44:23

#AI & #ML Lecture 14: Logistic Regression & Ensemble Learning - Bagging & Boosting - AdaBoost

SECourses

Shared 16/01/2021

01:29:24

#AI & #ML Lecture 13: Conditional Probability & Probabilistic Models, Joint Distribution, Random Var

SECourses

Shared 10/01/2021

01:46:25

#AI & #ML Lecture 15: Unsupervised Learning, Clustering Algorithms, Hierarchical Clustering, K-Means

SECourses

Shared 16/01/2021

57:48

#AI & #ML Lecture 12 : Large & Soft Margin Classifiers, Support Vector Machines (SVM), Loss Function

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Shared 04/01/2021

01:14:36

#AI & #ML Lecture 11 : Gradient Descent, Loss Function, Sparse & Missing Data, Regularization, L1 L2

SECourses

Shared 29/12/2020

01:20:18

#AI & #ML Lecture 10: What Is Learning To Rank (LTR), Pointwise, Pairwise, and Listwise Ranking

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Shared 19/12/2020

01:48:42

#AI & #ML Lecture 9 : Supervised Evaluation, K-Fold Cross Validation & Multiclass Classification

SECourses

Shared 13/12/2020

01:57:12

#AI & #ML Lecture 5 : Learning a Linear Classifier, Perceptron Learning & Hyperplanes, KNN, Neural

SECourses

Shared 08/11/2020

01:38:39

#AI & #ML Lecture 4 : Proper Model Training & Testing, KNN Algorithm & Practical Example, Accuracy

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Shared 30/10/2020

01:57:16

#AI & #ML Lecture 3 : Practical Example of Decision Trees with C# and Accord.NET, Cross Validation

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Shared 24/10/2020

01:51:47

#AI & #ML Lecture 2 : Introduction to Machine Learning & Decision Trees, Supervised Learning

SECourses

Shared 19/10/2020

01:21:14

#AI & #ML Lecture 1 : Introduction to Artificial Intelligence (AI) and Machine Learning (ML)

SECourses

Shared 13/10/2020