Use-Cases Of Machine Learning.

In this article we are gonna to learn how real industry solving their use-cases using “Machine Learning”.

What is Machine Learning ?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Machine Learning Adoption in Different Industries-

According to the latest KDnuggets Poll asking about the application of analytics, data science, and machine learning, the surveyed indicated the following top 10 industries:

1. CRM/Consumer analytics
2. Finance
3. Banking
4. Healthcare
5. Fraud detection
6. Science
7. Retail
8. Advertising
9. E-commerce
10. Education

Now Let’s talk about the use-cases of Machine Learning

1. Image & Video Recognition

Over the past few years, image and video recognition have experienced rapid progress due to advances in deep learning (DL), which is a subset of machine learning.

Image and video recognition are used for face recognition, object detection, text detection (printed and handwritten), logo and landmark detection, visual search, reverse image search, image composition, and image curation.

Machines are good at processing images. For instance, participants of ImageNet, one of many computer vision competitions, can now train DL models that recognize and classify images in the dataset far better than humans.

Video recognition is similar to image recognition in a sense that videos get broken down frame by frame and classified as individual digital images.

Companies using image & video recognition: Google, Shutterstock, Pinterest, eBay, Salesforce, Yelp, Apple, Amazon, Facebook.

2. Speech Recognition

Speech recognition is another area of machine learning that allows machines to “mimic” humans due to AI, ML, and deep learning techniques. In this case, however, not image pixels, or frame-by-frame videos, but audio files get analyzed and processed by neural networks to translate audio into a text file.

Speech recognition is used in search engines (e.g. Google, Baidu), virtual digital assistants (i.g. Alexa, Cortana, Siri, Google Assistant, AliGenie), smart speakers (e.g. Amazon Echo, Google Home), and voice-activated applications (e.g. Uber, Evernote).

3. Fraud Detection

In machine learning, fraud detection belongs to a separate class of classification problems, along with spam detection, recommendation systems, and loan default prediction.

To proactively detect fraud, ML models need to analyze transaction details in real time and classify a given transaction as legit or fraudulent, which, given enough data is provided, isn’t that complex to do.

Machine learning helps businesses save millions of dollars by detecting, flagging, and preventing fraudulent transactions.

4. Patient Diagnosis

Machine learning is extensively used in healthcare, offering doctors and health professionals tools to efficiently collect and analyze patients’ data for better diagnosis.

Efficient patient diagnosis is enabled by data, which comes in many shapes and sizes: MRIs, CAT scans, physician notes, pathology reports, bedside monitors, and more.

As of 2019, machine learning algorithms are capable of identifying cancerous tumors and skin cancer, diagnose diabetes, and most importantly, predict disease progression. No wonder that healthcare artificial intelligence market is expected to grow up to $34bn by 2025.

5. Anomaly Detection

Anomaly detection is widely used in manufacturing to increase productivity and efficiency, reduce costs, and optimize downtime.

Here’s how anomaly detection works:

• Sensors are installed onto a piece of equipment to collect data
• ML models process the data to find anomalous data
• Anomalous data is analyzed to identify a specific problem pertaining to it
• The problem is preemptively resolved to avoid equipment failure

Actually, this algorithm can be applied to a wide range of problems. For instance, credit card fraud, clinical diagnosis, structural defects are anomalies, which can be detected using machine learning.

Anomaly detection allows businesses to predict equipment failure to conduct maintenance and repairs, which cuts operational costs and saves lives. For instance, IoT sensors installed on aircraft aggregate and analyze data to report on components that need maintenance, which reduces the amount of plane accidents.

6. Customer service

Many organizations are investing in solutions that help to free up their human resources and automate as many customer service tasks as possible. Virtual assistants and chatbots are becoming a new standard in the world of customer service.

The volume of customer interactions organizations deal with is usually massive and generates a lot of data. Software data scientists can use it as training material for fine-tuning algorithms and building better learning models. By leaving routine customer service tasks to machines, enterprises can free up human employees to focus on more complex problems, be creative, and drive innovation, improving the speed of decision-making. According to Juniper Research, conversational assistants will generate $8 billion in cost savings annually by 2022.

7. Inventory Optimization

Inventory optimization is one of the most unnoticed, yet crucially important use cases of machine learning. It enables the machines to control how much stock to keep and how to keep it in the warehouse in the most efficient manner, to ensure that the supply chain won’t run dry.

In other words, ML-powered inventory optimization ensures that your business preemptively stores enough products to sell (see Demand Forecasting below), that these products are efficiently stored and distributed, and that your customers get their purchases on time.

Amazon is the world’s leader in optimizing their inventory using machine learning. The company manages to ship an average of 1.6 million packages per day, with an impeccable order fulfillment accuracy.

( Learn More about Amazon Inventory Management — https://youtu.be/zERrqLFotSY )

8. Recommendation Systems

Recommendation or recommender systems are one of the most ubiquitous applications of machine learning in daily life. These systems are used in search engines, e-commerce websites (e.g. Amazon, eBay), entertainment platforms (e.g. Netflix, Google Play), games, and multiple Web & mobile apps.

Recommender systems are usually classified by the filtering method:

• Content-based filtering method. This method recommends items to a user, based on items this particular user has engaged with. For example, if you’ve purchased a book about machine learning at Amazon, it’ll display more ML-focused books in the suggestions section.
• Collaborative filtering method. In the collaborative filtering method, the recommendation system analyzes the actions and activities of a pool of users to compute a similarity index and to further display similar items to similar users.

However, there’re more advanced types of recommender systems as well.

9. Security

Another area where the technology is helping enterprises improve their operations is security. In particular, ML helps in analyzing threats and responding to security incidents. According to ABI research, machine learning and data security will increase spending $96 billion by 2021.

ML powers predictive analytics tools that enable early detection of threats. Any anomalies within the system will never go unnoticed thanks to solutions that learn to become more effective with every exposure to data. Organizations that invest in such tools are able to navigate regulatory requirements and become innovation-driving leaders in their sectors.

“Siemens Handles 60,000 Cyber Threats per Second Using AWS Machine Learning”

10. Intrusion Detection

ML-powered intrusion detection is the lifeblood of adaptive intrusion detection systems (IDS), which monitor networks in real time to identify and cope with malicious traffic or intrusion techniques, like brute force, infiltration, and unauthorized access.

Machine learning has helped revolutionize intrusion detection. Traditionally, an IDS was designed to identify known threats. Bad actors could design a new intrusion method to bypass the system.

Now, however, network data is continuously collected and pre-processed to create high-quality datasets, which are used to train machine learning models that efficiently tell normal traffic from malicious traffic in real time.

“AI and ML aren’t just buzzwords anymore, but, probably, the most important technology to invest in the 21st century.”

“Machine Learning saves costs and eliminates a lot of work that humans find unpleasant, while allowing us to focus on the more strategic and people-centric aspects of our job. “

Jack McCullough , President the CFO Leadership Council

Thanks for Reading.