5 types of machine learning you need to know

Machine learning (ML) technologies can enable decision-making in virtually every industry, including healthcare, human resources, and finance, and in a variety of use cases such as computer vision, large language models (LLMs), speech recognition, and self-driving cars.

However, the growing influence of ML is not without its complications. Validation and training datasets that underpin ML techniques are often aggregated by humans, and humans are prone to bias and error-prone. Even if the ML model itself is not biased or flawed, deploying it in the wrong context can lead to errors due to harmful unintended consequences.

That’s why diversifying your enterprise AI and ML usage can be critical to maintaining a competitive advantage. Each type and subtype of ML algorithm has unique benefits and features that teams can leverage for a variety of tasks. Here we describe the five main types and their applications.

What is machine learning?

ML is a subset of computer science, data science, and artificial intelligence (AI) that allows systems to learn and improve on data without additional programming intervention.

Rather than using explicit instructions to optimize performance, ML models use algorithms and statistical models that deploy tasks based on data patterns and inferences. In other words, ML leverages input data to predict output and continuously updates the output as new data becomes available.

For example, on retail websites, machine learning algorithms influence consumer purchasing decisions by providing recommendations based on purchase history. E-commerce platforms from many retailers, including IBM, Amazon, Google, Meta, and Netflix, use artificial neural networks (ANNs) to provide personalized recommendations. And retailers are often leveraging data from chatbots and virtual assistants, along with ML and natural language processing (NLP) technologies, to automate their users’ shopping experiences.

machine learning types

Machine learning algorithms fall into five broad categories: supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, and reinforcement learning.

1. Supervised machine learning

Supervised machine learning is a type of machine learning in which a model is trained on a labeled dataset (i.e., the goal or outcome variable is known). For example, if a data scientist is building a model to predict tornadoes, input variables might include date, location, temperature, wind flow patterns, etc., and the output would be the actual tornado activity recorded on that date.

Supervised learning is commonly used in risk assessment, image recognition, predictive analytics, and fraud detection and consists of several types of algorithms.

• regression algorithm—Predict output values ​​by identifying linear relationships between real or continuous values ​​(e.g. temperature, salary). Regression algorithms include linear regression, random forests, gradient boosting, and other subtypes.
• classification algorithm– Label input data to predict categorical output variables (e.g. “junk” or “not junk”). Classification algorithms include logistic regression, k-nearest neighbors, and support vector machines (SVM).
• Naive Bayes Classifier—Enables classification tasks on large data sets. It is also part of a family of generative learning algorithms that model the distribution of inputs for a specific class or/category. The Naïve Bayes algorithm includes decision trees that can accommodate both regression and classification algorithms.
• neural network—Simulates how the human brain works using numerous connected processing nodes that can facilitate processes such as natural language translation, image recognition, speech recognition, and image generation.
• Random Forest Algorithm– Combine results from multiple decision trees to predict values ​​or categories.

2. Unsupervised machine learning

Unsupervised learning algorithms such as Apriori, Gaussian Mixture Models (GMM), and Principal Component Analysis (PCA) draw inferences from unlabeled data sets, facilitating exploratory data analysis and supporting pattern recognition and predictive modeling.

The most common unsupervised learning method is cluster analysis, which uses clustering algorithms to classify data points based on value similarity (e.g. for customer segmentation or anomaly detection). Association algorithms enable data scientists to identify associations between data objects within large databases, facilitating data visualization and dimensionality reduction.

• K-Means Clustering– Assign data points to K groups. Here the data points closest to a given centroid are clustered under the same category and K represents the clusters based on size and level of granularity. K-means clustering is commonly used in market segmentation, document clustering, image segmentation, and image compression.
• Hierarchical clustering— Aggregative clustering, in which data points are initially separated into groups and then merged repeatedly based on similarity until one cluster remains, and partitioned clustering, in which single data clusters are split based on differences between data points. Describes a series of clustering techniques, including divided clustering. .
• stochastic clustering—Helps solve density estimation, or “soft” clustering problems, by grouping data points based on their likelihood of belonging to a particular distribution.

Unsupervised learning models are often behind “customers who bought this product also bought…” type recommender systems.

3. Self-directed machine learning

Self-supervised learning (SSL) allows models to train themselves based on unlabeled data rather than requiring large annotated or labeled datasets. SSL algorithms, also called predictive or freetext learning algorithms, automatically generate labels by learning one part of the input from another part, transforming an unsupervised problem into a supervised problem. These algorithms are particularly useful for tasks like computer vision and NLP, where the amount of labeled training data required to train a model can be very large (sometimes prohibitively large).

4. Reinforcement learning

Also called reinforcement learning Reinforcement Learning with Human Feedback (RLHF); It is a type of dynamic programming that uses a system of rewards and punishments to train an algorithm. To deploy reinforcement learning, an agent takes actions in a specific environment to achieve a predetermined goal. Agents are rewarded or penalized for their work based on an established metric (usually a score), which incentivizes agents to continue doing good work and deleting bad work. Through repetition, the agent learns the best strategy.

Reinforcement learning algorithms are common in video game development and are often used to teach robots how to replicate human tasks.

5. Semi-supervised learning

The fifth type of machine learning technique offers a combination of supervised and unsupervised learning.

Semi-supervised learning algorithms are trained on small labeled data sets and large unlabeled data sets, with the labeled data guiding the learning process on larger bodies of unlabeled data. A semi-supervised learning model can use unsupervised learning to identify data clusters and then use supervised learning to label the clusters.

Generative adversarial networks (GANs), a deep learning tool that trains two neural networks to generate unlabeled data, is an example of semi-supervised machine learning.

ML models of any type can yield data insights from enterprise data, but their vulnerability to human/data bias makes responsible AI practices a necessity for organizations.

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From developers to users to regulators, almost everyone engages with machine learning applications at some point, whether or not they interact directly with AI technology. And the adoption of ML technologies is accelerating. The global machine learning market was valued at $19 billion in 2022 and is expected to reach $188 billion by 2030 (CAGR of over 37%).

With the scale of ML adoption and increasing business impact, understanding AI and ML technologies has become an ongoing and critical commitment, requiring careful monitoring and timely adjustments as the technologies evolve. IBM® watsonx.ai™ AI Studio allows developers to easily manage ML algorithms and processes.

IBM watsonx.ai, part of the IBM watsonx™ AI and data platform, combines new generative AI capabilities with a next-generation enterprise studio to help AI builders train, validate, tune and deploy AI models with short data. part of time. Watsonx.ai provides teams with advanced data generation and classification capabilities that help businesses leverage data insights for optimal real-world AI performance.

In an era of data proliferation, AI and machine learning are as essential to everyday business operations as they are to technological innovation and business competition. But as a new pillar of modern society, this is also an opportunity to diversify enterprise IT infrastructure and develop technologies that work for the benefit of businesses and the people who depend on them.

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