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2. Popular ML algorithms
  • There's a lot, and each one has its own set of appropriate use cases. You can classify ML algorithms based on learning style or similarity. The diagram below (open in new tab) does a great job of summarising the popular ones by similarity. For simplicity, we can group them based on learning style: supervised and unsupervised learning.
  • Essentials of Machine Learning Algorithms (with Python and R Codes)
    this article displays the list of machine learning algorithms such as linear, logistic regression, kmeans, decision trees along with Python R code
    Summary by Udara Jay
    What is a machine learning algorithm?
    Essentially machine learning employs algorithms that can learn from and make predictions on data. These are typically borrowed from statistics and range from simple regression algorithms to decision trees and more.
  • Supervised learning
    This is where the machine learning algorithm is trained using example scenarios. The training data comes tagged with known labels that allow the algorithms to build a model based on it. Once the model is trained sufficiently the algorithm will be able to determine the labels for unseen instances.

    Problems solved with supervised learning can be further broken down into classification and regression problems.
  • Unsupervised learning
    In contrast to supervised learning, unsupervised learning uses training data that is not labeled. This essentially means the algorithm figures out how to make sense (recognize patterns) of the data on its own.

    Unsupervised learning can be grouped into clustering and association problems.
Linear Regression for Supervised Learning