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Role of machine learning in data science

Data science is mainly the study of data cleansing, preparation, and analysis, whereas machine learning is a branch of AI and data science. Data science and machine learning are two important modern technologies. Data science and machine learning are closely related to each other, and they also have different functionalities and goals. Data science is a field that studies the approaches to find insights from raw data, whereas machine learning is a method by which group of a data scientists can enable the machines to learn automatically from previous data. Data science with machine learning allows computers to learn from previous experience and improve their performance and predict the output. 

Where is machine learning used in data science?

The use of machine learning in data science is understood with the development procedure or life cycle of data science. Let us take a look at the different steps which occur in the data science lifecycle:

  • Business requirement: here, we try to understand the need for business problems for which we want to use the same. Even if we want to create a recommendation system and the business requirement is to increase the sales. 
  • Data acquisition: in this, data is acquired for solving a specific problem. For the recommendation system, you can achieve the ratings which are provided by the user for different products and comments along with history too. 
  • Data processing: in this, the raw data is acquired from the above step, which is transformed into a proper format, and this can be used in the upcoming steps. 
  • Data exploration: in this, one can understand different patterns of data and even try to find useful insights from the data. 
  • Modeling: This is a step where machine learning algorithms are used. These steps involve a machine learning procedure that imports data, data cleaning, creating a model, training the model, testing the model, and even improving the model efficiency. 
  • Deployment and optimization: this is considered the last step where the model is deployed on a project, and the performance of the model is checked. 

Data science covers a wide spectrum of domains, and machine learning is one among them. Data science comprises different fields and techniques such as statistics and artificial intelligence for data analysis for drawing meaningful insights. 

Machine learning has dominated the industry overshadowing every aspect of data science. Machine learning analyzes large chunks of data. Machine learning automates the procedure of data for real-time with no human intervention. The flow of machine learning begins with feeding the data to be analyzed, and a data model is built. The machine learning algorithm is made for prediction the next time uploading the dataset. 

A few key machine learning algorithms in data science are regression, classification, and clustering. Professionals who are interested in knowing data science must avail the best data science course. Many companies mainly focus on using data to improve their products. Machine learning is going to be used for analyzing the humongous amount of data. Here, you can find the workflow of machine learning in data science. This also helps in transforming and enriching the data and makes their analysis-ready. 

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