What is machine learning

The simplest definition of machine learning comes from what Berkeley said: Machine learning is a branch of AI that explores ways to make computers more efficient based on experience.

In order to understand this definition more deeply, we will then split the analysis.

The branch of AI: Artificial intelligence is a research and development that enables computers and their systems to successfully complete tasks that typically require human intelligence to perform. Machine learning is an indispensable part of the technique and process of training a computer to perform the above tasks.

Exploration methods: At this stage, machine learning technology is still emerging. Although some models for training computers have been identified and used, different models are needed for different business problems. Different models can be used when training computers. More models will be developed over time.

Help the computer to improve its performance: In most cases, to get the computer to complete the task of artificial intelligence, it needs to be practiced and adapted with the help of manual help.

Based on experience: Another way of providing experienced AI is to provide data for it. As more data is entered into the system, the computer can more accurately respond to it and future data that it will encounter.

How does machine learning work?

Let's see how machine learning works:

Collection: Machine learning depends on the data. The first step is to ensure that you have the right data for the problem you are trying to solve.

Cleanup: Data can be generated by different sources, contained in different file formats, and represented in different languages. Information may need to be added or removed from the dataset because some instances may be missing information, while others may contain unwanted or unrelated entries. Its preparation will affect the reliability of its availability and results.

Split: Depending on the size of the data set, only a portion may be needed. From the selected samples, the data should be divided into two groups: one for the training algorithm and the other for the evaluation algorithm.

Training: This phase is primarily to find a function that accurately completes the selected goal. Depending on the type of model used, different training forms are used: for example, fitting a line in a simple linear regression model and generating a decision tree for a random forest algorithm. In order to better understand that we take the neural network, when the general algorithm encounters a part of the data set, it will try to process the data. Measuring its own performance and automatically adjusting its parameters (also known as backpropagation) until it can produce the desired results with sufficient reliability. Until it can continuously produce the desired results, and has sufficient reliability.

Evaluation: Once the algorithm performs well on the training data, it will again use the data that has not been seen for measurement. This process allows you to prevent overfitting, but this only happens when the learning algorithm is working well and is related to your training data.

Optimization: The model is optimized for integration within the target application to ensure its efficiency.

Is there a different type of machine learning?

Many different models can be used in machine learning, but they are usually divided into three different types of learning: supervised, unsupervised, and enhanced. Some models are more suitable and perform better than others, depending on the task to be completed.

Supervised learning: It is characterized by clearly marking the correct results of each data point when training the model, in order to find the relationship between them, to ensure that the prediction or classification can be correctly made when introducing unallocated data points.

For example, in the study of stock prices, the relationship between data points can be analyzed, and the regression data can be used to predict the next data point.

Unsupervised learning: The characteristic of this type of learning is that the algorithm does not mark the results during the training model period, but finds a meaningful relationship directly between the data points. Its value lies in the discovery mode and relevance. For example, a person who likes this bottle of wine also likes this one.

Reinforcement learning: This type of learning is a combination of supervised learning and unsupervised learning. It is often used to solve more complex problems. In practice, this type of learning can be applied to control robot arms, find the most efficient motor combinations, robot navigation and other fields. At the same time, logic games are also very suitable for intensive learning such as poker. Other applications of reinforcement learning are also common in strategic planning for logistics, scheduling, and tasks.

Where can machine learning be applied?

Companies need to consider the three phases of machine learning development and their applications. These three phases are: descriptive phase, predictive phase and normative phase.

The descriptive phase refers to the recording and analysis of historical data to enhance business intelligence. Provide descriptive information to managers and better understand the outcomes and consequences of past actions and decisions. This process has now become the norm for most large companies around the world.

The second phase of applying machine learning is prediction. Collecting data and using it to predict specific results can increase responsiveness and make decisions more efficiently.

The last normative phase is the most advanced machine learning phase, which has been applied and corporate activities, and is moving forward with the push of emerging companies. Understanding the causes, motivations, and contexts for effective and efficient business practices is a prerequisite for optimal decision making, and predicting only behavior or results is not enough. Specifically, this phase is possible when people and machines are combined. Machine learning is used to find meaningful relationships and predict outcomes, while data experts act as translators to understand why relationships exist. In this way, decisions can be made more accurately.

In addition, in addition to predictive insights, interested friends can also learn about another machine learning application: process automation. Here is an introduction and comparison of these two concepts.