4 Machine Learning Types and Why They Matter

The value of machine learning is undeniable. From improving medical diagnosing to automating repetitive tasks, machine learning types can be applied to some of the world's biggest (and smallest) learning problems to find better solutions more quickly.

The potential for artificial intelligence (AI) and machine learning is practically limitless, with these technologies still evolving rapidly. Researchers from the MIT Initiative on the Digital Economy examined the potential impact of machine learning on the global economy, and their findings suggest that no occupation will remain unaffected.

But while machine learning will touch every field and occupation, the researchers also suggest that 100% machine learning automation is also unlikely. The key to optimizing its potential is learning to adapt processes so they leverage machine learning and human intervention together.

Learn more about the four main types of machine learning and their unique applications, as well as common challenges and benefits associated with machine learning services. We’ll also explore key considerations you should review before making decisions about machine learning.

What Are the Different Machine Learning Types?

Machine learning (ML) is a discipline of AI — a set of tools used for data science and to solve a variety of problems. AI is a broad field that encompasses various approaches to creating intelligent machines. ML is a specific approach within AI that involves building algorithms and models — empowering computers to learn from data and improve performance over time. 

ML is fundamentally about developing systems that can learn from experience and examples rather than relying on hard-coded rules or traditional programming. These models can be trained on vast amounts of data, enabling them to recognize patterns and make accurate predictions or decisions without explicit instructions.

There are four main types of machine learning: supervised learning, unsupervised learning, reinforcement learning and semi-supervised learning. Let’s review each of these in greater detail.

Supervised Learning

In supervised learning, the machine is given a set of training data, including input data and the corresponding expected output. The machine then learns to map the input to the output using a function so it can produce the expected output for any new input.

Supervised learning can be divided into two main categories: regression and classification. In regression, the output variable is a continuous value, and the goal is to predict a numerical value. One example is predicting the price of a house or the weight of an object. Linear regression is a common algorithm used for regression problems.

In classification, the output variable is a discrete value, and the goal is to predict which category the input data belongs to. One example is identifying whether an incoming email is spam. Logistic regression and decision trees are commonly used algorithms for classification problems.

Unsupervised Learning

In unsupervised learning, the data isn't labeled. The algorithm must find patterns in the data. This type of machine learning is used to find groups in data and to make predictions about new data points. It’s also used for tasks such as clustering and dimensionality reduction.

Unsupervised learning algorithms are used to find patterns in data. The algorithm looks for structure in the data and tries to group together similar instances. You can cluster data points together or for dimensionality reduction, which aims to reduce the number of features in the data while preserving as much information as possible.

There are many types of unsupervised learning algorithms, including k-means clustering and principal component analysis (PCA). K-means clustering partitions the data into a set of K clusters, where each cluster is defined by a centroid. PCA is a linear transformation that projects the data onto a set of orthogonal axes so that the variance is maximized.

Reinforcement Learning

Reinforcement learning is a type of machine learning that involves training models to make decisions in environments where there are clear consequences to their actions. In other words, the model is reinforced with feedback based on its performance so it learns which actions lead to the best outcomes.

This type of learning is powerful for AI applications because it simulates how humans learn from experience. 

Semi-Supervised Learning

In semi-supervised learning, labeled and unlabeled data is used together to train the model. Generally, the quantity of labeled data is smaller than the amount of unlabeled data. Semi-supervised learning algorithms are often used as an alternative to supervised learning alone. 

What Are the Benefits and Challenges of Machine Learning Model Types?

Model development is an essential part of machine learning and includes building, testing and refining models that can accurately make predictions or decisions based on available data. 

Each of the different machine learning model types has their own use cases, advantages and challenges. Learn more about the advantages and disadvantages of each type.

Considerations for Supervised Learning

Supervised learning is a powerful machine learning technique that uses labeled data to make accurate predictions and classifications. Its ability to learn complex relationships between data points has made it particularly useful in areas such as natural language processing, computer vision and object detection and robotics.

One benefit of supervised learning is its ability to learn from more than one data set, making it useful for large-scale tasks. It can also identify complex relationships between data points, leading to new insights and discoveries.

However, supervised learning requires large amounts of labeled data to train the algorithm accurately. Identifying the correct features to use for training can be difficult. Supervised learning can also be prone to overfitting, leading to inaccurate predictions.

Considerations for Unsupervised Learning

Unsupervised learning is a type of machine learning that doesn't require labeled data for training. It uses algorithms to discover patterns and relationships in data, making it useful for uncovering insights in large datasets that are difficult to identify manually.

Another benefit of unsupervised learning is its ability to find patterns in data, especially where manual efforts would have low odds of success. This is particularly useful when the data is unstructured, noisy and/or incomplete. Unsupervised learning can also identify outliers from normal distributions and clusters, which can spawn new insights and discoveries.

Unsupervised learning has challenges, starting with a higher degree of difficulty compared with supervised learning. Unsupervised learning algorithms can be computationally expensive and time-consuming, especially with large or complex data sets. Accuracy isn’t guaranteed because you’re starting with unlabeled data.

