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Machine Classification Algorithms

Track :

Programming

Lessons no : 50

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What will you learn in this course?
  • Develop effective decision trees and random forests for accurate data classification using machine learning techniques
  • Implement support vector machines (SVM) to optimize classification accuracy in diverse datasets
  • Apply logistic regression models to predict categorical outcomes with high precision and interpretability
  • Utilize k-nearest neighbors (KNN) algorithm for efficient and scalable data classification tasks
  • Evaluate and select appropriate classification algorithms based on dataset characteristics and performance metrics
  • Tune hyperparameters to enhance the accuracy and robustness of machine learning classifiers
  • Identify and address common challenges like overfitting and bias in classification models
  • Integrate classification algorithms into real-world applications such as email filtering and spam detection
  • Analyze confusion matrices and other metrics to assess classifier performance effectively
  • Implement feature engineering techniques to improve classification accuracy and model interpretability
  • Compare supervised learning algorithms to determine the best fit for specific classification problems
  • Apply cross-validation methods to ensure reliable and generalizable classification model results

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Lessons | 50
Applications of Machine Learning
00:01:36

Applications of Machine Learning
Why use MATLAB for Machine Learning
00:03:14

Why use MATLAB for Machine Learning
Meet Your Instructor
00:01:25

Meet Your Instructor
Course Outlines
00:01:44

Course Outlines
MATLAB GUI
00:04:58

MATLAB GUI
Some common Operations
00:11:57

Some common Operations
Data Types that We May Encounter
00:06:03

Data Types that We May Encounter
Grabbing a dataset
00:02:21

Grabbing a dataset
Importing Data into MATLAB
00:09:37

Importing Data into MATLAB
Understanding the Table Data Type
00:11:37

Understanding the Table Data Type
Nearest Neighbor Intuition
00:09:20

Nearest Neighbor Intuition
Nearest Neighbor in MATLAB
00:09:40

Nearest Neighbor in MATLAB
Learning KNN model with features subset and with non numeric data
00:10:50

Learning KNN model with features subset and with non numeric data
Dealing with scalling issue and copying a learned model 4
00:03:33

Dealing with scalling issue and copying a learned model 4
Types of Properties 5
00:11:23

Types of Properties 5
Building a model with subset of classes missing values and instances weights 6
00:06:59

Building a model with subset of classes missing values and instances weights 6
Properties of KNN
00:05:09

Properties of KNN
Intuition of Naive Bayesain Classification
00:15:44

Intuition of Naive Bayesain Classification
Naive Bayes in MATLAB
00:10:55

Naive Bayes in MATLAB
Building a model with categorical data
00:06:25

Building a model with categorical data
A Final note on Naive Bayesain Model
00:03:01

A Final note on Naive Bayesain Model
Intuition of Decision Trees
00:09:02

Intuition of Decision Trees
Decision Trees in MATLAB
00:05:37

Decision Trees in MATLAB
Properties of the Decision Trees
00:14:25

Properties of the Decision Trees
Node Related Properties of Decision Trees
00:09:21

Node Related Properties of Decision Trees
Properties at the Classifer Built Time
00:07:27

Properties at the Classifer Built Time
Intuition of Discriminant Analysis
00:06:45

Intuition of Discriminant Analysis
Discriminant Analysis in MATLAB
00:04:42

Discriminant Analysis in MATLAB
Properties of the Discriminant Analysis Learned Model in MATLAB
00:07:04

Properties of the Discriminant Analysis Learned Model in MATLAB
Intuition of SVM Classification
00:07:43

Intuition of SVM Classification
SVM in MATLAB
00:12:36

SVM in MATLAB
Properties of SVM learned model in MATLAB
00:12:48

Properties of SVM learned model in MATLAB
Intuition of ECOC
00:05:31

Intuition of ECOC
ECOC in Matlab
00:09:17

ECOC in Matlab
ECOC name value arguemnts
00:13:00

ECOC name value arguemnts
Properties of ECOC model
00:04:52

Properties of ECOC model
Ensembles in MATLAB
00:12:34

Ensembles in MATLAB
Properties of Ensembles
00:05:29

Properties of Ensembles
Cross validition options Part 1
00:10:08

Cross validition options Part 1
Cross validition options Part 2
00:10:09

Cross validition options Part 2
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Classification algorithms in machine learning use input training data to predict the likelihood that subsequent data will fall into one of the predetermined categories. One of the most common uses of classification is filtering emails into “spam” or “non-spam .