At the moment, machine learning covers a wide range of applications from banks, restaurants, gas stations to robots in production. New tasks that arise almost every day lead to the emergence of new areas of machine learning. To be hundreds of steps ahead, the leading companies are embarking on a digital transformation. They incorporate the latest technology into their business models. That’s where we need Machine Learning.
In this article, we will reveal the essence of Machine Learning, research its use cases and ways it helps businesses. We’ll dive into the possible challenges, and answer whether your company should adopt machine learning.
What exactly is machine learning?
Machine learning (ML) is a set of methods in artificial intelligence and a collection of algorithms used to create a machine that learns from its own experience. As training, the machine processes enormous input data and finds patterns.
Machine learning is part of AI, algorithms that allow a computer to draw conclusions based on data without following strict rules. The machine can find patterns in complex and multi-parametric problems (which the human brain cannot solve), thus finding more accurate answers.
The main feature and priority goal is not to solve one specific task directly but to learn to perform other similar functions in applying solutions. For this purpose, the specialists use mathematical and statistical analyses, optimization, and other data processing techniques.
The origins of ML date back to the 1950s when the "Artificial Intelligence" term appeared for the first time - the idea of a machine capable of solving abstract problems without human help. It took more than half a century for businesses to switch to digital format. For example, in 2018, the digital segment surpassed the television in terms of funding in the advertising market for the first time. As soon as demand arose, machine learning became an indispensable tool in any business: some companies even began to create internal departments of data science specialists.
Machine Learning uses Data Science - the science of data analysis methods and extracting valuable information and knowledge. It intersects with machine learning, cognitive science, and technologies necessary for Big Data. The result of Data Science is the analyzed data and finding the right approach for further processing, sorting, sampling, data retrieval.
There are many types of Machine Learning and their combinations but we can point out three basic types:
- Supervised Learning is used to teach the machine to recognize objects or signals.
- Unsupervised Learning uses the principle of similarity. Algorithms study similarities and detect differences and anomalies by identifying what is unusual.
- Reinforcement Learning is used when the machine has to correctly perform the tasks in the external environment with many possible options. For example, in computer games, trading operations, or uncrewed vehicles.
The difference between Artificial Intelligence, Deep Learning, and Machine Learning
Let’s consider the following definitions for understanding Deep Learning versus Machine Learning and Artificial Intelligence.
Artificial intelligence is a variety of technological and scientific solutions and methods that help make programs similar to human intelligence. Artificial intelligence covers many tools, algorithms, and systems, including all components of Data Science and Machine Learning.
Machine learning is a subset of Artificial Intelligence that uses techniques (such as Deep Learning) to allow computers to use the experience to improve problem-solving.
Deep learning is a subset of Machine Learning methods. The data is analyzed through several layers of the deep learning network to draw conclusions and make decisions about the data. Deep learning methods allow you to achieve greater accuracy on large data sets, but these features make deep learning much more resource-intensive than machine learning.
The learning process of Machine Learning is based on the following steps:
- Transfer of data to the algorithm
- The data is used to train the model
- Model testing and deployment
- Use of the expanded model for automated problem solving based on predicting
Using Machine Learning and Deep Learning techniques, you can create computer systems and applications that perform tasks usually assigned to humans. Examples of these tasks include image recognition, speech recognition, and language translation.
Let’s see the main difference in this table of comparison.
|Artificial Intelligence||Machine Learning||Deep Learning|
|Came around in 1956||Came around in 1980s||Came around in 2000s|
|Human intelligence exhibited by machines||An approach to achieve AI||A technique to implement ML|
|Not a subset of ML or DL||A subset of AI||A subset of ML|
|Requires full programming services to make the system||Doesn’t require hardcore algorithms||Doesn’t require any programming|
|Example: Amazon Echo||Example: Search Engine Result Refining||Example: Automatic Translation|
The importance of algorithms
Machine learning and deep learning are algorithmic. In machine learning, researchers use a relatively small amount of data and choose the essential features needed for the algorithm to make predictions. This method is called functional engineering. For example, if the machine learning program teaches aircraft image recognition, its programmers develop algorithms that allow the program to recognize typical shapes, colors, and sizes of commercial aircraft.
