When training is done on a Laballed data-set, with known input and output, those algorithms learns to map between input and correct output.

Summary

  • What is Machine Learning.
  • Different Type of Machine Learnign
  • Supervised Machine Learning
    • Categories
      • the algorithms of each category

What is Machine Lerning

In simple terms, Machine Learning is a branch of Artifical Intelligence, that makes the machine think and understand like humans by learning from data.

Types of machine learning

We can generally distinguish here between 4 types.

  1. Supervised Machine Learning.
  2. Unsupervised Machine Learning.
  3. Semi-supervised Machine Learning.
  4. Reinforcement Learning.

Supervised Machine Learning:

let’s now get right into the topic of our discussion, the Supervised Machine Learning, which is basically when training is done on a Labelled data-set, with known input and output, those alogorithms learn basically how to map or connect between the input and the correct output.

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Categories:

Within the Supervised Machine Learning we can differenciate between two categories:

  • Classification
  • Regression

Classification:

is basically a way of telling whether a thing is the thing or not, for example classifying whether someone is blind or not, or is the email a spam or not.

Alogirthms:

  • Logistic Regression
  • Support vector machine
  • Random Forest
  • Decision Tree
  • K-Nearest Neighbors (KNN)
  • Naive Bayes

Regression:

Deals with predicting Continuous numerical values, like predicting the price of a house based on its size.

  • Linear regression
  • Polynomial regression
  • Ridge Regression
  • Lasso Regression
  • Decision tree
  • Random forest.

Advantages of supervised machine learning:

  • High accuracy: cause labelled datasets.
  • interpretable
  • It can often be used in pre-trained models which saves time and resources when developing new models from scratch.

Thanks!

Thanks for reading all along, i really appreciate anyone finishing this blog post, it was something exciting for me to learn, and more exciting to share, hope you learned something from this.