When we work on growing our company, we face various problems. Some of these might include: How can we improve the product? How can we gain more customers? Or, how can we make better use of resources? In fact, you may be the person responsible for solving these problems. However, could these obstacles be solved with Artificial Intelligence? How? This is where the importance of machine learning comes into play.
By definition, Machine Learning is a branch of computer science directly related to Artificial Intelligence. Machine Learning is the ability of a software or machine to learn through the adaptation of various algorithms in its programming, i.e., to identify complex patterns within multiple data sets.
Machine Learning is a technology that allows many operations to be carried out, reducing the need for human intervention. This system offers a great advantage in controlling a large amount of information more effectively. An example is AIV robots, which are capable of creating their own routes and trajectories. Although machine learning may sound foreign to our daily lives, contact with this technology is very common: fraud detection or online recommendations on Netflix.
The iterative aspect of machine learning is important because, as models are exposed to new data, they can adapt independently. They learn from previous calculations to produce reliable and repeatable decisions and results. It is a science that is not new but has gained new momentum, and thus, you should consider it in your company’s setup.
How Does Machine Learning Work?
Machine Learning uses algorithms that perform many actions on their own. These algorithms make their own calculations based on the amount of data collected in the system, and as more data is gathered, the resulting actions become better and more precise. To some extent, these computers program themselves, using these algorithms, which function as an engineer capable of designing new responses based on the information provided through its interface.
All of this data is transformed into an algorithm, and the larger the data set, the more complex and effective the calculations it provides for the computer system.
Machine Learning is very helpful for modeling and collecting knowledge to provide specific information and create better tools for people’s work. In the coming years, it is expected that the use of algorithms will be an important factor in professionalism and competitiveness.
For this reason, many companies are using Neural Networks with Machine Learning in their products and services, taking advantage of the benefits that the application of this system brings. Its use has improved their work experience, as well as optimized their production processes.
How Can I Create Value in My Company with Machine Learning?
Around us, we find different uses of machine learning. For example, Netflix, Amazon, and Spotify use recommendation systems to suggest products or content. We can also find Artificial Intelligence in applications as different as the self-driving cars developed by Google or in medical diagnostic systems.
Think about your work or your daily activities. Can all the tasks in your business be successfully handled by your team? Many tasks can be solved by humans, but when the information or tasks reach a very large volume, those actions become impossible. Just as certain manual tasks can be automated with robotic arms, machine learning replaces mental data analysis tasks with self-learning systems. So, think: What decision can be automated? What data can be used?
The reason machine learning is useful and creates value is that it automates decision-making using data related to the problem as input.
What Types of Machine Learning Exist?
The machine learning system is based on evidence and experiences in the form of data, which it comprehends by detecting behaviors or patterns. In this way, various scenarios are analyzed by the system and provide solutions for specific tasks.
Based on the number of examples provided in a situation, a model is created that deduces and generalizes behavior that has already been seen; from this pattern, it proceeds to make predictions for new cases. Let's look at the main types of machine learning:
Supervised Learning
This is based on training data. It is where a certain amount of data is provided, with labels defining the data, so that the system gets trained. For example, a computer is given images of cats or dogs with labels identifying them as such. After providing a sufficient amount of this data, new data can be introduced without labels, and the system can make predictions based on patterns it recorded during training.
Unsupervised Learning
In this type of learning, labels or true values are not used. The aim of this system is to abstract and directly understand patterns of information. It is a training method similar to how humans process information. It is also known as a problem model or clustering. clustering.
Semi-Supervised Learning
This type of learning takes into account both supervised and unsupervised data, combining both approaches so that classification can be done properly.
Reinforcement Learning
Systems learn with this learning model through experience. For example, the behavior of a self-driving car can be regulated in the event of a wrong decision. This is done through a system that records these values and responds with a system of rewards and punishments, forcing the vehicle to develop an effective learning process to perform its tasks.
Transduction
This system is very similar to supervised learning, although it does not build a clear function. It only attempts to predict varieties of future examples.
Multi-task Learning
These are learning methods that use previously learned knowledge by the system, where they are prone to face various problems.
What Are You Waiting For to Integrate One of These Types of Machine Learning into Your Company?
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