Algorithms are the set of successive operations to solve a specific problem. It is the method of solution. You can choose a sophisticated algorithm for each specific task or even use combinations for better results. The speed and accuracy of the input data processing directly depend on the chosen method.
Machine learning and, in particular, neural networks should be used to solve business problems in cases where:
- a large amount of different data has been accumulated, but there are no programs for their processing and systematization;
- available data is distorted, incomplete, or not systematized;
- the data is so different that it is difficult to identify the connections and patterns that exist between them.
There are two most used categories of ML algorithms: supervised and unsupervised ML. Supervised ML algorithms are based on “teaching” the machine to get the outputs based on its training data, which is already structured. Unsupervised ML algorithms process the unstructured data — data that hasn’t been classified.
There are many Machine Learning algorithms currently on the market, and this will only increase given the amount of research in this area. Linear and logistic regression are usually the first algorithms that data scientists study, followed by more advanced algorithms.
Below are some of the most popular Machine Learning algorithms:
- Linear Regression
- Logistic Regression
- Decision Tree
- SVM (Support Vector Machine) Algorithm
- Naive Bayes Algorithm
- KNN (K- Nearest Neighbors) Algorithm
- Random Forest Algorithm
- Dimensionality Reduction Algorithms
- Gradient Boosting Algorithm and AdaBoosting Algorithm
Machine Learning use cases
To be a data scientist, you need to have a deep understanding of some of the algorithms, as well as Deep Learning techniques.
The modern functionality of machine learning tools is truly impressive: applications are already able to recognize the language, gestures, and images, perform medical and technical diagnostics, stock and financial analysis, organize documentation and detect spam. Total digitalization leads to the accumulation of huge amounts of information in various fields, which, in turn, expands the scope of machine learning.
- User behavior analysis (training datasets, including health data, social media data, financial data)
- Improved automation (replacing lost credit or ATM cards, transferring data, automated onboarding, etc)
- Security improvements (Phishing attacks, Identity theft, Ransomware, Data breaches, Privacy concerns, Etc.)
- Financial management (High-Frequency Trading (HFT), loan/ insurance underwriting, robo-advisors, Fraud Detection, development of a chatbot)
- Computer vision (Predicting customer lifetime value, optimizing marketing campaigns)
- Monitoring (detecting spam, Image and speech recognition, voice control)
- Searching system (finding relevant content personalized for users, fixing bad queries and find relationships between data, log parsing)
- Data Mining (improving decision-making, augmented analytics, statistical analysis, cluster analysis)
- Evolutionary and genetic algorithms (artificial neural networks, computer vision, pattern recognition, document recognition)
- Augmented reality (simulation training, tactical situations)
- Natural language analysis systems (digital calls, semantic-based search, social media listing, smart product recommendation, digital assistants)
This list can be endless. The use of machine learning techniques in business process automation makes it possible to perform most of these tasks better than people. A well-trained model can take on most of the work of finding the appropriate primary documents, leaving a person only more complex cases. Therefore, the combination of machine learning technologies will achieve greater operational efficiency.
Ways Machine Learning Helps Businesses And Entrepreneurs
There are now many cases of successful use of algorithms to improve performance and achieve almost all business goals. Everyone knows about Netflix and Amazon's personalized referral system. The world's most popular online cinema and marketplace offers its users the movies and products according to the history of their previous requests. To provide the best experience, the services use machine learning algorithms.
- Automating Routine Tasks
Using machine learning to automate routine tasks saves time, manages resources more efficiently, and ultimately reduces costs and increases revenue. The list of tasks that can be automated with ML is very long. In particular, the use of machine learning allows you to automate the process of data classification reporting, as well as monitor and prevent IT threats by conducting internal audits. The machine learning algorithm can automate the error-free assessment of insurance risks by manual data entry.
- Intelligent Process Automation (IPA)
IPA is a product of ML, AI, and related technologies, including computer vision, cognitive automation, and machine learning. By combining these technologies into a single process, companies gain greater automation capabilities, revealing all business values to the enterprise.
- Managing Unstructured Data
One of the most common arguments in favor of machine learning is that this technology makes it possible to process tones of information that are unrealistic with more traditional approaches. It is especially true for small companies, which often have more data on transactions and customers than they can process.
