Article • 11 min read

Deep learning vs. machine learning: A complete guide

Uncover the difference between machine learning and deep learning to understand how they impact human-like AI.

By Patrick Grieve, Contributing Writer

Last updated March 14, 2025

An agent learning the difference between deep learning vs. machine learning.

What is the difference between deep learning and machine learning?

Deep learning (DL) is an evolution of machine learning (ML). Both are algorithms that use data to learn, but the key difference is how they process and learn from it. For example:

Machine learning models need human intervention to learn from behaviors and data.
Deep learning models use neural networks to adjust behaviors and make predictions.

In fact, a deep learning model is able to learn through its own method of computing—a technique that makes it seem like it has its own brain.

Machine learning and deep learning push the boundaries of innovation by powering tools with artificial intelligence (AI). From fully autonomous AI agents and self-driving cars to curated user feeds and personalized Netflix recommendations, ML and DL are everywhere.

As machine learning and deep learning surge, consumers are taking notice. According to our Zendesk CX Trends Report 2025, 70 percent of customers see a clear gap between companies leveraging AI effectively and those not. And while ML and DL work together, it’s important to understand the differences between deep learning vs. machine learning to meet customer needs and expectations.

In our guide, you’ll uncover why ML and DL dominate conversations about AI and successful examples of each.

Deep learning vs. machine learning vs. artificial intelligence

The key differences between deep learning vs. machine learning.

DL and ML are AI algorithms that simulate human intelligence. Along with AI, these technologies power AI-driven tools and programs. Below, we cover the differences between deep learning vs. machine learning vs. artificial intelligence.

Artificial intelligence vs. machine learning vs. deep learning
	AI	ML	DL
Use	A broad spectrum of intelligence simulation	Specific data-driven tasks and predictions	Specialized tasks requiring pattern recognition
Human intervention	Varies from fully autonomous to significant oversight	Requires regular engineering and model adjustment	Minimal once trained
Data	Can require multiple data types and sources	Can perform with moderate-sized datasets	Requires a massive dataset
Training	Varies by system	Standard computing training	Time-intensive and extensive
Engineering	Requires broad knowledge across multiple domains	Statistical knowledge and established algorithms	Neural network architecture

What is artificial intelligence?

AI refers to machines mimicking human intelligence and interactions. This broad field encompasses deep learning and machine learning, and its goal is to develop intelligent tools that can carry out cognitive functions such as problem-solving, customer sentiment analysis, and decision-making.

From conversational to generative AI, there are various use cases for AI across many industries. For example:

AI in customer service: Customer service teams using AI can automate workflows, enhance knowledge management, and proactively guide agents.
AI in sales: Sales teams using AI can predict future sales trends, improve lead generation, and onboard new customers.
AI in healthcare: Healthcare teams using AI can provide 24/7 patient support, assist with billing inquiries, and schedule appointments.
AI in retail: Retail teams using AI can personalize shopping experiences, increase self-service, and process order updates.

Further, this is not an exhaustive list as AI is also used in finance, e-commerce, hospitality, human resources, transportation, and beyond.

An example of AI:

Mimicking human-like intelligence, AI copilot empowers agents to deliver fast, personalized service. This AI-powered tool is ideal for working behind the scenes to help agents analyze customer intent and automate tasks while offering intelligent recommendations and providing real-time insights.

What is machine learning?

The machine learning process, from receiving data to delivering an answer.

Machine learning is a branch of AI trained on statistical models and algorithms, which enable it to make predictions and decisions. By identifying patterns in its training data, ML algorithms can improve and adapt over time, enriching its capabilities.

For machine learning to be accurate, it relies on human engineers to feed it relevant, pre-processed data. With human assistance, ML is adept at solving complex problems and generating important insights by identifying patterns in data. Once a team trains and optimizes the performance of an ML model, it will follow a standard process:

Receive new information via a user query.
Analyze the data.
Find a pattern.
Make a prediction.
Send an answer back to the user.

This process is repeated for each query, and the primary difference between machine learning models is how you train them. Moreover, there are three common types of learning of ML models:

Supervised learning: A machine learning algorithm is given labeled training data and learns a model for responding to data.
Unsupervised learning: AI tools are fed unlabeled data and identify patterns without human intervention.
Reinforcement learning: ML models are fed training data and use trial-and-error and feedback to learn.

The best type of learning depends on each user’s needs and expectations, especially if an ML model is expected to support intelligent automation.

