Updated on: June 23, 2022
These days, the inclusion of data in research has become the new normal. Almost every business or organization is under stress over the use of data in order to improvise their business processes, including their products and services. If you look at the prospect of data science, you will come across a fact wherein machine learning is just a part of data science.
What is Machine Learning?
Did you know that you were familiar with machine learning long before the term became popular? Have you ever observed the pattern followed by video apps such as YouTube? How does this app recommend your next videos? It actually observes watching patterns and follows the sync to suggest your next move. This is how machine learning actually works.
Machine learning is a type of artificial intelligence that follows certain patterns or habits and uses them to predict future outcomes.
Why is Machine Learning Important?
Machine learning holds the importance of analyzing a large amount of data within an organization in order to look for the growth and demand prospects of the products and services. It actually helps in identifying the risk factors and provides the techniques to change the data engineering task.
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What is Data Science?
Data science is the field that processes, manages, analyzes, and interprets large amounts of data in order to create and generate meaningful insights. The information that is derived from the pool of data is being used to create informed business decisions. It is a field that combines various forms of skills such as scientific methods, statistics, data analysis, and many others to come to the right and well-informed conclusions and predictions.
What Role does Machine Learning Plays in Data Science?
As you have studied above, data science is all about finding valuable insights from raw data. This is done by exploring the data and understanding consumer behavior and trends. When this is processed, machine learning plays its role.
But before going into the deep analysis of the role played by machine learning, let’s get some quick differences between data science in machine learning.
Data science, as you know, is the field that is involved with extracting information from structured and unstructured data, whereas machine learning generates the ability of the system to learn without any explicit programming. Machine learning focuses on algorithm statistics, but data science takes care of the data processing using those algorithms.
From the above, one thing is clear: Machine learning algorithms are used in data science in order to make accurate estimates about a given set of data.
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Role of Machine Learning in Data Science
Now, let’s learn about the role of machine learning in data science with the help of the following steps.
Step 1. Data Collection:
This is the very first step of the machine learning process. In this, machine learning helps in collecting and analyzing all types of data, whether it is structured, unstructured, or semi-structured from the database. The data can be in the form of a PDF, a document, images or even handwritten form.
Step 2. Data preparation and cleansing:
once data is collected, machine learning processes through preparing the data and its features, keeping in mind the business problem. Once this is done, it is followed by data cleansing that focuses on removing the inconsistencies, noise, incomplete information, and missing value types of data.
Step.3. Model training:
This step involves the choice of the data and the choice of the machine learning algorithm. Basically, an algorithm is selected on the basis of the end-user’s needs. Also, algorithm complexity, performance, resource requirements, interpretability, and speed are considered for better model accuracy. Once the model is finalized, the training and testing part is done.
After completing this process, a working model comes into play that is further being validated, tested, and deployed.
Step 4. Model Prediction:
After taking and following all the above three steps seriously, the crucial stage of model prediction arrives. At this stage, it’s quite important to understand and avoid maximum errors, which later help in building accurate models. For a successful data science project, it’s quite important to know the difference between bias and variance predictions.
Machine learning takes into account the major steps of data collection, preparation, cleaning, model training, etc. that fuel the process of data science, which includes deep and detailed analysis in order to achieve the desired results, i.e., outcome.
Thus, in a nutshell, you can say that data science and machine learning are complementary to each other. Machine learning helps in making the process of data science easy and smooth. As organizations are largely involved in taking the real-time value of the data to enhance their products and services, thus, data science becomes the need of the hour and it cannot be processed all alone without the help of machine learning.