Supervised and Unsupervised Learning

June 30, 2021, 9:01 a.m.

Supervised learning is a technique in which we teach or train the machine using data which is well labelled.
Algo: Linear Regression, Logistic Regression
Unsupervised learning is the training of machine using information that is unlabeled and allowing the algorithm to act on that information without guidance.
Algo: K-means, C-means

Supervised Learning: Supervised learning is a machine learning approach that's defined by its use of labeled datasets. These datasets are designed to train or supervise algorithms into classify data or predicting outcomes accurately.

Using labeled inputs & outputs, the model can measure its accuracy and learn over time.

    Classification: problems use an algorithm to accurately assign test data into specific categories, such as seaparating apples from oranges. Eg. Linear classifier, Support vector machines, Decision Trees & Random forest.

   Regresssion: method that uses an algorithm to understand the relationship between dependent and independent variables. 

Regression models are helpful for predicting numerical values based on different data points. Eg. Linear Regression, Logistic Regression & Polynomial Regresssion.

Unsupervised Learning: Unsupervised learning uses machine learning algorithm to analyze and cluster unlableled datasets. These algorithms discover hidden patterns in data without the need for human intervention.

  Clustering: is a data mining technique for grouping unlabeled data based on their similarities or differences. K-means clustering algorithm assign similar data points in the groups, where the K value represent the size of grouping and granularity. Eg. Marketing Segmentation, Image compression

  Association: is another type of unsupervied learning method that uses different rules to find relationships between variables in a given dataset. Eg. Recommendation Engines, Customer who bought this item also bought.