Machine learning and Data Science are highly relevant to each other. Consider that the ability of a machine to extract knowledge from data is what is meant when the term "machine learning" is used. Without data, machine learning is extremely constrained. If anything, data science will grow more relevant as machine learning spreads across numerous industries. The potency of machine learning relies on the available data and the algorithms' potential to exploit it. All data scientists will eventually be expected to have a basic understanding of machine learning.

How is machine learning used in data science?

Data science is the practice of drawing conclusions from unprocessed data. This can be achieved by studying the intricate patterns and trends in the data at a very detailed level. This is Why machine learning is important in data science. 

When precise predictions regarding a set of data are needed, such as when determining whether a patient has cancer-based on the results of their bloodwork, machine learning algorithms are utilized in data science. We can achieve this by providing the algorithm with a sizable sample set, which includes the lab findings for each patient as well as patients who either had cancer or didn't. In order to effectively identify whether a patient has cancer-based on their test results, the algorithm will continue to learn from these experiences.

The use and importance of Machine Learning in data science are largely seen particularly in the following five stages of data science:

• Data Collection
• Cleaning and Preparing Data
• Model Training
• Model Evaluation and Retaining
• Prediction

Why machine learning is important in data science?

To note why machine learning is important in data science, let us discuss a few of the major Machine Learning applications in data science.

Real-Time Navigation: Google maps is the finest example of the most popular application of real-time navigation. Every person who makes use of this service helps to improve the accuracy of this program. When you launch the application, it continuously transmits data to Google, providing details on the route taken and the flow of traffic at any given time of day. As a result of the information provided by the large number of users that regularly use the program, Google now has access to a vast collection of traffic data that allows Google Maps not only to monitor the traffic at a given time but also to provide predictions for the route. 

Image Recognition: One of the major applications of Machine learning in data science is Image recognition. Image recognition is employed for identifying people, places, and objects. Facebook's automatic suggestions of  tagging friends and face recognition is a fine example of the application of this software

Product Recommendation: Online retailers and entertainment providers like Netflix, Amazon, Hotstar, etc. heavily rely on product recommendations. To make suggestions for products and services you might be interested in, they use a variety of Machine Learning algorithms on the data they have about you.

The above brief discussion specifies how is machine learning used in data science and how these two applications work more efficiently when combined.