Deep Learning vs. Machine Learning: What’s the Difference?

If you’re involved in IT—and even if you aren’t—you’ve probably come across terms like “deep learning,” “machine learning,” or “AI” at some point in the last few years. But what do those buzzwords really mean?

Artificial intelligence (AI) involves training computer systems to learn and act based on the knowledge they gain from data. As a result, these systems are able to understand more about their environment and perform complex tasks that typically require human intelligence. Machine learning (ML) and deep learning are types of AI technology used by organizations around the world for software applications, programming, data analytics, automation, and more. These kinds of AI enable computing machines and programs to learn from structured and unstructured data sets and improve over time.

AI technology continues to grow and develop at a rapid pace. Whether you’re using voice assistants, navigating with a GPS, or receiving personalized recommendations online, AI is likely playing a role behind the scenes. This blog examines the differences between machine learning and deep learning and how they’re used in the IT world today. Read on to learn more.

Machine learning is a type of AI that enables computers to learn from data and make decisions without being explicitly programmed for every task. Through the use of algorithms, machines can identify trends, make predictions, and even improve outcomes as more data is collected.

Machine learning models rely on input data—user behavior, browsing history, transaction records, and more—to learn and make adjustments. For example, a streaming service might use ML to recommend shows or movies based on a user’s viewing history. However, machine learning still requires human oversight. Programmers are needed to fine-tune algorithms, correct mistakes, and improve the model’s accuracy over time.

So why use machine learning? ML provides many benefits for individuals and organizations, as it:

• Automates data processing and pattern recognition.
• Improves business strategy and decision-making.
• Enhances customer and user experiences.
• Enables predictive analytics.

While machine learning has its strengths, it’s also not perfect. ML usually takes time, computing power, and high-quality data to function properly. ML models are prone to producing errors without adequate human intervention (or supervised learning). Depending on how much it’s needed, ML implementation can also be highly resource-intensive for the organizations looking to adopt it. Overall, machine learning is a great option for organizations as they work to incorporate data to optimize their products or services.

Real-world examples of machine learning are becoming more and more apparent across industries. Below are some of the most common machine learning use cases today:

• Smart assistants. Devices like Siri, Alexa, and Google Assistant are capable of speech recognition and learn user routines to improve their responses over time. 
• Social media. Platforms like Facebook and X tailor users’ feeds based on their interactions and engagement. 
• Streaming and music apps. Spotify and Apple Music recommend new content based on a user’s listening history and genre preferences. 

Deep learning is an advanced subset of machine learning. It uses complex structures called deep neural networks, which are modeled after the human brain, to analyze data and make decisions.

Unlike traditional machine learning, deep learning models can interpret large volumes of data with minimal human intervention. This makes it especially useful for tasks involving speech recognition, image analysis, or real-time decision-making.

Deep learning models often involve dozens—or even hundreds—of hidden layers in a neural network. These interconnected layers (or neurons) allow the system to quickly parse and detect intricate patterns and make highly accurate predictions. For instance, a deep learning system might be used in a self-driving car to identify road signs, recognize pedestrians, and make split-second decisions—all without human input.

Prominent examples of deep learning at work include the following:

• Language translation tools. Services like Google Translate rely on deep learning for more accurate grammar and context detection.
• Image recognition and colorization. Deep learning tools can identify and add realistic color to black-and-white photos by absorbing information from large datasets of colored images. Convolutional neural networks (CNNs) excel at image recognition.
• Autonomous vehicles. Self-driving cars use deep learning to interpret complex environments and navigate safely.

Large language models are deep learning AI systems that are trained to comprehend and generate human language. LLMs are built on enormous datasets with millions of parameters that govern their behavior and output. This data is typically compiled from books, magazines, online articles, websites, and other texts. As LLMs are used, they can learn patterns from the way people use language and form adaptive, human-like responses to queries.

LLMs can perform a variety of helpful tasks, such as answering questions, writing reports and emails, generating code, summarizing information, and clarifying complex concepts.

Examples of notable LLMs include:

• ChatGPT. Developed by OpenAI, ChatGPT provides advanced reasoning and conversational abilities for a wide array of topics.
• Gemini. Google’s Gemini is often used for high-level reasoning and interpreting images, audio, and video in addition to text.
• Claude. Anthropic’s LLM family, Claude, is especially useful for long-context understanding, long-form writing, and complex reasoning tasks.
• Grok. This xAI-created chatbot is known for the more casual and humorous tone present in its generated content.
• Llama. Llama is Meta AI’s family of LLMs that relies on multimodal models to handle text and visual information.
• Perplexity. Perplexity is an LLM-enhanced search engine that offers search result summaries and citations for users.

As artificial intelligence continues to evolve, so does the demand for IT professionals who understand how to build, manage, and optimize AI systems. Organizations across healthcare, business, finance, education, and the government are investing heavily in AI-powered tools and applications. And while automation may change certain job functions, it also opens doors to new career paths for professionals with the right skills.

For individuals hoping to work in an AI-specialized role, there are abundant career paths to explore, including:

• Machine Learning Engineer
• Data Scientist or Analytics Expert
• Business Intelligence Developer
• AI/ML Designer or Researcher

These roles require robust technical skills, including programming, structured data analysis, and a foundational understanding of how AI systems operate. Many professionals begin by earning a degree in information technology, data science, or computer science and by building their expertise through certifications and hands-on experience.

Ready to jump into the exciting realm of artificial intelligence? Start by earning an accredited IT degree from the right institution. At WGU, our career-focused IT degree programs are designed to prepare you for the evolving demands of the tech industry. Whether you're interested in AI, cybersecurity, software development, or data analytics, WGU offers:

• Flexible, online learning tailored to your schedule.
• Industry-recognized certifications built into your degree.
• Practical experience through project-based learning.
• Supportive faculty and mentorship every step of the way.

Plus, our competency-based learning model means that you advance through your coursework as quickly as you master the material, potentially saving you time and money.