An example of machine learning:

Music and video streaming services—like Spotify, Apple Music, and YouTube—use machine learning and reinforcement learning to power on-demand recommendations. ML algorithms associate the listener’s preferences (e.g., saved songs, playlists, followed creators, and favorite videos) with artists, albums, creators, and others with similar tastes.

What is deep learning?

Deep learning is a subset of machine learning that uses an artificial neural network to autonomously learn, make intelligent decisions, and determine prediction accuracy without human intervention. Because deep learning models analyze data continuously, they build extensive knowledge over time and draw conclusions by taking in information, consulting data reserves, and determining an answer.

Deep learning applications use a layered structure of algorithms—AKA neural networks—to intake unlabeled data (the input), identify patterns, and develop a final response (the output). There are various types of neural networks, including:

Feedforward neural networks (FF or FFNN): An artificial network where information flows in one direction—from input nodes to output nodes. This is the simplest form of deep learning and does not include cycles or loops.
Recurrent neural networks (RNNs): AI algorithms that use built-in feedback loops to “remember” past data points. RNNs can use this memory of past events to inform their understanding of current events or even predict the future.
Long/short-term memory (LSTM): A specialized recurrent neural network designed to learn and remember long-term dependencies in sequential data. These networks are particularly effective for tasks where context from earlier inputs is crucial.
Convolutional neural networks (CNNs): Algorithms that often power computer vision and image recognition. These networks filter visual prompts by assessing patterns, textures, shapes, colors, and other components.
Generative adversarial networks (GAN): A deep learning architecture consisting of two networks—a generator creating synthetic data and a discriminator distinguishing real and fake data.
Multilayer perceptrons (MLPs): Feedforward neural networks composed of multiple layers of perceptrons, making them capable of learning complex patterns and solving non-linear problems across various domains.

Because of these neural networks, deep learning tends to be more advanced than standard machine learning models.

An example of deep learning:

Zendesk AI agents use deep learning to automatically categorize support tickets, predict customer sentiment, and resolve customer inquiries. For these autonomous bots, customer messages are the input, the message’s urgency and complexity are analyzed within the neural network, and the ticket’s route and predicted resolution time are the output.

Frequently asked questions

What is a neural network?

What are the similarities between deep learning vs. machine learning?

What is an example of machine learning that is not deep learning?

A classic example of machine learning that is not deep learning is a random algorithm used for credit risk assessment in banking. By analyzing historical financial data, such as income, credit score, employment history, and previous loan performance, this ML algorithm can use a predictive model to forecast the likelihood of a loan applicant defaulting. This would require feature engineering (and human intervention) and simple computational models.

Is CNN deep learning or machine learning?

How do machine learning and deep learning impact customer service?

Deep learning and machine learning in customer service are impactful in various ways. A few key examples of this include:

Agent assistance: AI-powered tools like AI copilot natural language processing (NLP) and continuous learning make it possible for AI to streamline customer support and empower agents with insights.
Customer service chatbots: Conversational bots can use ML and DL to personalize responses, collect customer data, and answer customer questions without human involvement.
Workflow automation: ML and DL can optimize workflows by intelligently routing requests to the right agent and automatically suggesting pre-written responses to customer questions.
Predictive analytics: Using historical data and machine learning capabilities, predictive analytics helps teams forecast what will happen in the future to help teams anticipate customer issues.
Fraud detection: Deep learning and machine learning can help support teams by proactively flagging security issues such as an unsafe password or a suspicious login.

All of these tools are beneficial to customer service teams and can positively impact agent performance.

Customer story

Catapult Sports

Catapult offers elite CX performance with Zendesk AI

“Zendesk Advanced AI has provided us with tools that save agents time, make their job easier, and empower them to make really good decisions day to day. Because of that, I have a team that really enjoys coming to work.”

Thomas Powers

Vice President of Product Support

Read customer story

Deliver seamless CX with intelligent AI

According to Zendesk Benchmark Data, 71 percent of customers believe AI improves the quality of service they receive—and they expect to see more AI in customer service in the future. The difference between deep learning and machine learning matters as their combined uses will continue to drive time, resource, and cost savings.

Fortunately, Zendesk AI is a powerhouse AI solution with a low barrier to entry. Our AI solution can handle even the most complex customer interactions by prioritizing AI in CX and using billions of customer service data points as the foundation of its training. Don’t wait: Fill your knowledge gaps and help agents reach new productivity levels with our AI-powered solution today.