Your company’s ads target prospective customers, your CRM software delivers insights to sales for funnel optimization, and your chatbots converse with customers—these are all examples of machine learning at work.
Machine learning is a type of artificial intelligence (AI) that enables computers to imitate human learning processes on their own based on data input. Computers learn from algorithms that programmers develop and the data set that programmers feed into it.
What is AI? Watch at Webopedia:
6 machine learning types
Machine learning breaks down into five types: supervised, unsupervised, semi-supervised, self-supervised, reinforcement, and deep learning.
In this type of machine learning, a developer feeds the computer a lot of data to train it to connect a particular feature to a target label. A feature could be images or text that the computer matches to an object to identify it.
For instance, credit card companies may use this type of machine learning to help determine who should receive a credit card offer. The machine learning algorithm will recognize credit scores above a certain threshold to be included in the offer.
Supervised machine learning has many applications. A few of them include:
- Sentiment analysis to get a sense of market positioning
- Speech recognition to enhance multimedia interfaces with assistive technology
- Recommendations for qualifying leads based on buyer readiness
With supervised machine learning it’s easy to control the type and number of classes used for organizing data. This method also makes it easy to set class definitions in a cut and dry manner in the form of a mathematical formula to boost the computer’s accuracy in classification.
In spite of these benefits, supervised learning requires a lot of human intervention at the outset. Programmers need to define classes and labels, which is a time-intensive process that is prone to error.
While it may be useful for simple classification tasks, it won’t capture nuance like other machine learning types do. For instance, it might be able to distinguish between good and bad customer feedback marked with words, such as “great” or “horrible,” respectively. However, unless programmers add labels “terrific” or “abysmal,” the machine might not pick up on those terms to arrive at an accurate classification.
Supervised learning is also susceptible to adversarial machine learning. This involves small but malevolent changes to the training data in machine learning algorithms that lead to inaccurate information. For these reasons, use of supervised machine learning is not as widespread.
For more on how human bias creeps into ML, watch this video from TechRepublic:
In unsupervised machine learning, a computer picks up on patterns and structures in unlabeled input data.
Once the computer system identifies a pattern, there is no output value to match it with. It therefore creates a new data category or output label on its own. That way, it can classify similar raw data that it encounters in the future.
Because companies deal with more unlabeled or unstructured data than labeled data, unsupervised learning is much more common.
Unsupervised machine learning is used in situations where you want to discern a pattern, though you might not know which pattern you’re looking for exactly. This type of machine learning is concerned with finding outliers in data. And a computer can’t know what the outliers are until it collects and learns from a lot of data.
For example, unsupervised learning comes into play for device maintenance, such as mistimed traffic light signals. Only after collecting data on traffic control systems can a computer learn what is “normal” traffic signal activity versus what isn’t.
Read more at Datamation: Key Machine Learning (ML) Trends
As a hybrid of supervised and unsupervised machine learning, semi-supervised machine learning involves training with a small sample of labeled input data and then performing classification and regression tasks. These steps prepare it to generate new labels for unlabeled data it encounters.
Semi-supervised machine learning mimics inductive reasoning by generating broader insights based on what it already knows from its small, labeled dataset.
A common application of semi-supervised machine learning is a text document classifier that helps companies catalog and organize large amounts of content, for instance.
Self-supervised machine learning, also known as predictive or pretext learning, allows a computer to do more with less—learn more from fewer labels and smaller samples.
In self-supervised machine learning, a computer learns from a small set of unlabeled sample data. It learns by means of an artificial neural network to automatically create its own output labels.
The computer’s model parses data input into smaller parts. In doing this, the model learns one part of data input in relation to another part. From there, the model infers or fills in the blanks by learning how the known and unknown parts relate to each other. Using what it knows first to make inferences, the computer model gets a clearer picture of the overall object, image, or task in question.
Because self-supervised machine learning works well with incomplete, distorted, or corrupted data, it applies to natural language processing (NLP). Using this machine learning type, a computer can, for example, infer missing words from text or speech. It also learns to recognize partial images of people or objects.
Self-supervised learning is different from supervised and semi-supervised learning because it uses only unlabeled data—no labeled data.
Self-supervised also differentiates itself from unsupervised learning in that it learns from a small sample data set rather than a massive one.
Reinforcement machine learning uses contextual information to quickly find the best way to achieve a goal with limited information.
In reinforcement learning the computer continuously receives input from the environment. The computer system therefore also constantly learns and improves based on favorable and unfavorable reactions in the environment.
The computer trains itself and makes critical decisions based on experience and without human intervention.
Reinforcement machine learning is typically applied in robotics, self-driving vehicles, and traffic light management systems.
Deep learning is an advanced type of machine learning, as it mimics human learning and reasoning. Deep learning consists of three or more layers of artificial neural networks:
- An input layer that receives incoming data and forwards it to the hidden layer
- A hidden layer, which is often made up of node layers
- Node layers that vary in structure and complexity, depending on the type of data they process and the tasks they must perform
- An output layer that produces the final result, such as a recommendation or decision
Each layer contains interconnected nodes—much like synapses between neurons in the human brain—that can operate in a supervised, semi-supervised, self-supervised, or unsupervised manner.
Why deep learning is more advanced than other types of machine learning
Scalable algorithms: Deep learning algorithms scale up as they take in more data, so they’re capable of incorporating and improving from ever-growing quantities of data. This is a key benefit over other machine learning types that see diminishing return as they collect and process more data.
Nuanced data extraction: Deep learning recruits each neural network layer to automatically extract multiple features of an object all at once and assign labels to those features. Because of this, deep learning machine models learn more quickly than other types of machine learning.
Speed: In addition, the continuous evolution of cloud computing and graphics processing units (GPUs) that power deep learning computers means that they learn in a matter of hours rather than weeks. However, because of their extraordinary ability and accuracy, deep learning computers require more computing power and resources than other types of machine learning.
Deep learning helps businesses leverage data to make high-level decisions and solve complex problems. It also saves time by automating routine tasks.
Also read at Datamation: 5 Top Deep Learning Trends
Benefits of machine learning
Machine learning boasts several benefits for today’s businesses:
- Smarter business decisions
- Automation to save time on routine tasks
- Real-time data insights
- Better planning through forecasting
- Enhanced security through real-time alerts
- Improved customer experience through personalization and accessibility
As companies glean insights from an exponentially growing dataset, machine learning is becoming indispensable for long-term business success. It fortifies business’s existing security measures, frees up employees to perform more cognitively demanding tasks, and helps business leaders find and act on opportunities that align with strategic business growth.
Which type of machine learning should you use?
Each type of machine learning has its own particular use cases. To choose the right type of machine learning for your desired goals or tasks, consider the following questions as a starting point:
- How large of a dataset are you working with?
- How much computing capacity do you have?
- Is the data structured or unstructured?
- How critical is the desired task or outcome? How important is it to have accurate results from the start?
Regardless of which type(s) of machine learning your business implements, a common benefit among all of them is saved time and smarter decision making. Machine learning does not replace human intelligence in running a business; rather it enhances and assists humans.
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