Course: Distance-based Models for Machine Learning

$59.00
$71.39 incl. vat

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duration: 4 hours |

Language: English (US) |

access duration: 90 days |

Details

Machine learning (ML) is widely used across all industries, meaning engineers need to be confident in using it. Pre-built libraries are available to start using ML with little knowledge. However, to get the most out of ML, it's worth taking the time to learn the math behind it. In this course, you’ll learn all about distance-based models. You’ll be introduced to different distance measures, such as Euclidean, Manhattan, and Cosine. The distance-based algorithms K Nearest Neighbors and K-means clustering are arguably the most popular due to their simplicity and efficacy. With these algorithms you can perform regressions, clustering and classifications.

Result

After completing this course, you'll have a solid foundational knowledge of the mechanisms behind distance-based machine learning algorithms. Moreover, you'll be able to perform classification, regression, and clustering using the KNN and K-means algorithms.

Prerequisites

No formal prerequisites. However, some prior knowledge about ML is recommended.

Target audience

Software Developer, Database Administrators

Content

Distance-based Models for Machine Learning

4 hours

Distance-based Models: Overview of Distance-based Metrics & Algorithms

  • Machine learning (ML) is widely used across all industries,

  • meaning engineers need to be confident in using it. Pre-built
  • libraries are available to start using ML with little knowledge.
  • However, to get the most out of ML, it's worth taking the time to
  • learn the math behind it. Use this course to learn how distances
  • are measured in ML. Investigate the types of ML problems
  • distance-based models can solve. Examine different distance
  • measures, such as Euclidean, Manhattan, and Cosine. Learn how the
  • distance-based ML algorithms K Nearest Neighbors (KNN) and K-means
  • work. Lastly, use Python libraries and various metrics to compute
  • the distance between a pair of points. Upon completion, you'll have
  • a solid foundational knowledge of the mechanisms behind
  • distance-based machine learning algorithms.

Distance-based Models: Implementing Distance-based Algorithms

  • Knowing the math behind machine learning (ML) opens up many

  • exciting avenues. There are vast amounts of ML algorithms you could
  • learn. However, the distance-based algorithms K Nearest Neighbors
  • and K-means clustering are arguably the most popular due to their
  • simplicity and efficacy. In this course, practice building a
  • classification model using the K Nearest Neighbors algorithm. Build
  • upon this algorithm to perform regression. Then, perform a
  • clustering operation by implementing the K-means algorithm. And in
  • doing so, explore the techniques involved in converging the
  • centroids towards their optimal positions. Upon completion, you'll
  • be able to perform classification, regression, and clustering using
  • the KNN and K-means algorithms.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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