Machine Learning

How Machine Learning Changes the Business World

Zoë Niederhübner
How Machine Learning Changes the Business World

We encounter machine learning in our day-to-day life probably more often than we realize. Asking a personal virtual assistant to set an alarm, booking an Uber ride, or automatically filtering spam mail, all of those applications are powered and improved by machine learning. Though that is by far not everything, machine learning is utilized in many industries and businesses. From search engines to customer support and fraud detection, those services rely heavily on machine learning in order to keep up to date and to provide the best experience for the user or customer.

What Is Machine Learning?

One of the big advantages of machine learning is that the computer can essentially learn from data without a programmer having to explicitly code anything. As a part of artificial intelligence, machine learning combines data and statistical tools to predict certain outputs, which then can be used to make improvements and changes to various real-life applications. In layman's terms, machine learning is based on feeding data into an algorithm. This algorithm is then able to process said data and learn from it, thus becoming better at predicting answers or solutions. The output is then a model representing what was learned and what is required to make accurate predictions.

What Can Machine Learning Do?

1. Image Recognition

Image recognition is a common example of machine learning. Based on the intensity of every single pixel in black and white or in color images an object can be identified as a digital image. Furthermore, machine learning can also be used for facial recognition in photos by matching the faces to a database of people.


- Identifying cancerous growth in an x-ray

- Facial recognition in law enforcement and social media

2. Speech Recognition

Speech recognition is the process of translating speech into text. A software application recognizes and then converts the spoken words into a text file. Devices like Amazon’s Alexa or Siri by Apple use speech recognition.


- Voice dialing

- Controlling appliances

- Word dictate function

3. Medical diagnosis

Even for diagnosing diseases, machine learning can be useful. Here the collaboration between human doctors and machines plays a critical role. While machine learning can help with the diagnosis or treatment plans, it is the doctor who is responsible for making the right decision.


- Forming a diagnosis or recommending therapy options

- Recognizing cancerous tissue

- Predicting the disease progression

4. Statistical Arbitrage

Arbitrage is an automated short-term trading strategy that is used to manage a large number of securities. This strategy employs a trading algorithm to analyze a set of securities, focusing on economic variables and often historical correlations. The arbitrage strategy can then be improved by machine learning in order to produce the most viable results.


- Analyzing data sets

- Identifying arbitrage opportunities

5. Learning Associations

Learning associations means developing some insight into how people and potential customers associate one product with another. Through machine learning associations between products bought by customers can be easily studied.


- Personalized online product recommendations

- Product marketing in general

6. Prediction

Prediction is another common application of machine learning. By classifying available data into groups, that are defined by a set of rules determined by analysts, the probability of a fault can then be calculated.


- Predicting fraudulent transactions

- Product development

- Real estate pricing

7. Extraction

Extracting information from unstructured data is another useful application of machine learning. The algorithm uses a huge amount of data from customers as input and outputs structured information.


- Developing methods to prevent or treat disorders

- Helping to diagnose illnesses quickly

How is Machine Learning Utilized by Other Businesses?

By now, many businesses and industries have realized the potential of machine learning. Through machine learning, massive amounts of data can be used to obtain meaningful insights to effectively provide solutions for complex business problems. The machine learning algorithms do not have to be specifically programmed to assimilate the inputted data but rather learn from the input provided.

Improving scalability and enhancing operations of a business can be achieved through the usage of machine learning and other AI tools, thus those technologies have gained popularity in many different industries. To benefit from machine learning yourself it is crucial to understand how other businesses have implemented this new technology in their own process.

Social Media giants like Twitter, Facebook, and Pinterest but also companies like Goggle, IBM, and Salesforce are utilizing machine learning in interesting new ways.

The healthcare sector now employs machine learning algorithms to help diagnose patients faster. In the field of transportation, machine learning is utilized to make transport more efficient and, therefore, cheaper. Even the government uses this technology to be able to quickly adapt to new situations or to predict potential future scenarios. Discovering fraud and identifying investment opportunities, machine learning also has its place in financial services. Another sector that employs machine learning extensively and very successfully is the marketing industry. Here this tool is mainly used to increase customer satisfaction by improving customer support and shopping experience.

How Can Your Business Benefit from Machine Learning?

Machine learning allows your company to gain meaningful insight by evaluating a huge amount of raw data. Thus, it can help solve a variety of business problems, allows you to improve customer experience, and enables more effective marketing.

In the following there are listed some important benefits machine learning can bring to your company:

Personalized Shopping Experience

By using machine learning, businesses can gain invaluable knowledge about their customers. Through past searches and the buying history of a customer, businesses can predict customer behavior as well as purchasing patterns and make fitting recommendations for each individual customer therefore tailoring the shopping experience perfectly to the customer.

Increased Customer Satisfaction

Managing customer relationships becomes much simpler thanks to machine learning as well. By analyzing previous call records, the customer can immediately be assigned to the correct customer service employee. Additionally, features like chatbots help decrease the workload for human employees too.

Flawless Data Entry

False or duplicate data can disrupt the work process though machine learning can solve this problem by helping to avoid errors like that, which are often caused by manual data entry. That allows employees to focus on more important tasks instead of fixing issues caused by faulty data.

Improved Cyber Security

Cyber Security has by now become a concern for almost every business in the world, thus protecting your business from phishing attacks and other security threats is essential. Machine learning can be used to build technologies that can quickly and successfully detect unknown and new threats.

Financial Analysis

Due to the large amount of quantitative and accurate historical data, machine learning can be used in financial analysis. That includes, amongst other things, fraud detection and algorithmic trading.

The Future of Machine Learning

Machine learning is an amazing tool to turn large numbers of data into information that allows businesses to gain insight into their company and help them understand customers better. The main benefit is that the computer can learn from inputted data and consequently adapt to do tasks automatically that would be too difficult for humans.

This technology is already used to improve many aspects of a business; however, the usefulness and importance of machine learning will certainly increase in the future.

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