- Improving Personalization
Popular voice assistants make it easier to communicate with people. They all use a machine learning algorithm for language recognition based on natural language processing (NLP). The algorithm converts the language into numbers using machine learning and formulates the answer accordingly. NLP is also used to translate unclear legal texts in contracts into plain language. Researchers expect that it will become phenomenally smarter with the improvement of machine learning methods in the future.
- Making Customer Engagement More Effective
Machine learning helps customer support by using chatbots that respond to consumer inquiries. Using the concepts of natural language processing (NLP) and mood analysis, machine learning algorithms can understand customer needs and the tone of their voice. The system then forwards the request to the appropriate support professional.
- Improving Marketing Efficiency
Using machine learning functions, the marketing industry segments customers based on behavioral and characteristic data. Digital advertising platforms allow marketing specialists to focus on the target audience with an appropriate product impact. They understand the requirements of customers and, accordingly, promote products better.
- Saving Time For Cybersecurity Workforce
As businesses deal with ongoing cyberattacks and complex ongoing threats. To successfully detect violations, next-generation tools need to evaluate a large amount of data at a high level and speed to identify potential violations. Thanks to machine learning, qualified network professionals can quickly unload most heavy movements, which will help them distinguish the threat from the actual activity without the need for additional analysis.
- Preventing frauds
Large financial companies and banks use machine learning to detect fraud. Machine learning can also be useful for credit card companies. The technology is trained to detect transactions that appear to be fraudulent based on specific criteria following company rules. In addition, with the help of machine learning, the company can get an idea of its competitive environment and customer loyalty and predict sales or demand in real-time.
- Predicting Where The Market Is Moving
Organizations can use machine learning models to predict customer behavior based on their past data. Companies look for what people talk about on social media and then find those looking for a product or service. For example, Zappos uses analytics and machine learning to provide customers with personalized search and results, as well as behavior prediction models.
- Medical Diagnosis
The value of machine learning in healthcare lies in its ability to process vast amounts of data beyond human capabilities and then reliably turn the analysis of that data into clinical data. Machine learning helps plan and provide care, leading to better results, lower treatment costs, and increased patient satisfaction. Computer Diagnostics (CAD), a machine learning application, can also be used to view the results of mammographic scans of women in predicting cancer.
Challenges of machine learning
ML has excellent potential. That's why software vendors are investing in developing such technologies. But there are many challenges along the way. The main task is to prepare high-quality data that can be used to create algorithms. Such data is scattered across various file storages and databases in many companies or stored in quite difficult formats to process.
Another challenge is setting priorities because, with so many opportunities, it's hard to decide where to start. To facilitate this task, the developers offer pre-configured solutions that allow you to use the latest machine learning technologies out of the box.
You also need to keep in mind your customers. Machine systems can go too deep into analyzing confidential data, and not everyone likes it. It is necessary to respect the customer's right to privacy, so sometimes, you have to receive consumers' consent to process such data.
The growing role of artificial intelligence is inevitable. It is evolving at a fast rate. Therefore, the question is not whether you should implement ML but how quickly it should be done. At the same time, you need to carefully analyze all the advantages and disadvantages of this technology concerning your own business.
Should your company adopt machine learning?
Machine learning technologies are available to all companies. It is an investment, and you shouldn’t see it not as a luxury but as a tool that brings benefits. The simplest ML tools are free, and you can use them now.
You can get more advantages if you react faster to the technological changes and the market's demand. And the success of such global players as Amazon, Walmart, Google proves the success of machine learning implementation.
If you don't start using ML, you risk being behind the other tech companies (and AI-powered competitors). As in the case of the Internet, today, ML is a mandatory and inevitable part of any business.
Of course, starting to work with machine learning for a small or medium-sized company can be quite difficult because to process data, this data must still be obtained to process data. Then you need to make their initial processing and classification (which can also be done by machine learning tools), set up data pipelines, build applications to work with data, make graphs and notifications. Even in the case of SaaS solutions, this process can take many months, if not years. Therefore, delegating some of this work is a good solution for most companies. It allows you to concentrate on business tasks and get quick results. And having defined the model, tasks, and most importantly, the cost, you can use them in future companies' projects completely risk-free.
If you feel interested and don’t know where to start, let's talk and see how we can grow your business together, implement ML solutions and attract your customers.