What is Machine Learning and how it can be useful for your business project in 2023?
Companies nowadays work with a lot of data. We have entered a new era of data science generation because machine learning and data analysis tools have become more accessible.
Machine learning (ML) is a branch of artificial intelligence. It is a data analysis technique that allows a machine/robot/analytic system to learn independently by solving an array of similar problems. ML can classify data, build forecasts, identify features and perform other tasks.
Machine learning is adaptive, so it can be used in scenarios where data, queries, or tasks are constantly changing. This makes ML a versatile tool for solving business problems and provides broad applicability.
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WHAT IS NEEDED FOR THE MACHINE LEARNING PROCESSES:
Machine learning aims to predict results from input data. The more varied the input data, the easier it is for the machine to find patterns and the more accurate is the result.
So, if we want to train a machine, we need three things:
Data - examples of solutions and everything that can help in the learning process: statistics, example texts, calculations, indicators, historical events.
If you want to detect spam, you need examples of spam emails, if you want to predict the stock price, you need the price history, if you want to know the user interests, you need his likes or posts. We need as much data as possible. Data is collected over the years and combined into huge arrays - datasets that all IT corporations have. An example of a collection is a captcha, which asks you to choose all the photos with cars and remembers the correct answers.There is a big hunt for good datasets.
Features, properties, characteristics, attributes - they may be the mileage of a car, the gender of the user, price of shares, or even frequency of occurrence of a word in the text may be a feature.
The machine has to know exactly what to look at. It's good when the data is just in tables - the names of their columns are the features. But what if we have a hundred gigabytes of pictures with cats? When you have a lot of features, the model works slowly and inefficiently. Selecting the right features often takes more time than the rest of the training. But there are also reverse situations when the human itself decides to select only the "right" features in its view and introduces subjectivity into the model - it starts to lie. These are things that the machine has to pay attention to during the learning process. The fewer the features and the clearer they are labeled and shaped, the easier it is to learn. However, for complex problems, modern models have to take into account tens of millions of parameters that determine how inputs are converted into outputs.
Algorithms are a way of solving a problem. There can be many of them for the same problem and it is important to choose the most accurate and efficient one. The choice of the method determines the accuracy, speed and size of the finished model.
Spheres where Machine Learning can be useful in 2023
Banks and other businesses in the financial industry use machine learning technology for such purposes as identifying important data and preventing fraud.
They can identify investment opportunities or help investors know when to trade. According to BarclayHedge research, more than 50 percent of hedge funds use AI and machine learning to make investment decisions, and two-thirds use it to generate trading ideas and optimize portfolios.
Intelligent data analytics can also identify clients with high-risk profiles or use cyber surveillance to pinpoint signs of fraud.
More accurate stock market forecasts and brand capitalization estimates, decisions about lending products to individuals and businesses, determining the cost and appropriateness of insurance, and even reducing office queues while reducing staff costs are just some of the opportunities that are becoming available in this area.
Analyzing Stock Market Activity with Python
Machine learning is a rapidly growing trend in the healthcare industry, thanks to the advent of portable devices and sensors that can use data to assess patient health in real-time. In medical institutions, machine learning allows rapid processing of patient data, pre-diagnosis, and selection of individual treatment based on the patient's disease information from the database. ML also makes it possible to automatically identify risk groups when new strains of viral diseases emerge. This technology can also help medical experts analyze data to identify trends or "red flags" that can lead to improved diagnosis and treatment. Machine learning helps teach medical robots how to operate on patients on their own, taking many factors into account.
Websites that recommend products you might like based on previous purchases use machine learning to analyze your purchase history. Retailers rely on machine learning to collect data, analyze it, and use it to personalize the shopping experience, conduct marketing campaigns, optimize pricing, plan product shipments, and understand customer needs.
The streaming platforms build models using analytics, data from users and content to create a personalized audio experience for users and try to keep them as long-term customers. The audio streaming provider Spotify used raw data from playlists, listening behaviors from users, content, audio analytics and information gained from what users are browsing and skipping through. Using that data, Spotify built machine learning models to understand the similarities between specific pieces of music or podcasts, and what content users prefer.
Visualizing the songs by creating a dataframe that consists of Spotify audio features
Big Data is used to develop special offers for guests, taking into account the load on seating capacity in restaurants and cafes, and operates services to plan purchases for chefs.
The most obvious example of the use of machine learning in marketing are the search engines Google and Yandex, which use it to control the relevance of advertisements. The social networks Facebook, Instagram and others use their own analytical machines to research user interests and improve the personalization of the news feed. Marketing research, preceding the development and release of company products, becomes easier in terms of implementation, and the resulting data is more accurate. Clustering in groups with similar parameters turns customized offers into reality - it is possible to solve the problems of each individual, not groups of consumers.
ML is helping in finding new sources of energy. Soil analysis proves or disproves the presence of minerals and helps outline the area of future development. Machine learning is also used for predicting refinery gauge failure and optimizing oil distribution to make it more efficient and cost-effective. The number of options for using machine learning in this industry is huge and growing.
Downtime due to breakdowns, malfunctions or raw material shortages can cost a factory millions of dollars. Machine learning helps prevent them. It does this by collecting data from sensors on the equipment and then seeing at what indicators the failures occur. This information can be used to predict when and why downtime will happen, and how to avoid it. Machine learning helps make production safer: identifying minor changes in equipment operation and alerting in time to possible disasters.
For example, it could be that the temperature in a shop always rises before a machine breaks down. Then if the temperature rises, the system will alert the engineers and they will prevent the problem in time.
With the help of sensors and machine learning, you can not only perform narrow tasks, such as preventing breakdowns, but also manage the entire production:
reduce the percentage of rejected parts: analyze why rejects occur and how to avoid them;
optimize individual steps so they take less time;
use less material for production, and thus reduce costs;
monitor the condition of equipment, record its efficiency and workload;
automate individual stages of production.
Transport and Logistics
Data analysis and aspects of machine learning modelling are important tools for delivery companies, public transportation, and other transportation organizations. Analyzing data to identify patterns and trends is key for the transportation industry, which relies on improving route efficiency and predicting potential problems to improve profitability.
Fuel is one of the main cost items in logistics. With the help of machine learning it is possible to reduce its consumption: optimize routes or understand how to reduce the number of cars, preserving productivity.
A delay of even one vehicle leads to a failure in the entire supply chain: downtime, loss of money and customer dissatisfaction. Machine learning helps to avoid this: it predicts risks, helps prevent them in time, and adjusts delivery time taking all factors into account.
Today's security field is unimaginable without machine learning. Facial recognition systems in the subway and the use of cameras that scan faces and license plates when driving on the highway have become an integral part of human life and indispensable aids for police in finding criminals and lost people.
Government agencies, such as public safety and utilities, are particularly in need of machine learning because they have multiple sources of data from which to gain insights. For example, data sensor analysis identifies ways to improve efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.
DeepMind's AlphaFold neural network was able to decipher the mechanism of protein folding in 2020. Biologists have been working on this task for more than 50 years.
If your company is not already using ML, you will surely appreciate its potential in the near future, and AI will become a major driver of IT strategy for your business. Artificial intelligence is already playing a huge role in transforming the development of the IT industry: it is applicable to any workflow implemented in software-not just within the traditional business side of enterprises, but also in research, manufacturing processes and, increasingly, the products themselves.
Machine learning technology has already become part of everyday life, and the number of startups and products based on machine learning is growing rapidly. Being the cause of technological revolutions in some areas of the economy, ML is capable of being a driver on the scale of businesses. Today is a good time to think about integrating machine learning into business processes so as not to lose competitiveness.
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