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Computational Learning
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Lecture 18 (Evaluation Measures)
1:57:43
Lecture 18 (Evaluation Measures)
Lecture 24 (Deep Learning)
1:28:11
Lecture 24 (Deep Learning)
Lecture 23 (ML) - Part II
57:53
Lecture 23 (ML) - Part II
Lecture 22 (ML)-Part I
1:25:14
Lecture 22 (ML)-Part I
Lecture 17 (CNN)
1:30:22
Lecture 17 (CNN)
Lecture 16 (Hierarchical & Probabilistic Clustering, Association Rule Mining, Apriori Algorithm)
1:41:28
Lecture 16 (Hierarchical & Probabilistic Clustering, Association Rule Mining, Apriori Algorithm)
Lecture 21 (NLP)-Part II
1:28:40
Lecture 21 (NLP)-Part II
Lecture 20 (NLP)-Part I
1:21:15
Lecture 20 (NLP)-Part I
Lecture 15 (Clustering) - Part II
36:42
Lecture 15 (Clustering) - Part II
Lecture 14 (Clustering) -Part I
1:17:53
Lecture 14 (Clustering) -Part I
Lecture 19 (Games) - Part III
26:19
Lecture 19 (Games) - Part III
Lecture 18 (Games) - Part III
50:21
Lecture 18 (Games) - Part III
Lecture 17 (Games)-Part II
11:39
Lecture 17 (Games)-Part II
Lecture 16 (Games)-Part I
1:21:39
Lecture 16 (Games)-Part I
Lecture 15 (Backtracking-II)
52:53
Lecture 15 (Backtracking-II)
Lecture 14 (Backtracking-I)
55:44
Lecture 14 (Backtracking-I)
Lecture 13 (Information Gain, Gain Ratio, Gini Index, Prunning DT and Random Forest)
1:24:24
Lecture 13 (Information Gain, Gain Ratio, Gini Index, Prunning DT and Random Forest)
Lecture 12 (Remaining Activation Functions, Neural Network, And Decision Trees)
1:41:56
Lecture 12 (Remaining Activation Functions, Neural Network, And Decision Trees)
Lecture 13 (Crypto Arithmetic Problems)
41:56
Lecture 13 (Crypto Arithmetic Problems)
Lecture 12 (Genetic III, Constraint Satisfaction, Crypto Arithmetic)
44:44
Lecture 12 (Genetic III, Constraint Satisfaction, Crypto Arithmetic)
Lecture 11 (Genetic Algorithm - Part II)
40:20
Lecture 11 (Genetic Algorithm - Part II)
Lecture 11 (Neural Netowrk and Activation Functions)
48:47
Lecture 11 (Neural Netowrk and Activation Functions)
Lecture 10 (Loss Function and Gradient Descent of Logistic Regression)
1:58:42
Lecture 10 (Loss Function and Gradient Descent of Logistic Regression)
Lecture 9: Hill Climbing
1:20:43
Lecture 9: Hill Climbing
Lecture 10: Simulated Annealing and Genetic Algorithm (part-I)
1:21:12
Lecture 10: Simulated Annealing and Genetic Algorithm (part-I)
Lecture 9 (Supervised Learning Techniques): Logistic Regression
1:09:26
Lecture 9 (Supervised Learning Techniques): Logistic Regression
Lecture 8 (Data Preparation and Feature Selection
55:51
Lecture 8 (Data Preparation and Feature Selection
Lecture 7 (Data Preparation)
47:15
Lecture 7 (Data Preparation)
Lecture 6 (Understanding data through visualization)
51:05
Lecture 6 (Understanding data through visualization)
Lecture 5 (Understanding data through statistics)
42:56
Lecture 5 (Understanding data through statistics)
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