What Is Machine Learning? (Uses and Benefits)

Before machine learning, computers were limited to following explicit directions and could not naturally get better at tasks over time. They can now be taught to scan data, identify patterns, make predictions, work autonomously, and adjust how they do all of these things to continually improve — all of which can help employees be more productive and to serve customers better.

Developments in machine learning, a key area of artificial intelligence (AI), have unlocked tremendous opportunities for businesses. Tasks such as data analysis, predictive analytics, lead scoring, and personalized recommendations are now much easier and more efficient thanks to AI agents that work together with humans.

Let's explore what machine learning is and how it works. We’ll also look at the ways it's related to AI approaches like deep learning, advantages and disadvantages are, and how your business can benefit.

What we'll cover:

• What is machine learning?
• How does machine learning work?
• Machine learning vs. deep learning vs. neural networks: What’s the difference?
• Machine learning methods
• Common machine learning algorithms
• Advantages and disadvantages of machine learning algorithms
• How AI agents use machine learning
• Get hands-on with machine learning with free courses
• Use the power of machine learning for your business

Machine learning (ML) is a subset of AI that lets machines continually learn from new results and information, becoming more intelligent and capable over time. This is made possible through the development of models and use of algorithms that enable computers to look for patterns in data, make predictions, refine these predictions, and autonomously complete tasks.

Machine learning is meant to emulate the way humans are taught and trained. The process begins with providing information, then interpreting it, checking to see if the interpretation is right, refining the interpretation, and putting the knowledge to use.

Depending on how it's being used, machine learning can have varying elements to this process, but these are the core steps:

1. Data collection: Data is the foundation of machine learning. The model must be able to identify past patterns so it can make predictions. The approach involves collecting high-quality data from a relevant set of sources, which could include almost any kind of content: structured (content you would already have stored in a customer relationship management (CRM) system, or unstructured (data pulled from emails, customer chat logs, prospect profiles, financial spreadsheets, etc.).
2. Data preprocessing: The data collected for training must be put into a usable form. The gathered information is unified and organized so that AI can easily access and interpret it.
3. Choosing an algorithm and model: With machine learning, an algorithm is run on data to inform a model capable of tackling certain tasks (different types of ML algorithms and models are discussed later). You narrow down which algorithm and model to use based on the business problem you're trying to solve.
4. Training: In this phase of machine learning, the AI uses the algorithm to search the provided information for patterns. It looks for inputs linked to specific results (called "targets") — for example, the actions taken when a prospective customer receives an email. Once it finds patterns, the AI creates a model that can make predictions.
5. Evaluation: Your team reviews the output to determine how accurate or likely the model's predictions are. This is often done by using data where you know the target but the model doesn't (known as "hold-out data") and then measuring performance with a range of metrics.
6. Tuning the model: Once you understand the machine learning model's weaknesses, you can adjust the data, the algorithm, or other machine learning guidelines to make it more accurate.
7. Predictions: Training, evaluation, and tuning are an iterative part of the process that you'll run and repeat until you're satisfied with the quality of the output. At that point, the model is ready to make real predictions — reliable forecasts based on unknown data.
8. Deployment: Integrate the machine learning AI into an environment where it can ingest data and make new predictions. Note that deployment isn't the end of the process as the model can and likely should continue to be refined.

When comparing deep learning vs. machine learning, it’s critical to understand that both fall under the general umbrella of artificial intelligence. Deep learning is a subset of machine learning that is inspired by how the human brain works.

Similarly, there isn’t a difference between machine learning vs. neural networks since they are connected. Deep learning relies on artificial neural networks (ANNs) — layers of interconnected nodes (called neurons) that work together to extract hierarchical representations of data, which feed machine learning.

Deep learning differs from traditional machine learning in that it can operate autonomously on unstructured data, reducing the need for human intervention. However, machine learning models can often be trained on smaller datasets and scaled with fewer computational resources than deep learning models.

Just as people learn in different ways, so do computers. You can approach machine learning using several methods, including:

The model learns from a sizable set of supervised data that is specific, labeled, processed, and/or organized. By labeling the information it's trained on, it learns the relationships between the data it takes in versus its output. It makes predictions based on the data it's trained on, which can be compared with test data.

