Applications of Machine Learning in Data Science

The rapidly increasing field of data science incorporates machine learning as a vital element. In order to provide classifications or predictions and uncover crucial insights in data mining projects, programs are trained using statistical approaches. Ideally, the choices taken as a result of these insights affect important growth metrics in applications and businesses. Data scientists will increasingly be in demand as big data keeps up thriving and flourishing. They will be expected to assist in determining the most pertinent business questions and the information needed to address them.

A real-life example of Machine Learning in Data Science

Your Smartphone’s face-unlock feature uses face recognition technology, which is a real-life application of Machine Learning.

Humans can recognize any image quite effortlessly. For instance, when you imagine a car, you draw a car’s image in your mind. You can imagine the car’s body (SUV, Sedan, sports, etc.), its brand, and even its color. Nevertheless, a computer only sees digital images as a set of numerical values. Therefore, it employs image processing algorithms to search for patterns in them (videos, graphics, or still images). Computers use algorithms to understand visual patterns. Machine learning algorithms can detect any type of visuals.

When you unlock your phone just by looking at it, you implement machine learning. Your phone’s high-end camera can unlock the device because it identifies you by recognizing 80 nodal points on your face. This process involves machine learning algorithms.

Some additional applications for image recognition systems include:

1. Drones
2. Manufacturing
3. Autonomous vehicles
4. Military observation
5. Forest Activities

What is Data Science?

The study of data and how to extract meaning from the data is known as data science. This field employs a variety of techniques, algorithms, systems, and tools to obtain information from both structured and unstructured data. This knowledge is further utilized by business organizations, governments, and other corporations to increase revenue, develop new products and services, improve infrastructure and public institutions, etc.

What is Machine Learning?

Machine learning, which is a subfield of Artificial Intelligence, implements algorithms to harvest data and then forecast future trends. Software engineers can run statistical analyses on data using software that has been programmed with models. This helps them to identify future data trends.

How machine learning is used in Data Science?

Machine learning is used in data science but also emerges in domains outside of it as a set of concepts and methods. Data scientists frequently use machine learning in their work to accelerate the collection of information or to improve trend analysis.

Let me illustrate these using analogies involving beer and hamburgers.

Stage 1:

Descriptive analytics:

Past information is always helpful to gain an understanding of the data. Customer segmentation is an example. It goes without saying that many organizations are curious about who their consumers are so they can focus on high-yielding markets. For a human analyst, this becomes practically impossible because you’re dealing with millions of customers. One lucrative option is to employ machine learning algorithms like k-means clustering. Usually, you can gain data insights like “Who were my customers?” and “What did they buy?”

Stage 2:

Diagnostic Analytics:

After learning what happened, most people want to know why it happened. It can be challenging to determine if the events are causally related. But a simple decision tree can be a solution. For example, “clients in group A buy plenty of beer after buying hamburgers.”

Stage 3:

Predictive Analytics:

Predictive analytics is typically linked to machine learning. Using our example as a guide, you could forecast what would happen to the extremely profitable beer sale if the unprofitable hamburgers were removed from the portfolio. For that, regression methods may be utilized. Predictive analytics can also be utilized to make claims about the past and present, which is a frequently ignored truth.

For example, you might want to predict which sector a consumer will fall into before they reach your store. Here, a classification algorithm would be necessary. If you don’t have all the essential data about one of your past clients, you may guess what they could have purchased in the past and fill in the blanks. “We are aware that this customer purchased a missing item and then spent further amounts on hamburgers. According to our prediction system, this was beer.”

Level 4:

Prescriptive Analytics:

In actuality, this is the rarest but most beneficial type of analytics. Here actions are either directly or indirectly suggested by the facts. A possible illustration is anomaly detection. Notify the store clerk (decision support) if a new customer entering one of your outlets could be a VIP. If it’s a suspected thief, automatically close the doors (decision automation). The last illustration may not be very accurate, but you get the point. Artificial intelligence and machine learning both have applications in prescriptive analytics. Algorithms can provide answers to queries like “which products should the company provide at discounted prices during Christmas?” or they can regulate prices on their own.

How does Machine Learning Revolutionize the Field of Data Analysis?

Trial and error methods have long been used in data analysis. However, these methods become impractical when dealing with large and heterogeneous data sets. This was why Big data was slammed for being overhyped. The complexity of developing new, accurate predictive models strongly correlates with the amount of available data. Conventional statistical approaches place a greater emphasis on static analysis. They are only capable of analyzing samples that have been frozen in time. Consequently, this could lead to glitches and incorrect predictions.

Machine learning recommends clever ways to process enormous volumes of data. Therefore, it emerges as an answer to all this confusion. It is a significant improvement in computer science, statistics, and other recently developed applications. Machine learning may produce reliable insights and analysis by creating effective and fast algorithms and data-driven models for real-time data processing.

Conclusion

Data scientists must master machine learning because their work is data-driven. For this reason, EducationNest has released a ground-breaking AI, ML and Data Science certification program that offers expert-level training on the subject.

EducationNest, which is a subsidiary of Sambodhi Research and Communications, has the modules specifically designed by industry experts for industry aspirants. You will gain practical experience using several in-demand machine learning techniques using both supervised and unsupervised learning. Our special case study methodology enables you to work with data and learn from it.