Machine Learning vs. AI — What’s the Difference?

“The most important skill in AI and machine learning is knowing about all of the classical machine learning and AI algorithms that came before” – Frank Kane (CEO of Sundog Education, Former Sr. Manager at Amazon)

AI and Machine Learning are similar, but they aren’t the same thing. Artificial intelligence is a field of computer science that focuses on developing systems capable of performing complex tasks at a level comparable to or even surpassing human abilities. This enables technologies like ChatGPT to process information and generate responses efficiently.

Machine learning (ML), a subset of AI, is centered on training computers to recognize patterns and analyze vast amounts of data to improve performance over time. Rather than directly imitating human thought processes, ML leverages statistical methods to refine decision-making and predictions.

While AI and ML have made significant advancements, misconceptions about their capabilities and limitations persist. Common misconceptions include:

• AI and ML are science-fiction technologies. They’re both very real and together, they’re transforming the lives of people around the world.
• AI and ML precisely mimic human intelligence. AI can use Machine Learning to perform highly specialized tasks, but it’s incapable of matching the full range of human cognitive abilities that people possess. It can’t feel happiness, sadness, and other emotions that make the human experience unique. 
• AI and ML are taking jobs from people. Businesses are increasingly using artificial intelligence and machine learning to automate tasks that employees once performed. At the same time, they’re creating new career opportunities for people who master AI and ML. 

Artificial intelligence and machine learning help people solve problems faster and more efficiently than ever before, and those who understand these technologies are in demand. 

Read on for more information about these technologies and how they can help you grow your career. 

What Is Artificial Intelligence (AI)?

Artificial intelligence is a technology that can analyze, learn and reason to identify and achieve the best possible outcomes. To understand this, it’s important to consider how AI is used. 

Computers equipped with AI generate insights from massive amounts of data and leverage that information to make decisions and solve problems quickly and efficiently.  

How artificial intelligence works depends on the system. Machine learning is one of the techniques used to fuel a full AI system, but it is not the only one, for example rule-based or expert systems have been explored in the past. What is more exciting for AI systems is the Generative AI models able to process unstructured data like text and images, follow instructions in the form of prompts and produce outcomes. 

All of the exciting progress in recent years, first in Deep Learning and neural networks and later in Generative AI, has made AI systems more capable than ever before. AI powered by neural networks is inspired by how the human brain works—processing data in layers, identifying patterns, and solving problems. This allows it to understand raw, unstructured data more effectively than ever before.

With recent LLMs able to emulate multi-step reasoning capabilities, AI systems are now able to plan, reason and solve novel problems they initially were not trained to solve, moving their capabilities beyond classical machine learning systems. 

You may use artificial intelligence every day without realizing it. Let’s take a look at some examples of AI applications.

Voice Assistants

Siri, Alexa, and other voice assistants use artificial intelligence to recognize speech patterns and detect natural language. When you ask a voice assistant a question, it detects what you’re saying and responds appropriately within seconds. 

AI-Powered Chatbots

Many businesses use AI-powered chatbots, which you may have seen on various websites and mobile apps. Chatbots help companies automate and optimize their customer service. Type a question or command into a chatbot, and you’ll get an instant response.  

Autonomous Vehicles

If you own a Tesla, you have an AI-powered car. Teslas and other autonomous vehicles use artificial intelligence to detect road hazards and alert drivers if they’re drifting out of a lane. In doing so, they help make the roads safer for everyone. 

Smart Robotics

Let’s not forget about the use of AI in smart robotics. With artificial intelligence and machine learning, robots learn things in real time and adapt to changing environments. As such, these robots are incredibly valuable in certain industries, including automotive and manufacturing. 

What Is Machine Learning?

Machine learning doesn’t exist without artificial intelligence. ML is a branch of artificial intelligence that enables computers to identify patterns and improve task performance by analyzing large amounts of data without being explicitly programmed.

Artificial intelligence powers the creation of machine learning models. A typical ML model consists of three parts:

1. Decision Process: A machine learning algorithm predicts or classifies labeled or unlabeled data to get an estimate. 
2. Error Function: This function compares the model’s estimate against known examples to verify its accuracy. 
3. Model Optimization Process: The model accounts for differences between its estimate and known examples, and updates how it evaluates data accordingly. This helps make future projections more accurate.

Not all machine learning is created equal. Common types of ML include:

• Supervised: Labeled datasets teach algorithms to detect patterns and predict outcomes. This results in models that view relationships through the spectrum of inputs and outputs. 
• Unsupervised: Unlabeled datasets teach algorithms to detect patterns and predict outcomes. The algorithm groups data based on similarities, differences, and patterns. 
• Reinforcement: This involves a trial-and-error approach to teach algorithms how to make decisions. Algorithms are rewarded or penalized based on their actions within an environment and how accurately they predict future outcomes.

Machine learning models know how to improve their performance continuously and don’t require additional programming. Let’s take a look at some common machine-learning applications.

