Select a good splitter in a practical setting.
4:50
Randomized Algorithms: 1.3 Median Finding Algorithm - Role of a good splitter
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Randomized Algorithms: 1.3 Median Finding Algorithm - Role of a good splitter
3:42
Randomized Algorithms: 1.2 Median Finding Algorithm - Walkaround of Pseudocode
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Randomized Algorithms: 1.2 Median Finding Algorithm - Walkaround of Pseudocode
8:14
Randomized Algorithms: 1.1 Median Finding Algorithm - Introduction to Median
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Randomized Algorithms: 1.1 Median Finding Algorithm - Introduction to Median
4:03
Building Secure Machine Learning Models: Understanding Output Integrity Attack
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Building Secure Machine Learning Models: Understanding Output Integrity Attack
1:37
Building Secure Machine Learning Models: Combatting Stealthy Channel Attacks
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Building Secure Machine Learning Models: Combatting Stealthy Channel Attacks
0:45
Curve Fitting in Data Science and Machine Learning: Polynomial Fit (Implementation in Python)
Analytics with Snow
Curve Fitting in Data Science and Machine Learning: Polynomial Fit (Implementation in Python)
19:23
Algorithms Analysis and Design: Minimum Cost Spanning Tree - Kruskal's Algorithm (Session 3)
Analytics with Snow
Algorithms Analysis and Design: Minimum Cost Spanning Tree - Kruskal's Algorithm (Session 3)
5:19
Algorithm Analysis and Design: Minimum Cost Spanning Tree - Kruskal's Algorithm (Session 2)
Analytics with Snow
Algorithm Analysis and Design: Minimum Cost Spanning Tree - Kruskal's Algorithm (Session 2)
4:48
Algorithms Analysis and Design: Minimum Cost Spanning Tree - Introduction (Session 1)
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Algorithms Analysis and Design: Minimum Cost Spanning Tree - Introduction (Session 1)
4:26
Algorithms Analysis and Design: Basic Categorization of Problems
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Algorithms Analysis and Design: Basic Categorization of Problems
4:15
Data Fallacies to Avoid: Survivorship Bias
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Data Fallacies to Avoid: Survivorship Bias
8:29
Algorithms Analysis and Design: Master's Theorem - Practice Problem 4
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Algorithms Analysis and Design: Master's Theorem - Practice Problem 4
7:20
Algorithms Analysis and Design: Master's Theorem - Practice Problem 3
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Algorithms Analysis and Design: Master's Theorem - Practice Problem 3
4:17
Algorithms Analysis and Design: Master's Theorem - Practice Problem 2
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Algorithms Analysis and Design: Master's Theorem - Practice Problem 2
4:05
Algorithm Analysis and Design: Master's Theorem - Practice Problem 1
Analytics with Snow
Algorithm Analysis and Design: Master's Theorem - Practice Problem 1
4:59
Algorithm Analysis and Design: Master's Theorem - Introduction
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Algorithm Analysis and Design: Master's Theorem - Introduction
8:32
Data Fallacies to Avoid: Data Dredging
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Data Fallacies to Avoid: Data Dredging
9:17
Data Fallacies to Avoid: Cobra Effect
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Data Fallacies to Avoid: Cobra Effect
13:07
Data Analytics - Data Fallacies to Avoid: Cherry Picking
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Data Analytics - Data Fallacies to Avoid: Cherry Picking
11:34
Natural Language Processing - Zipf Law [Glossary]
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Natural Language Processing - Zipf Law [Glossary]
5:06
Natural Language Processing - Linguistic Competence Vs Performance [Glossary]
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Natural Language Processing - Linguistic Competence Vs Performance [Glossary]
5:49
Regression Models in Machine Learning: Isotonic Regression (monotone fit from predictor to target)
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Regression Models in Machine Learning: Isotonic Regression (monotone fit from predictor to target)
22:42
Natural Language Processing - Rationalist Vs Empiricist [Glossary]
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Natural Language Processing - Rationalist Vs Empiricist [Glossary]
5:50
Natural Language Processing - Corpus [Glossary]
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Natural Language Processing - Corpus [Glossary]
2:49
R-Squared Statistics: Select a superior regression model.
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R-Squared Statistics: Select a superior regression model.
26:25
Time Complexity for Least Squares Regression problem.
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Time Complexity for Least Squares Regression problem.
12:24