Not man, but machine. Machine learning: algorithms, tasks, functions

Content:

1. What is machine learning?

• What machine learning is for
• Where machine learning is used
  

2. How machine learning works

• Methods of machine learning
• Machine Learning Tasks
  

3. How to become a Data Science professional

• Stages of machine learning work
• How to master machine learning
  

Conclusion

What is machine learning?

Machine learning is a way to help computer systems, like humans, learn and get better from experience. Machine learning and programming are very different things. While in the second case, the programmer sets the instructions to perform the task, in machine learning, the system itself finds the relationships, summarizes the data, and produces a solution.

For example, if we want a computer system to learn to distinguish between cats and tigers, we have to load many pictures of each animal into it. And when we provide the program with a new photo, it will compare it with the previously received ones, draw conclusions and give an answer who is depicted in the photo. There are different methods of machine learning, but the most popular one is using a neural network.

What machine learning is for

• Machine learning can create programs that can think like humans, but they have serious advantages: unlike humans, they don't get tired or sick.

• Machine learning allows you to analyze big data, it would be difficult for a human to process it manually.

• Using machine learning, it is possible to create systems and algorithms that learn by themselves. As time goes on, models adapt to new conditions, becoming more accurate and efficient.

• Machine learning is capable of performing complex tasks that are not always humanly possible. For example, in the field of image and sound processing, neural networks can be used to recognize objects in photos and use voice assistants.

• Another important aspect of machine learning is the creation of new products and services. Without machine learning, their emergence would not have been possible. Machine learning algorithms make it possible to create technologies of the future, for example, a cab traveling on its own, without a driver.

Where machine learning is used

Machine learning is applied in different areas of life:

Banks

In banking, the application of machine learning helps to avoid some risks. Banks use machine learning algorithms to automatically detect fraudulent activity, to identify deviations from the norm in customers' financial transactions. This helps to stop potential financial crimes and keep people's money safe.

Another example of the application of machine learning is credit scoring. This is the process of determining the borrower's ability to pay. This procedure is needed to determine if a person can be given a loan or if they will not repay it. Over the years, banks have accumulated a large amount of data about their customers: their income, employment, marital status, and credit history. This information is used to train a machine learning model that will be able to predict the likelihood of loan repayment.

Medicine

Here, machine learning is used for tasks such as diagnosing and predicting diseases. Machine learning algorithms analyze data from tests, exams, and patient medical records, finding hidden connections between different factors and diseases. This allows doctors to diagnose more accurately, make assumptions about how the disease may progress, and prescribe the right treatment.

Marketing

In this field, machine learning is used to analyze information and make predictions about how consumers will behave. Machine learning algorithms can process big data and find hidden trends and patterns¹. This will help predict customer preferences, help create more personalized offers, and improve the quality of service.

Agriculture

Here, the goal of machine learning is to increase crop yields. Algorithms can be used to analyze information about soil, weather, and other factors affecting future yields. All this helps to find the problem and take action in time.

Business

Machine learning in business is being applied successfully. One example is the automation of business processes. With machine learning algorithms, companies can improve quality of work and reduce costs. So, automated data processing will help to save time and effort for employees. Apart from this, machine learning in business is used to make predictions for the future. Algorithms help to forecast growth for goods and services, determine market demand, and pricing policies. Due to all this, the company develops more successfully than its competitors.

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¹Using a specific algorithm that already exists to solve a problem

How does machine learning work?

Machine learning is like a complex mechanism that processes large amounts of data and outputs the result. Essentially, it is a set of algorithms and mathematical models that are trained to automatically detect patterns and produce results.

To better understand what kind of model this is, let's take an example from life. Let's imagine a neurosurgeon looking at hundreds of X-rays. He learns to recognize brain diseases on them: tumors, hemorrhages, clots. To do this, the doctor learns the basics of anatomy, the laws of physics, biomechanics, and radiation diagnostics.

With experience and training, the neurosurgeon develops what is known as a model that can already almost automatically determine the presence or absence of disease on brain X-rays. The machine learning model works on the same principle, but it is many times superior to the human brain. It can use its accumulated experience and knowledge to automatically categorize information, highlight patterns, and suggest solutions.

The basics of machine learning can be compared to a sponge that receives data and has to produce an answer. But, this sponge has different parameters that affect how the data will be processed. The customization of these settings depends on what machine learning techniques will be used. Working with model data is taught by a "Data Science Specialist".

