What Are Machine Learning Concepts?

Machine learning concepts form the foundation of many modern data-driven technologies, empowering systems to learn from data and make predictions or decisions without explicit programming. This process is based on how humans learn new information and apply it. From predictive analytics to natural language processing, machine learning concepts are revolutionizing industries and reshaping our digital landscape.

Explore machine learning concepts like supervised, unsupervised, and reinforcement learning, what makes machine learning different from deep learning, and how machine learning contributes to a future driven by artificial intelligence (AI).

Machine learning concepts

Machine learning algorithms empower computers to classify data and make predictions based on the data they have encountered in the past. When presented with new data, the algorithm will compare that data with training data to make decisions and adjust how data is processed in the future. This is done by assigning each given data point a weight, which helps the computer decide which pieces of data are more important and relevant.

Experience is how humans learn. Although our brains are more complicated than any AI algorithm, humans learn by exploring the world and comparing new information to past experiences. For example, if you walk into a restaurant you’ve never visited, you can look at the menu and know what sounds good based on other foods you’ve had in the past. Machine learning tries to replicate this process. 

Machine learning encompasses various concepts and techniques, each serving different purposes in data analysis and pattern recognition. These concepts can be broadly categorized into supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning

Supervised learning is a form of machine learning that trains a model on labeled data. The algorithm learns how to process that data, mapping it between inputs (the data) and target outputs (the algorithm’s goal). 

Common supervised learning algorithms include: 

• Linear regression: A model that evaluates how numerical data relates on a linear plane, most commonly a timeline. For example, comparing how much something cost in the past to how much it costs now. 

• Neural network: A model that attempts to replicate human cognition by adding complex layers of nodes, enabling the machine to evaluate data in advanced ways. 

• Support vector machine: A model that classifies data to separate it into distinct categories. It is often used for tasks like sentiment analysis and spam filtering. 

• Decision tree/random forest: A model that uses a branching structure of questions to determine how to classify data. A random forest algorithm uses many decision trees to conclude based on the results of many decision trees. 

Applications of supervised learning

As supervised learning requires labeled training data, you can use it in situations where you have a clear idea of how you want the algorithm to interact with the data. A few supervised learning applications include predictive analytics, sentiment analysis, and object or image recognition. 

• Predictive analytics: Predictive analytics is a form of analysis that predicts what could happen in the future. Companies and organizations can use supervised learning to predict future performance. For example, a company could use past sales data and financial information to predict next year’s cash flow.

• Sentiment analysis: Machine learning can consider a large amount of text and provide insight into the sentiments contained within the data. For example, companies can use sentiment analysis to analyze how people discuss the company’s brand or products on social media, offering insights that could be used for marketing, product design, and more. 

• Object or image recognition: Supervised learning powers object and image recognition. These algorithms can detect anomalies and patterns by classifying objects into categories or spotting visual cues. In the example of self-driving cars, this could include pedestrians. 

Unsupervised learning

Unsupervised learning uses a process similar to supervised learning. However, the algorithm is trained on unlabeled data instead of labeled data. The algorithm independently identifies patterns and structures, creating its own labels and interacting with data without explicit instructions. 

In addition to models like neural networks, common unsupervised learning algorithms include: 

• Principal component analysis: A model that allows a machine to simplify input data without losing the most relevant parts of the information.

• K-means clustering: A model that sorts unlabeled data into smaller groups. It is commonly used for exploring how data points relate to one another and finding patterns in an open-ended way that might not be immediately obvious to human researchers. 

• Probabilistic clustering: A model that creates clusters similar to K-means but observes how the data and clusters exist in the space compared to one another. 

You may also encounter a type of supervised learning that combines supervised and unsupervised techniques. This is called semi-supervised learning. This category of models uses a subset of labeled categories and data to guide the algorithm as it works with a larger, unlabeled set of data.

Applications of unsupervised learning

Unsupervised learning is helpful when you aren’t sure what the algorithm will find in the data. For example, you can use unsupervised learning to detect anomalies in data, segment your customer or audience base, and power recommendation engines. 

• Detecting anomalies: Unsupervised learning algorithms can help you detect anomalies in your data, or data points that don’t react the way you’d expect. 

• Customer segmentation: While supervised learning can help you segment your customers into predictable categories like age, region, and interests, unsupervised learning can help you learn new insights about your customers that may not be obvious. You can create customer personas to inform engagement efforts, brand messaging, and product development. 

• Recommendation systems: A recommendation system analyzes how you interact with a product and suggests other products you might like based on those results. For example, you may use a streaming service that analyzes your viewing or listening history and recommends similar media based on your likes and dislikes. Companies can also use this technology to offer products to customers that complement products they already have or are popular with similar customers. 

Reinforcement learning

Reinforcement learning is a type of unsupervised learning that enables a machine to learn through trial and error to find the best solution to a problem. Reinforcement learning uses a gamified reward system to help a computer determine which method works best. This methodology is then reinforced through a series of successful outcomes. Reinforcement learning algorithms learn through making decisions.

Applications of reinforcement learning

You can use reinforcement learning when unsure of the best strategy for accomplishing a task. Some examples include market personalization, optimization, and financial predictions. 

• Marketing personalization: Companies can personalize their marketing efforts using an algorithm similar to a recommendation engine that improves over time by learning and adjusting how individuals engage with the brand. 

• Optimization: When you want to find the optimal way to allocate resources or to complete a task efficiently, reinforcement learning can help you by using a trial-and-error approach to determine what approach will most efficiently accomplish your goals.

• Financial predictions: While you can use supervised learning to predict future financial performance, reinforcement learning can go further by making decisions based on a strategy's previous success or failure in a given market.

Machine learning vs. deep learning

Machine learning and deep learning both describe types of AI. Machine learning refers to all the types of learning that a robot or computer can use, such as supervised, unsupervised, and reinforcement learning. Deep learning is a type of machine learning, specifically a neural network that uses many layers of neural networks to complete more complicated analyses. Deep learning algorithms are sometimes called deep neural networks. 

Learn more about machine learning concepts on Coursera

Machine learning has applications in all industries, from financial projections to resource planning to strategic decision-making. If you want to learn more about machine learning concepts, consider the IBM Machine Learning Professional Certificate, the Deep Learning Specialization from Deep Learning.AI, or the Mathematics for Machine Learning Specialization offered by the Imperial College of London.