Professional Certificate of Competency in Machine Learning and Artificial Intelligence

5.0
2 reviews
  • Awesome services, I must say far above many universities. Good experience!
    |
  • I got to learn a lot about steps and all calculations.
    |

Foundation degree

Online

Price on request

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Description

  • Type

    Foundation degree

  • Methodology

    Online

  • Duration

    Flexible

  • Start date

    On request

  • Online campus

    Yes

  • Delivery of study materials

    Yes

  • Support service

    Yes

  • Virtual classes

    Yes

Machine learning is a field of computer science that programs computers with the ability to learn from data and make informed, adaptive dynamic predictions and decisions using algorithms. It is related to computational statistics, mathematical optimization and Artificial Intelligence (A.I.).The last decade has witnessed exciting developments in machine learning that led to impressive consumer applications such as virtual assistants and speech recognition. This remarkable development results from increasingly powerful computers and the proliferation of smart objects' data.Machine learning will probably revolutionize every industry. Some applications already exist, but many are to come especially with the advent of Internet of Things (IoT) and Industrial Internet of Things (IIoT). Autonomous vehicles, predictive maintenance, fault diagnosis, smart alarm processing, and advanced process control are few domains where machine learning could be applied in the industry.Over 12 weeks, this course will aim to introduce you to basic techniques used in machine learning. You will learn how techniques such as neural networks or decision trees can solve real world problems. You will learn how to use MATLAB and WEKA software tools to apply machine learning in practice.Course OutlineMODULES 1 & 2: INTRODUCTION TO MACHINE LEARNINGDefinitions
Introduction to algorithms
Basic statistics and probability concepts
Introduction to MATLAB and WEKAMODULES 3 & 4: PROBLEM SOLVING BY SEARCH PART 1Problem-solving agents
Problem types
Example problems
Basic search algorithmsMODULES 5 & 6: PROBLEM SOLVING BY SEARCH PART 2Best-first search
A* search
Hill-climbing search
Genetic algorithmsMODULES 7 & 8: SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM)Robot localization techniques and principles
Mathematical optimization
Overview of Kalman filters and particle filters
SLAMSLAMMODULES 9 & 10: MACHINE LEARNING: DECISION TREES AND NAÏVE BAYESProblems solved by machine learning
Decision trees

Facilities

Location

Start date

Online

Start date

On requestEnrolment now open

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Reviews

5.0
  • Awesome services, I must say far above many universities. Good experience!
    |
  • I got to learn a lot about steps and all calculations.
    |
100%
5.0
excellent

Course rating

Recommended

Centre rating

S.Madenga

5.0
16/01/2021
About the course: Awesome services, I must say far above many universities. Good experience!
Would you recommend this course?: Yes

E.Meiring

5.0
13/01/2021
About the course: I got to learn a lot about steps and all calculations.
Would you recommend this course?: Yes
*All reviews collected by Emagister & iAgora have been verified

This centre's achievements

2021

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 6 years

Subjects

  • Problem Solving
  • Artificial Intelligence
  • Networks
  • Internet
  • Algorithms
  • Statistics
  • Climbing
  • Industry
  • Process Control
  • Computational

Course programme

Overview

Machine learning is a field of computer science that programs computers with the ability to learn from data and make informed, adaptive dynamic predictions and decisions using algorithms. It is related to computational statistics, mathematical optimization and Artificial Intelligence (A.I.).

The last decade has witnessed exciting developments in machine learning that led to impressive consumer applications such as virtual assistants and speech recognition. This remarkable development results from increasingly powerful computers and the proliferation of smart objects' data.

Machine learning will probably revolutionize every industry. Some applications already exist, but many are to come especially with the advent of Internet of Things (IoT) and Industrial Internet of Things (IIoT). Autonomous vehicles, predictive maintenance, fault diagnosis, smart alarm processing, and advanced process control are few domains where machine learning could be applied in the industry.

Over 12 weeks, this course will aim to introduce you to basic techniques used in machine learning. You will learn how techniques such as neural networks or decision trees can solve real world problems. You will learn how to use MATLAB and WEKA software tools to apply machine learning in practice.

Course Outline

MODULES 1 & 2: INTRODUCTION TO MACHINE LEARNING
  • Definitions
  • Introduction to algorithms
  • Basic statistics and probability concepts
  • Introduction to MATLAB and WEKA
  • Definitions
  • Introduction to algorithms
  • Basic statistics and probability concepts
  • Introduction to MATLAB and WEKA
MODULES 3 & 4: PROBLEM SOLVING BY SEARCH PART 1
  • Problem-solving agents
  • Problem types
  • Example problems
  • Basic search algorithms
  • Problem-solving agents
  • Problem types
  • Example problems
  • Basic search algorithms
MODULES 5 & 6: PROBLEM SOLVING BY SEARCH PART 2
  • Best-first search
  • A* search
  • Hill-climbing search
  • Genetic algorithms
  • Best-first search
  • A* search
  • Hill-climbing search
  • Genetic algorithms
MODULES 7 & 8: SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM)
  • Robot localization techniques and principles
  • Mathematical optimization
  • Overview of Kalman filters and particle filters
  • SLAM
  • Robot localization techniques and principles
  • Mathematical optimization
  • Overview of Kalman filters and particle filters
  • SLAM
MODULES 9 & 10: MACHINE LEARNING: DECISION TREES AND NAÏVE BAYES
  • Problems solved by machine learning
  • Decision trees
  • Naïve Bayes
  • Problems solved by machine learning
  • Decision trees
  • Naïve Bayes
MODULES 11 & 12: MACHINE LEARNING: NEURAL NETWORKS AND RULES
  • K-NN
  • Neural networks
  • Association rules
  • K-NN
  • Neural networks
  • Association rules

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Professional Certificate of Competency in Machine Learning and Artificial Intelligence

Price on request