Considerations for Reinforcement Learning

Reinforcement learning is a type of machine learning that focuses on training models to make decisions in environments where there's a clear goal or reward. This contrasts with other types of machine learning, which focus on predictions without a specific goal in mind.

One benefit of reinforcement learning is its ability to train models to make optimal decisions in complex environments. By receiving rewards or punishments for actions taken, the model learns which actions are optimal to take in different situations. Reinforcement learning can teach simpler tasks, such as playing games like tic-tac-toe, and complex tasks like driving a car or flying a plane.

Reinforcement learning can also train models to make decisions in real time. By continuously receiving feedback and adjusting behavior accordingly, the model can make decisions that are tailored to the current environment. This is important for situations where conditions are constantly changing.

However, reinforcement learning can be difficult to implement because it requires a lot of data to train on. The model also needs the freedom for trial and error, as it won’t know right away which actions lead to optimal outcomes. This can be a challenge in real-world environments where safety is a concern.

Another challenge of reinforcement learning is that in situations where the goal or reward is unclear or difficult to quantify. Reinforcement learning algorithms also require a lot of data to start with before they can converge on an optimal solution. This can be particularly challenging in situations where data is scarce or expensive to obtain.

Considerations for Semi-Supervised Learning

Semi-supervised learning is a powerful technique that combines elements of supervised and unsupervised learning. Semi-supervised learning improves accuracy and efficiency by leveraging labeled data where available and then using unsupervised techniques to identify patterns in unlabeled data.

One of the primary benefits of semi-supervised learning is its ability to improve accuracy by leveraging labeled and unlabeled data. Additionally, semi-supervised learning is often more efficient because it can leverage large amounts of unlabeled data to identify patterns and relationships.

Semi-supervised learning is challenging because it requires large amounts of data and a more complex algorithm that can incorporate labeled and unlabeled data..

How to Determine Which Machine Learning Services Are Right for You

When working with machine learning, the types of models vary, and every business must figure out for itself the right approach. Here are some of the key factors to consider.

Define Your Problems and Objectives

What specific business challenge do you want to solve? What are your objectives for using machine learning? Are you looking to improve customer satisfaction, increase sales, or optimize operational efficiency? Defining your problem and objectives will help you narrow down which machine learning algorithms are best suited to your business.

Consider the Size and Complexity of Your Data

The amount and complexity of your data will also impact your choice. For example, if you have a large and complex data set, you might turn to deep learning algorithms such as convolutional neural networks or recurrent neural networks. With a smaller data set, traditional machine learning algorithms such as decision trees or logistic regression may be more appropriate.

Evaluate the Algorithm

Accuracy is table stakes for machine learning algorithms, but it’s not the only consideration. You also need to evaluate the algorithm’s interpretability, or the ease with which humans can understand and explain the algorithm's decisions. Decision trees are more interpretable, for example, while deep neural networks are less so.

Get Support

Many solutions exist to support your efforts to use machine learning algorithms. Amazon Web Services (AWS) offers SageMaker, a managed service helping developers and data scientists to build, train and deploy machine learning models at scale.

Amazon SageMaker is designed to simplify the machine learning workflow without worrying about infrastructure and maintenance. However, using tools like SageMaker to their full potential requires significant time and resources. 

The tools you’ll need depend on the machine learning types you’re using. Some algorithms require significant computational resources, while others can be implemented on a standard desktop computer. You also need the right expertise to develop and maintain algorithms. If you don't have the in-house expertise, consider outsourcing the work to a certified AWS ML Services Competency partner, such as Mission Cloud. 

Learn how JibJab leveraged Mission Cloud’s AWS machine learning expertise to help launch a brand new product line.

FAQ

1. How can organizations effectively integrate semi-supervised learning into their existing machine learning workflows?

Organizations can integrate semi-supervised learning into their existing machine learning workflows by using small amounts of labeled data and a larger pool of unlabeled data. This approach is useful when obtaining labeled data, which is costly or time-consuming. It involves using labeled data to guide the model's learning process, which then applies learned insights to classify or predict outcomes in unlabeled data, thereby enhancing the overall efficiency of the data annotation process.

1. What are the specific challenges in implementing reinforcement learning in real-world business applications?

The challenges of implementing reinforcement learning in real-world business applications include the complexity of designing appropriate reward systems to guide agent behavior, the computational expense of training, and the difficulty of simulating a realistic environment for training the model. These factors can make it challenging to deploy reinforcement learning outside controlled experimental settings effectively.

1. Which machine learning type is most effective for predictive analytics in large-scale financial data?
2. Supervised learning is generally the most effective machine learning type for predictive analytics in large-scale financial data. It uses historical financial data to train models that predict future trends and outcomes, such as stock prices or credit risks, based on patterns recognized during the training phase. This approach is particularly valuable for its accuracy and ability to handle vast datasets with complex attributes.