Discriminant Analysis Intuition (13:12).
Explaining the code of visualization (9:53).Visualizing the Decision Boundaries of KNN (13:06).Your Data Preproprocessing Timplate (3:58).Dealing with Categorical Data (Part 2) (6:20).Dealing with Categorical Data (Part 1) (9:50).Computing Accuracy, Error Rate, Specificity and Sensitivity (5:10).More Customization and information while generating ROC (6:25).Generating ROC Curve based on the testing data (8:45).Comparing two classifiers with holdout (13:16).Classification Loss, Margins, Predictions and Edge for cross validated models (10:49).Classification Margins and Edge (15:23).Determining the classification loss (7:59).Making Predictions with the Models (8:06).Cross validition options (Part 2) (10:08).Cross validition options (Part 1) (10:07).Properties of SVM learned model in MATLAB (12:46).Properties of the Discriminant Analysis Learned Model in MATLAB (7:03).Intuition of Discriminant Analysis (6:44).Properties at the Classifer Built Time (7:25).Node Related Properties of Decision Trees (9:20).Properties of the Decision Trees (14:24).A Final note on Naive Bayesain Model (3:00).Building a model with categorical data (6:24).Intuition of Naive Bayesain Classification (15:43).Building a model with subset of classes, missing values and instances weights (6:58).Dealing with scalling issue and copying a learned model (3:32).Learning KNN model with features subset and with non-numeric data (10:48).Understanding the Table Data Type (11:36).Data Types that We May Encounter (6:02).Why use MATLAB for Machine Learning (3:13).Applications of Machine Learning (1:35).
Understand how to perform a meaningful analysis of your data & share it w/ others.Learn how to confidently implement machine learning algorithms using MATLAB.Familiarize yourself w/ key classification algorithms, like K-Nearest Neighbor & Decision Trees.Explore the MATLAB basics & the Statistic and Machine Learning toolbox.Access 50 lectures & 6.5 hours of content 24/7.Upon completion of this course, and all courses included in the bundle, you'll also receive a certification of completion validating your new skills! This is especially useful for including in your portfolio or resume, so future employers can feel confident in your skill set. Following along step-by-step, you'll start with the MATLAB basics then move on to working with key classification algorithms, like K-Nearest Neighbor, Discriminant Analysis, and more as you come to grips with this machine learning essential. This course will show you how to implement classification algorithms using MATLAB, one of the most powerful tools inside a data scientist's toolbox. DescriptionAs the name suggests, classification algorithms are what allow computers to well.classify new observations, like how your inbox decides which incoming emails are spam or how Siri recognizes your voice.