What Is Machine Learning?

 

What Is Machine Learning?

Machine learning (ML) is the subset of artificial intelligence that focuses on building systems that learn—and improve—as they consume more data. Artificial intelligence is a broader term that refers to systems or machines that mimic human intelligence. Machine learning and AI are often discussed together, and the terms are sometimes used interchangeably, but they don’t mean the same thing.

In short, all machine learning is AI, but not all AI is machine learning.

Key Takeaways

• Machine learning is a subset of AI.
• The four most common types of machine learning are supervised, unsupervised, semi-supervised, and reinforced.
• Popular types of machine learning algorithms include neural networks, decision trees, clustering, and random forests.
• Common machine learning use cases in business include object identification and classification, anomaly detection, document processing, and predictive analysis.
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Machine Learning Explained

Machine learning is a technique that discovers previously unknown relationships in data by searching potentially very large data sets to discover patterns and trends that go beyond simple statistical analysis. Machine learning uses sophisticated algorithms that are trained to identify patterns in data, creating models. Those models can be used to make predictions and categorize data.

Note that an algorithm isn’t the same as a model. An algorithm is a set of rules and procedures used to solve a specific problem or perform a particular task, while a model is the output or result of applying an algorithm to a data set.

Before training, you have an algorithm. After training, you have a model.

For example, machine learning is widely used in healthcare for tasks including medical imaging analysis, predictive analytics, and disease diagnosis. Machine learning models are ideally suited to analyze medical images, such as MRI scans, X-rays, and CT scans, to identify patterns and detect abnormalities that may not be visible to the human eye or that an overworked diagnostician might miss. Machine learning systems can also analyze symptoms, genetic information, and other patient data to suggest tests for conditions such as cancer, diabetes, and heart disease.

The key features of machine learning are the

• Automatic discovery of patterns
• Prediction of likely outcomes
• Creation of actionable information
• Ability to analyze very large volumes of data

Types of Machine Learning

There are four main types of machine learning. Each has its own strengths and limitations, making it important to choose the right approach for the specific task at hand.

• Supervised machine learning is the most common type. Here, labeled data teaches the algorithm what conclusions it should make. Just as a child learns to identify fruits by memorizing them in a picture book, in supervised learning the algorithm is trained by a data set that’s already labeled. An example of supervised machine learning is a spam email filter, where the algorithm is trained on a labeled data set in which each email is tagged as either spam or not spam. The model learns from these labeled examples and then can predict whether new incoming emails are likely spam or not based on the patterns it identified. This type of supervised learning requires a human expert to provide the correct answers by labeling data so the algorithm can learn and make accurate predictions in the future.
  
  Note that the goal here isn’t to train using pristine data. You want to mimic what the system will see in the real world—some spam is easy to spot, but other examples are stealthy or borderline. Overly clean data leads to overfitting, meaning the model will identify only other pristine samples.
• Unsupervised machine learning employs a more independent approach, in which a computer learns to identify complex processes and patterns without relying on previously labeled data. Unsupervised machine learning not only involves training based on data that doesn’t have labels; there’s also no specific, defined output, such as whether an email is likely spam.
  
  Unsupervised machine learning tends to spot groupings of similar data, creating clusters. Once trained, the model can identify similar patterns and put that data into its appropriate group. A common use of unsupervised machine learning is recommendation engines, which are used in consumer applications to provide “customers who bought that also bought this” suggestions. When dissimilar patterns are found, the algorithm can identify them as anomalies, which is useful in fraud detection.
• Semi-supervised machine learning addresses the problem of not having enough labeled data to fully train a model. For instance, you might have large training data sets but don’t want to incur the time and cost of labeling the entire set. By using a combination of supervised and unsupervised methods, you can often achieve a fully trained model. The training process starts off like supervised learning, using labeled data to get initial results and establish guidelines for the algorithm. When labeled data is exhausted, the semi-trained model is given the unlabeled data sets. It uses the training it has and looks at the unlabeled data, with the goal of adding to the labeled data set. If the model can find an appropriate label for a sample with high certainty, that sample is added to the labeled data. The learning process starts again, but now with a larger set of labeled samples. By iterating, more samples are labeled with what’s often called pseudo-labels, and the model can be further refined.

  Reinforcement machine learning, like unsupervised machine learning, uses unlabeled data sets and allows algorithms to evaluate the data. However, reinforcement learning differs in that it’s working toward a set goal rather than exploring data to discover whatever patterns might exist. With an objective in mind, the algorithm proceeds in a trial-and-error process. Each move receives positive, negative, or neutral feedback, which the algorithm uses to hone its overall decision-making process. Reinforcement learning algorithms can work on a macro level toward the project goal, even if that means dealing with short-term negative consequences. In that way, reinforcement learning handles more complex and dynamic situations than other methods because it allows the context of the project goal to influence the risk in choices. Teaching a computer to play chess is a good example. The overall goal is to win the game, but that may require sacrificing pieces as the game goes on.