Machine Learning

Course Description

This Course will introduce Machine Learning concepts and practical information on how Machines learn to perform certain tasks as those used in the Artificial Intelligence world.

Objectives

Recognize the topics to be covered in this tutorial.
Save and load inference models
Build and apply Decision Tree inference models for data classification
Build and apply Random Forest (RF) models for data classification
Evaluate the performance of ML classifiers using the K-Fold Cross Validation
Build and apply Neural Networks (NN) classification models
Master the concepts of linear and multivariant regression.
Apply multiple leaner regression to categorical data
Build and apply Supporting Vector Machine inference models for data classification
Build and apply Naive Bayes (NB) models for data classification
Build and apply K-means Clustering to classify/cluster unlabeled data
Evaluate the accuracy of NN classification models
Acquire thorough knowledge of the mathematical formulation of cost function and cost function minimization in Machine Learning.
Measure the accuracy of the inference model
Evaluate the accuracy of DT and SVM models
Evaluate the accuracy of DT and SVM models
Evaluate the accuracy of K-Means clustering
Be able to build and apply simple regression models.
Build and apply logistic regression inference models for data classification
Evaluate the accuracy of logistic regression and decision tree models

Course Topics

First Session: Basics of Regression Techniques - Part 1
Second Session: Basics of Regression Techniques - Part 2
Decision Trees and Support Vector Machines
Random Forest and Naïve Bayes Classifiers
K-Fold Cross Validation and K-means Clustering
Neural Networks

Course Mode

Blended (Online and Face-to-Face)

Course Level

Advanced

Course Language

English

Arabic

Course Category

Information Technology