Methods of machine learning

There are several ways to do machine learning and each has its own advantages:

• Learning with a teacher is a method in which a model is trained on labeled data, where each example corresponds to a target label. The model seeks to discover dependencies and patterns that enable predictive labeling for new data. For example, the algorithm is trained to identify only triangles from a set of geometric shapes. The model classifies all figures according to the parameters specified for this particular geometric figure and finds relationships.
  
  
• Learning without a teacher is an approach in which the data is no longer labeled in advance, patterns need to be identified on your own. Let's return to the same example with triangles, but now we need to find them without known parameters.
  
  

• Reinforcement learning is a method in which the model learns to make a decision based on the situation. In return, she receives a reward or a penalty. It's like a computer game where the outcome depends on the actions of the person.

Machine Learning Tasks

Learning with a teacher

The challenge is to categorize new, unknown data based on the knowledge gained. Examples of such a task could be categorizing emails (spam or non-spam), classifying images, or determining the tone of texts. In addition to classification, learning with a teacher can also be used to solve regression problems. 

Here, the model is trained to predict a continuous value based on the input data. For example, it could be predicting the price of a property based on its characteristics or forecasting the demand for a product based on various factors.

Learning without a teacher

This approach is used to solve classification problems without prior data partitioning. The model independently finds patterns and structure in unlabeled data, identifies groups and clusters of similar elements. This can be useful, for example, for segmenting your customer base by behavioral characteristics or analyzing social networks.

Reinforcement learning

It is based on the model's interaction with the environment and the rewards it receives for doing the right thing. This method is used, for example, to create bots and agents capable of making decisions independently in unpredictable environments.

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The job of a machine learning specialist

The work of a machine learning specialist involves several steps:

• Data Collection. A machine learning specialist collects the data that will be used to train the computer. They can be collected from a variety of sources, including databases, the Internet, sensors, and other available sources of information.
  
  
• Data partitioning. The next step is to define and categorize the different elements of the dataset. For example, when it comes to creating a model capable of recognizing images, the markup may include identifying specific objects such as faces, cars, or animals.

• An exploratory analysis. After collecting and partitioning the data, the expert performs exploratory analysis to understand the characteristics of the dataset and determine effective methods for training the computer. This may include statistical analysis of data, visualization, and other techniques to help identify patterns and patterns.
  
  
• Model Training. In this stage, a machine learning expert creates and trains the model using the selected methods and algorithms. It adjusts model parameters, trains on the data and optimizes the process to achieve the best result.

• Testing and Assessment. Once the model has been trained, the expert performs testing to verify its performance and accuracy. It can use separate test datasets or perform cross-validation to evaluate the model's performance on different data. As a result, the technician gets an estimate of the model's performance and can make adjustments if necessary.

How to master machine learning?

If you want to master machine learning, you should start with simple tutorials². Off-the-shelf frameworks such as Google's TensorFlow are great for this. It can be used to develop and train neural networks, making the process accessible to a wide range of users.

One of the additional bonuses is the Google Colab service. It is a free virtual machine for training neural networks. You can use it to experiment and deepen your knowledge of machine learning. Today, it is one of the main languages for machine learning. Thanks to a rich set of libraries, it can be used to efficiently analyze data and implement complex machine learning models. Using Google's Colab service and the Python programming language, you can deepen your knowledge of machine learning.

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²Step-by-step instructions that explain how to achieve the desired result

By the way, in CODDY's "Python Programming and Machine Learning" course, your child can learn about the principles of machine learning, train some of their own models, and learn about different ways to analyze data.

Another powerful language for machine learning is R. It is designed specifically for working with data analysis and statistics. Among the programming languages that are used for machine learning Java. It can be used to develop complex algorithms and high-speed computing.

The choice of programming language for machine learning depends on the needs and experience of the developer. But, the key criteria are computational efficiency, data usability, and extensive machine learning libraries.

But first of all, of course, you need to familiarize yourself with the basics of machine learning. If you do not have a specialized education, you can improve your knowledge at CODDY courses.

Have you mastered the theory? Start practicing! Try to realize your project. Try to choose tasks that are not only interesting to you, but also relevant. This will help you deepen your knowledge and build a portfolio that will come in handy when looking for a job.

Conclusion

Machine learning can solve complex problems, some of which even humans are not capable of handling. With its high performance, ability to handle large amounts of data, find hidden images, process images and sound, in many areas of life, it is already indispensable. And this is just the beginning! In the future, machine learning will still surprise us with its capabilities.