Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, the text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
Some Machine Learning Methods
Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The type of algorithm a data scientist chooses to use depends on what type of data they want to predict.
- Supervised learning: In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm are specified.
- Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. Both the data algorithms train on and the predictions or recommendations they output are predetermined.
- Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
- Reinforcement learning: Reinforcement learning is typically used to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.
How supervised machine learning works
Supervised machine learning requires the data scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:
- Binary classification: Dividing data into two categories.
- Multi-class classification: Choosing between more than two types of answers.
- Regression modeling: Predicting continuous values.
- Ensembling: Combining the predictions of multiple machine learning models to produce an accurate prediction.
How unsupervised machine learning works
Unsupervised machine learning algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised learning algorithms are good for the following tasks:
- Clustering: Splitting the data set into groups based on similarity.
- Anomaly detection: Identifying unusual data points in a data set.
- Association mining: Identifying sets of items in a data set that frequently occur together.
- Dimensionality Reduction: Reducing the number of variables in a data set.
How semi-supervised learning works
Semi-supervised learning works by data scientists feeding a small amount of labeled training data to an algorithm. From this, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. The performance of algorithms typically improves when they train on labeled data sets. But labeling data can be time-consuming and expensive. Semi-supervised learning strikes a middle ground between the performance of supervised learning and the efficiency of unsupervised learning. Some areas where semi-supervised learning is used include:
- Machine translation: Teaching algorithms to translate language based on less than a full dictionary of words.
- Fraud detection: Identifying cases of fraud when you only have a few positive examples.
- Labeling data: Algorithms trained on small data sets can learn to apply data labels to larger sets automatically.
How reinforcement learning works
Reinforcement learning works by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. Data scientists also program the algorithm to seek positive rewards which it receives when it performs an action that is beneficial toward the ultimate goal and avoid punishments that it receives when it performs an action that gets it farther away from its ultimate goal. Reinforcement learning is often used in areas like:
- Robotics: Robots can learn to perform tasks in the physical world using this technique.
- Video gameplay: Reinforcement learning has been used to teach bots to play a number of video games.
- Resource management: Given finite resources and a defined goal, reinforcement learning can help enterprises plan how to allocate resources.
Uses of machine learning
Today, machine learning is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook's News Feed.
Facebook uses machine learning to personalize how each member's feed is delivered. If a member frequently stops to read a particular group's posts, the recommendation engine will start to show more of that group's activity earlier in the feed.
Behind the scenes, the engine is attempting to reinforce known patterns in the member's online behavior. Should the member change patterns and fail to read posts from that group in the coming weeks, the News Feed will adjust accordingly.
In addition to recommendation engines, other uses for machine learning include the following:
- Customer relationship management: CRM software can use machine learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.
- Business intelligence: BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points, and anomalies.
- Human resource information systems: HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position.
- Self-driving cars: Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.
- Virtual assistants: Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.
Importance of human Interpretable machine learning
Explaining how a specific ML model works can be challenging when the model is complex. There are some vertical industries where data scientists have to use simple machine learning models because it's important for the business to explain how each and every decision was made. This is especially true in industries with heavy compliance burdens like banking and insurance.
Complex models can accurate predictions, but explaining to a layperson how the output was determined can be difficult.
The future of machine learning
While machine learning algorithms have been around for decades, they've attained new popularity as artificial intelligence has grown in prominence. Deep learning models, in particular, power today's most advanced AI applications.
Machine learning platforms are among enterprise technology's most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM, and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, data classification, model building, training, and application deployment.
As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify.
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