Role of Machine Learning in Data Science?

Machine Learning and Artificial Intelligence have dominated the industry, masking other parts of Data Science including Data Analytics, ETL, and Business Intelligence.

Machine Learning analyzes large volumes of data automatically. Machine Learning effectively automates the data analysis process and gives real-time data-driven predictions without requiring human intervention. To make real-time predictions, a Data Model is constructed and trained automatically. This is where Machine Learning Algorithms are used in the Data Science Lifecycle.

The standard Machine Learning loop starts with you providing the data to be analyzed, then you define the specific features of your Model, and eventually, a Data Model is created. The Data Model is then trained with the Training dataset that was first handed to it. Once the model has been trained, the Machine Learning Algorithm is ready to make a prediction the next time you upload a new dataset.

3 Key Machine Learning Algorithms in Data Science

When you have a dataset, you can classify the problem into three types:

  • Regression
  • Classification
  • Clustering

1) Regression

When the output variable is in continuous space, regression is used. Curve-Fitting Techniques are most likely something you’ve seen in math class. Do you remember the expression “y=mx+c”? In regression, the same rules apply. Finding the equation of a curve that fits the data points is similar to a regression, and once you have the equation, you can predict the output values.

Some well-known Regression Algorithms are Linear Regression, Perceptron, and Neural Networks.

Financial forecasting, such as stock market forecasting and housing price forecasting, requires regression.

2) Classification

When the output variables are discrete, classification is used. If you’re trying to find out which group your data belongs in, it’s a Classification difficulty. Current data is analyzed by classification algorithms to help predict the Class or Category of fresh data.

It’s more like categorization to find curves that divide data points into different Classes/Categories.

When it comes to classifying an email as spam, classification is a challenge. Gmail, for example, will check every email for spam-like characteristics and begin storing it in your Spam Folder if 80 percent or more of the criteria are met.

Some well-known Classification Algorithms include Support Vector Machines, Neural Networks, Naive Bayes, Logistic Regression, and the K Nearest Neighbour.

3) Clustering

If you merely want to group data points with comparable features without labeling, it’s a Clustering task. In theory, based on numerous definitions of similarity, comparable data points should be clustered together in the same Cluster. Different Clusters should have as many dissimilar points as possible. Clustering techniques scan a dataset for patterns without assigning labels.

Two well-known clustering methods are K-Means Clustering and Agglomerative Clustering.

Using this method, customers’ purchase habits are clustered.

Regression and classification are included in the Supervised Learning Model of Machine Learning, while clustering is included in the Unsupervised Learning Model.