Definition

Machine learning (ML) is artificial intelligence (AI) that can learn from data. ML can continuously pattern, evaluate, and relearn to improve model accuracy and performance using labelled data, algorithms, and statistical models without needing a system with specific instructions. Instead, the data, whether text, files, videos, etc., are marked by adding informative tags that identify the context so that ML algorithms can learn from them.

ML builds knowledge and expertise but is limited in how it is applied. For example, if you train a model to recognize cats and dogs, ML can’t learn how to bake a cake because that task is far outside its field of expertise.

Who uses Machine Learning and Why?

ML uses various algorithms to make decisions, predict group outcomes, and detect anomalies. Data clusters are data groups with similar characteristics not present in other data clusters. For example, anomaly detection identifies data with outliers, different from the data specified in a given cluster.

For example, an ML model will analyze the images of dogs and cats and create two clusters: one for dogs and one for cats. If the models analyze the idea of a bird, that image will be detected as an anomaly.

There are endless uses for ML in every industry, including financial services, insurance, health care, retail, government, military, agriculture, etc. You experience many examples of ML in the course of your daily life. Examples of how ML is used now include:

  • When you write a message on your mobile phone or in Microsoft Word, ML recommends words that reduce handwriting and entering errors.
  • Search engines use ML to make proposals when looking for specific information or a specific site.
  • Email filters use ML to identify spam.
  • Banking applications use ML to recognize fraudulent transactions and assess a person’s creditworthiness.
  • Insurance organizations use ML to identify fraudulent claims and predict premiums and also losses for their policies.
  • In the health sector, ML is used for DNA sequencing to identify disease patterns, detect health problems early, and improve the quality of care.
  • ML is used to recover the accuracy of goal identification and also transportation of military personnel and equipment in the military.
  • Automakers use ML for image recognition in cars and trucks.
  • Netflix uses ML to recommend what you might watch next
  • Speech recognition uses ML to identify patterns and refine a person’s speech recognition.

How does Machine Learning Work?

There are several ways that ML can work. However, it is one of the simplest examples of ML in action.

Step 1

The company must label and classify data. Data classification results from extracting features from data labels and organizing data by file type, content, and other metadata. For example, data can be classified by section, user, sensitivity levels, etc. These types of tasks are accomplish through the use of supervise ML models that are train on label data.

Step 2

This step is intertwine with Step 1. The model uses a training data set to learn. It is an interactive process and includes a feedback loop or error function if something is not classified correctly, thus going back to Step 1 for reclassification. ML develops a binary decision tree for this process, which is a structure based on a sequential decision process. Beginning from the root, the model evaluates a feature and selects one of two branches. This process is repeat until the last leaf on the tree is reach.

Step 3

The new classify data is now incorporate into the model for retraining to improve performance and verify accuracy. At this point, the model will classify the percentage of false-positive results. It is up to the company to decide what percentage of false positives is acceptable. For example, suppose the percentage of false positives is not good. In that case, more data must be fed into the system, or more features must be extract from the data to retrain the model to meet the acceptable level of false positives.

Step 4

Alternatively, ML can compact or optimize the generated model depending on the algorithm used. For example, you can chain multiple steps or make parallel treating.

What is the Difference Among Artificial Intelligence and Machine Learning?

Artificial Intelligence

AI is the arena of science that uses a computer to mimic human intelligence. True AI involves having a team know and having the team know how to apply that knowledge using reasoning and deductive logic. There are three subsets:

  • Narrow AI means the team is good at doing one thing, like playing chess.
  • General AI implies the team is good at a couple of things, but it’s not a cognitive being.
  • Super AI can learn, define its problems, learn more, and have a sense of self-awareness. Skynet, the artificial neural network-based conscious group mind and fictional artificial general superintelligence system in the Terminator series, is an example of super AI.

Fortunately, or unfortunately, we have only reached the narrow AI. While the goal of AI is to create intelligent teams that can simulate human thought, ML is a subset of AI that allows teams to learn on their own using data without receiving specific programming.

Advantages of Machine Learning Technologies and Why is it Important?

There are significant benefits associate with ML, including:

Ideal for Analyzing Large Data Sets

ML can work fast with substantial data sets, something the human mind is incapable of doing.

Quickly Identify Anomalies and Outliers

ML is better at noticing anomalies and outliers because it doesn’t look for something specific, unlike the human mind, but instead looks for something that shouldn’t be there.

Quickly Identify Trends and Patterns

ML can identify trends and patterns that people cannot, making ML a good application for data mining.

Continuously Improve Results

ML does not produce statistical results but constantly improves its models and effects with continuous retraining.

Automate Decision Making

Thanks to its constant retraining to improve performance, ML is ideal for quickly automating decisions. For example, ML may analyze your online behaviour and recommend relevant websites, products or services that you may want. ML is an essential component in improving the customer experience (CX).

Conclusion

Machine learning is the study of computer algorithms that can automatically improve through experience and also by using data. It is seen as a part of artificial intelligence.

Also Read: What is Ethereum? – Work, Main Terms, and More