Streaming Services

Do you know how Netflix recommends movies that suit you perfectly? Or how Spotify introduces you to bands and musicians you’ve never heard of before but now they’re some of your favorites? Netflix, Spotify, and other streaming services use ML to learn about you and your preferences and provide personalized content recommendations that match your expectations. 

Fraud Detection in Finance

Banks and credit unions use machine learning for fraud detection. ML tracks your spending habits — if a suspicious purchase shows up in your bank account, the financial institution will likely alert you. That way, you can respond to potential fraudulent activities on your account right away. 

Healthcare

Doctors, other healthcare providers, and their patients benefit from machine learning. The technology helps medical personnel make data-driven diagnoses, improving patient care and minimizing the risk of errors. It also helps medical professionals accurately predict how patients will respond to lifestyle changes and treatments. 

Now that you’re up to speed on artificial intelligence and machine learning fundamentals, let’s explore their differences. 

AI vs. ML — Key Differences

AI involves all intelligent machines, including rule-based and learning systems. ML is a part of these machines and systems and primarily focuses on pattern recognition and learning from the data it’s given.

ML systems rely on its examples to solve a problem or a model designed by a machine learning engineer for a specific problem. Artificial intelligence on the other hand, can go beyond that by utilizing information encoded in the form of logic, rules or more recently instructions provided by the user in the form of prompts. This empowers AI systems to solve problems beyond their dataset or initial design, making them more general problem solvers.

It pays to know the ins and outs of AI and ML, especially if you want to take the next step forward in your career or start a new one. 

Careers in AI and Machine Learning

The demand for people with skills and experience in artificial intelligence and machine learning is growing. Some of the most in-demand AI and ML roles include:

• AI/ML engineer: Builds and optimizes AI and ML models
• Data scientist: Uses ML for predictive analytics and producing insights.
• Software developer: Implements AI-based applications.
• Business analyst: Utilizes ML to uncover insights and make strategic decisions
• Cybersecurity analyst: Leverages AI to help businesses and consumers fight malware, ransomware, and other cyber threats. 

As artificial intelligence and machine learning evolve, the demand for individuals with advanced AI and ML knowledge will likely increase. The sooner you master the fundamentals of AI and ML, the sooner you can lay the groundwork for a rewarding career. 

Online courses that teach you about artificial intelligence and machine learning will help you launch a new career or take your current one to the next level. You’ll gain the skills you need to capitalize on career opportunities in AI and ML. 

AI vs ML: Career Skills

The skill sets required for AI and ML differ, though they share some overlap. Both fields demand strong knowledge of mathematics, statistics, and programming, but the focus varies.

Artificial Intelligence (AI) Career Skills

To excel in AI, you need strong skills in problem-solving, programming, and designing algorithms. AI engineers often work with complex systems that may not learn or adapt but instead rely on pre-defined rules to function. Key skills include:

• Programming: Python, Java, or C++ for building AI models
• Mathematics & Statistics: Linear algebra, calculus, probability, and statistical methods
• Algorithms: Designing algorithms for AI models, rule-based systems, and AI problem-solving
• Neural Networks: Knowledge of creating networks inspired by human cognition.
• Prompt Engineering: Crafting effective inputs to guide large language models in generating accurate, relevant, and safe outputs
• LLMs: Understanding how large language models are trained, fine-tuned, and applied to real-world tasks like summarization, translation, and code generation

Machine Learning (ML) Career Skills

ML specialists focus on building systems that can learn from data and improve over time. These skills require knowledge of data processing, algorithms for prediction, and advanced techniques for optimizing models. Key skills include:

• Data Analysis & Processing: Expertise in data cleaning, data manipulation, and data visualization
• Algorithms & Models: Understanding machine learning models like regression, decision trees, and clustering algorithms
• Programming: Proficiency in Python, R, or similar languages and libraries like TensorFlow, Keras, or PyTorch
• Mathematics: Strong foundation in linear algebra, probability, and optimization techniques
• Model Evaluation: Experience in evaluating models using metrics like accuracy, recall, F1 score, etc.

While AI experts may design the logic behind intelligent systems, ML professionals focus on  learning aspect through data and optimization.

Expert Insights with Frank Kane (CEO of Sundog Education, Former Sr. Manager at Amazon)

Q: What skills are essential for professionals looking to build a career in AI and machine learning?

We all need to know how to use AI tools effectively and safely to accelerate our work. That means understanding their limitations and appropriate applications. Large language models, such as ChatGPT, still have a tendency to make up answers that sound authoritative, but aren’t – so it’s important to know how to augment these models with additional context, information, and capabilities to improve their accuracy for a given task.

If you’re a software developer, AI has changed everything. But even for writing code, AI has its limits – your software engineering fundamentals are still needed to nudge the AI toward producing code that fits within a well-engineered larger system, and to validate its correctness, robustness, and security.

