What Is Machine Learning? (Uses and Benefits)

Before machine learning, computers were limited to following explicit directions and could not get better at tasks naturally over time. They can now be taught to scan data, identify patterns, make predictions, work autonomously and adjust how they do these things to improve continually. This can help improve employee productivity and 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 personalised 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, and how your business can benefit.

What we'll cover:

• What is machine learning?
• How does machine learning work?
• What’s the difference between machine learning, deep learning, and neural networks?
• Machine learning methods
• Real-world machine learning case studies from India
• What are the common machine learning algorithms
• Advantages and disadvantages of machine learning algorithms
• Challenges in machine learning implementation
• 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 technique within AI that lets machines learn continually from new results and information, becoming more intelligent and capable over time. This is possible through the development of models and using algorithms that help computers look for patterns in data, make predictions, refine these predictions and autonomously complete tasks.

Machine learning is designed to emulate the way humans learn and train. The process begins with providing information, interpreting it, checking to see if the interpretation is right, refining the interpretation, and putting the knowledge to use.

Machine learning can have varying elements to this process depending on how it is used, but these are the core steps:

1. Data collection: Data is the foundation of machine learning. Its model must be able to identify previous patterns so it can make predictions. This involves collecting high-quality data from a relevant set of sources, which could include 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 training data must be formatted into a usable form. It should be unified and organised so that AI can easily access and interpret it.
3. Choosing an algorithm and model: An algorithm runs on data to inform a model capable of tackling certain tasks. You narrow down the algorithm and model based on the business problem you're trying to solve.
4. Training: In this phase, the AI uses the algorithm to search the provided data 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 done by using data in which you know the target, but the model doesn't (known as "hold-out data") and then measuring performance against 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 guidelines to make it more accurate.
7. Predictions: In this process, you train, evaluate and tune multiple times until you are satisfied with the quality of the output. Now, the model is ready to make real predictions and reliable forecasts based on unknown data.
8. Deployment: You then integrate the machine learning AI into an environment where it can ingest data and make new predictions. This isn't the end of the process as the model can and likely should continue to be refined.

Both deep learning and machine learning 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, machine learning and neural networks are similar 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, labelled, processed and/or organised. By labelling 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 in unstructured or raw data using algorithms. All of this is achieved without the need for human involvement. The algorithms help you uncover patterns or data groupings.

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

This is a hybrid model in which training is done using a combination of supervised and unsupervised data. It does so by using a smaller labelled dataset 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.

When embedded into real-world systems, ML delivers measurable results across industries. Here’s how Machine learning helped an Indian company — Recykal — work smarter by learning from data and improving over time. Recykal is a Hyderabad-based startup, building Asia’s largest circular economy marketplace. It uses machine learning to optimise waste classification, routing, and compliance. By integrating Google’s ML tools, it automated how materials were tracked and matched with recyclers, helping divert over one million metric tons of waste from landfills. The platform supports more than 620 brands in meeting their sustainability goals and turning compliance into impact at scale. Through machine learning, Indian businesses can operate smarter, faster, and more sustainably.

As with model methods, there are quite a few machine learning algorithms you can choose from 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. For example, in sales it can predict 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 does or doesn't intend to purchase.

The AI searches for patterns in data to create groups. For example, in marketing, an analysis of 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 chooses to respond to a customer and when it chooses to escalate to a human representative or not.

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 analysing 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
• Personalised experiences for both employees and customers
• Continual improvement through ongoing training modification

Machine learning is powerful, but it’s not perfect. Some of its disadvantages include:

• Data dependency: Machine learning needs large volumes of high-quality data. Poor or biased data leads to poor outcomes.
• Lack of transparency: Many ML models operate as “black boxes,” making it hard to explain how decisions are made.
• Bias and fairness issues: If training data reflects existing biases, ML can reinforce or amplify them, leading to unfair results.
• High resource requirements: Building and deploying ML models often needs significant computational power, time, and specialised skills.
• Security and privacy risks: ML models can unintentionally leak sensitive information or be vulnerable to adversarial attacks.

In addition to this, machine learning comes with real implementation challenges, the biggest of which is data quality. ML models depend on large, well-labelled datasets, but in many organisations, data is siloed, inconsistent, or unstructured. Solving this means investing in data hygiene, building integrated systems, and setting clear standards for how data is collected and used.

Another major hurdle is the talent gap. Machine learning requires specialised skills in data science, model training, and deployment. Many businesses struggle to hire or retain this expertise. However, a business can overcome this by upskilling existing teams through training, or partnering with platforms like Trailhead that simplify ML development using pre-trained models and low-code tools.

Governance also forms a significant barrier. As ML adoption grows, so does the need for clear frameworks around model accountability, fairness, and compliance. Organisations must define who owns the model lifecycle, how results are monitored, and how risks are managed. Embedding governance early builds trust and keeps innovation aligned with business values.

Machine learning can help your businesses unlock the full potential of their data and power an AI strategy that benefits every part of the organisation and forges stronger connections with customers.

A key example is through AI agents, a subset of virtual agents that can 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 aren't merely chatbots. In fact, there is a significant difference between AI agents and chatbots — including their autonomous capabilities and the ability to be deployed in a wide range of areas.

When you combine AI agents and your CRM, you create a fleet of powerful assistants tailored to each department's needs. 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 to 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 is capable of constant iteration, you can improve strategic decision-making and drive more innovation and efficiency.

With 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 organisation. Find out how to get started with Agentforce.