Machine learning approaches

 


MACHINE LEARNING APPROACHES:

Machine learning:

Machine learning is the study of computer algorithms that uses the data and provide the analyzed results. Machine learning algorithms would build a data model which uses the dataset called trained data.

 These datasets have been trained based on the algorithms that we need. Machine learning is used in many fields such as medicine, aerospace technology, government-related works, email filtering, speech recognition, etc., 


Machine learning approaches are categorized into various types like

  1. Supervised learning

  2. Unsupervised learning

  3. Semi-supervised learning

  4. Reinforcement learning


In this blog, we will see about supervised learning and unsupervised learning.


Supervised learning:


Supervised learning can be defined easily by its use of labeled data. This way of approach is used for building a mathematical model which takes input and gives predicted output. This approach is usually used to build the model for predictions and classifications. The model can measure the accuracy and learn over time.


Regression: These types of algorithms are used for understanding the relationship between dependent and independent variables. It is extremely useful for predicting numerical values such as weather forecasting, sales revenue projection, covid cases prediction, etc. The various popular regression algorithms are linear regression, logistic regression, polynomial regression.


Classification: These types of algorithms are used for accurately assigning the test data into particular categories. In the real world, spam emails are being separated and stored in the spam folder. Linear classifiers, decision trees, support vector machines are popular classification algorithms. 


Unsupervised learning:


Unsupervised learning takes the input and finds out the structure of data points like grouping or clustering. Unsupervised learning uses unlabeled datasets. Thus it finds out the result without the intervention of humans. 


Unsupervised learning models are used for two main reasons.


Clustering: It is a data mining technique for grouping unlabeled datasets based on their commonalities and differences. A retail company uses clustering to identify a group of households. K-means, Gaussian mixture models are popular clustering algorithms


Association: It uses various principles to find the relationship between one item to another in the dataset. A typical example is a market-based analysis. Apriori, FP-growth are popular association algorithms.


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