For those aiming for an AI engineer role, understanding how to extend or specialize these systems for a given problem is a crucial skill. The application of techniques such as fine-tuning, retrieval-augmented generation, and agentic frameworks are skills to start with, along with using these systems through their API’s. I’ve worked with Udemy to produce several hands-on labs to help you practice those skills.

Q: What are some emerging trends in AI and ML that professionals should be aware of?

One trend is toward efficiency, which I applaud – not every problem demands an energy-hungry solution. The DeepSeek model made some waves in producing results comparable to systems that were much more power-hungry in their training. But at the same time, we have companies producing ever-larger, more complex models requiring massive data centers of their own, such as Grok.

As a professional, the important skill is knowing how to choose the right model for the product at hand. Often, a less-expensive small language model or classical model, coupled with the right data sources, fits the bill.

Agentic frameworks are also gaining traction this year. It’s an ill-defined term, but it basically means giving large language models access to external tools. It could be as simple as giving it access to external data sources, or as complex as giving it control of your web browser, investment accounts, or real-world machines. Ethics, responsibility, and governance are all very important topics as we apply AI agents; things can go very, very wrong if they are unleashed and don’t behave in ways you expect. A hot topic right now is Model Context Protocol (MCP,) which is a way for external systems to easily plug into these AI agents.

AI vs ML: Career Opportunities

Both AI and ML are fast-growing fields with numerous career opportunities, but each offers distinct career paths.

Careers in AI

AI encompasses a wide range of fields that go beyond machine learning. Professionals in AI work on systems that simulate human reasoning, decision-making, and problem-solving. Key career opportunities include:

• AI Engineer: Builds and optimizes AI models and systems
• AI Researcher: Conducts research on advanced AI topics, including neural networks and cognitive computing
• Robotics Engineer: Focuses on designing and optimizing robots using AI for automation and decision-making
• Business Analyst (AI): Uses AI to extract insights and drive strategic decisions
• AI Solutions Architect: Designs and implements large-scale AI systems for enterprises

Careers in Machine Learning

Machine learning is more specialized and deals with teaching systems to analyze and interpret data. ML professionals typically focus on predictive analytics, data modeling, and continuous learning. Key career opportunities include:

• Machine Learning Engineer: Develops, tests, and implements ML models
• Data Scientist: Uses ML to analyze large datasets, predict outcomes, and generate actionable insights
• ML Researcher: Investigates new algorithms and approaches to improve ML models
• Deep Learning Engineer: Specializes in developing deep neural networks for complex problems like image recognition and natural language processing
• Business Analyst (ML): Applies machine learning to solve specific business problems and predict trends

While both fields offer high demand and competitive salaries, ML roles tend to be more data-centric and focus on hands-on model development, while AI roles may involve broader research and application in systems beyond just machine learning.

Why Learning AI and ML Is Essential to Many Career Paths

Once you start learning AI and ML basics, you’ll see how these technologies can be used across a variety of industries. Sectors that depend on AI and ML for automation, data analytics, and decision-making include:

• Agriculture
• Cybersecurity
• Energy
• Finance
• Healthcare
• Manufacturing
• Retail

Many companies are on the lookout for AI and ML professionals to innovate and improve efficiency. Some are willing to provide new hires with on-the-job training so they can become valuable contributors to their long-term success. 

AI and ML certifications give you a leg up over other job candidates and open the door to high-paying jobs. Since you’ll stand out in a competitive job market, you’ll be well-positioned to earn a great salary and benefits package. 

As you debate what to do next, consider how Udemy can help you unlock your potential. 

How to Learn AI and ML with Udemy

We offer 250,000 online courses to help you adapt to change and thrive.

Python for AI and ML Development

Sign up for the Python for Data Science and Machine Learning Bootcamp to learn the go-to language for AI and ML development. Python is simple, reliable, and handles complicated data analysis tasks. You can also use it with NumPy, TensorFlow, and other libraries for data manipulation, deep learning, and more.  

Hands-On Projects for Building AI-Powered Applications

Gain the skills and confidence you need to create AI-powered apps. Our instructors walk you through the process of creating custom apps, so you’ll have no trouble tackling hands-on projects. 

Deep Learning and Neural Networks

Enroll in courses that teach you how to utilize deep learning. This subset of ML lets you establish neural networks similar to the ones the brain uses to make complex decisions and advanced predictions.

Real-World AI Use Cases

Find out how you can apply artificial intelligence to your everyday life. Once you do, you’ll be able to use AI to change the world in ways you never thought possible. 

Our course catalog is growing, and we continue to add to our collection of artificial intelligence and machine learning courses. 

The Bottom Line on Machine Learning vs. AI 

AI and ML are similar but different, and they have distinct roles in technology and business. Learning to use them can open the door to new career opportunities in many global industries. 

At Udemy, we make it easy to build your AI and ML skill set. Our courses are available in 75 languages and are backed by 80,000 instructors. They help you prepare for the path ahead and make the most of it. Start learning AI and ML with our expert-led courses today.