Rather than being trained on a large amount of preset data, this model is set up to detect patterns among unstructured or raw data using algorithms. All of this is achieved without the need for human involvement. The algorithms help them uncover patterns or data groupings.

Dimensionality reduction is a part of unsupervised learning. In this method, the number of features in a dataset is referred to as its dimensions. This model autonomously reduces how many there are. The goal is to lower the number of variables to improve accuracy and decrease the resources needed to run the model.

In this hybrid model of machine learning, the model is trained using a combination of supervised and unsupervised data. It does so by using a smaller labeled data set to guide it while also pulling from a larger unlabeled set, applying learnings from both.

The model is not given training data and instead learns to achieve accuracy or success through a process of rewards and punishments — or reinforcement.

As with model methods, there are quite a few machine learning algorithms you can choose for your machine learning AI. Some of the algorithms used most often include:

A formula computes the relationship between variables and unknowns to arrive at a value. An example in sales would be predicting a lead score based on historical data.

The algorithm uses probability to predict whether something is or isn't part of a specific class. For example, it could be used in commerce websites to predict whether a visitor likely does or doesn't intend to purchase.

The AI searches for patterns in data to create groups. An example related to marketing is analyzing audience data to predict which subsets are more likely to respond to a certain type of messaging.

AI's predictions and data classifications are the result of following a sequence of choices made. This flow can be easily traced linearly, enabling your business to see the logic behind the decisions. This is helpful in reviewing service interactions to see how an AI agent chose to respond to a customer and when it did or did not choose to escalate to a human representative.

With this, the outputs of multiple decision trees are merged. Each tree is given a random subset of the overall data and the prediction is made based on the aggregated results.

These are used for deep learning, replicating the ways the human brain makes connections, and are especially useful for tasks that involve analyzing complex datasets, such as speech recognition. They are at the heart of the large language models (LLMs) that underlie generative AI tools.

Like all technology, AI has advantages and disadvantages. The pros and cons apply to machine learning as well as specific algorithms and models.

Some of the key advantages of machine learning include:

• Powerful data analysis by identifying patterns and trends
• Predictions grounded in a company's accumulated data
• Personalized experiences for both employees and customers
• Continual improvement through ongoing training modification

Some of the challenges of machine learning include the need for:

• Large training datasets so that ML models are accurate
• Computational resources to power the learning
• Expert staff, such as data scientists and machine learning engineers
• The need to review and correct biases or hallucinations which may occur

You can overcome these issues by using a platform that integrates your data with machine learning and makes it easy to deploy AI-powered solutions such as autonomous agents. Having an integrated system gives you seamless data access and reduces the need for additional resources and outside input.

Machine learning can help your business unlock the full potential of its data, powering an AI strategy that benefits every part of the organization and forges stronger connections with customers.

A key example is through AI agents, a subset of virtual agents that are able to act autonomously. AI agents use machine learning, natural language processing, and conversational AI to draw on your data, identify patterns, make decisions, and provide answers. They're not merely chatbots. In fact, there are major differences between AI agents and chatbots — including their autonomous capabilities and ability to be deployed in a wide range of areas.

When you combine AI agents and your CRM, you can create a fleet of powerful assistants tailored to each department's needs and powered by your data. For example, service AI can answer questions based on your knowledge base, sales AI can predict quarterly targets, commerce AI can act as a personal shopper, and marketing AI can generate campaign briefs.

Although machine learning may sound daunting, it's often straightforward once you know the fundamentals and try it out for yourself. Trailhead , Salesforce's free online learning platform, has several courses to help you get started with some hands-on exposure, including these:

• Explore Machine Learning Predictions : Learn more about what machine learning can do and how to use ML predictions
• Artificial Intelligence Fundamentals : Gain an understanding of the basics of artificial intelligence and how to get started
• Discover AI Techniques and Applications: Dive into the different types of AI technologies and machine learning approaches

By combining your data with artificial intelligence that — thanks to machine learning — is capable of constant iteration, you can improve strategic decision-making and drive more innovation and efficiency.

Agentforce — the agentic layer of the Salesforce platform — you can easily and cost-effectively make the most of machine learning by deploying powerful AI agents across your organization. Find out how to get started with Agentforce.