Machine learning and artificial intelligence are among the most advanced technologies in the world today. They are the backbone of some of the most advanced tools and technologies today, like autonomous vehicles and LLMs, including Gemini and ChatGPT. Though both AI and machine learning are highly interrelated, there are a few major differences that set them apart. All students aspiring to make a successful AI career in 2025 should clearly understand the major differences between these technologies.
In this article, we will explore the major differences between AI and machine learning in terms of technologies, career prospectus, salaries, and more.
Artificial Intelligence
Artificial intelligence is a broad domain and refers to the technology that makes machines and computer systems perform jobs similar to humans by mimicking human intelligence. AI systems include a wide range of capabilities such as learning, reasoning, problem-solving, perception, language understanding and processing, decision-making, and more.
The ultimate goal of Artificial intelligence is to create machines that can perform jobs that require human intelligence, from simple rule-based systems to complex neural networks.
Machine Learning
Machine learning is a subset of artificial intelligence that helps computer systems learn from data without explicit programming. Machine learning algorithms do not rely on regular hard-coded rules to identify patterns, and make predictions, or decisions, but they improve their performance over time through experience.
This is possible because training in machine learning algorithms is done on large datasets that help them extract relevant features and build machine learning models that can work on new and unseen data.
The Relationship Between AI and Machine Learning
We can visualize the relationship between AI and ML as a set within a larger set. AI is a broader concept that includes several approaches to develop intelligent machines whereas machine learning is one of those approaches which focus on learning from data.
So, we can say all machine learning is AI, but not all AI is machine learning.
This can be easily understood through this analogy – AI is like building a car whereas machine learning is like designing the engine. The car (AI) can be powered by different types of engines (various AI approaches) and machine learning is one of those engine types.
Methodologies and Techniques
AI Methodologies
It includes several methodologies AI engineers work on, of which machine learning is just one part. Following are some essential AI methodologies:
- Rule-based systems – these systems rely on predefined rules for their decisions. They can easily solve problems that require clear rules
- Expert Systems – this AI approach helps systems make decisions similar to humans in specific domains. They use knowledge bases and inference engines to function properly.
- Natural Language Processing – it helps computer systems understand and generate natural human languages
- Robotics – it is the combination of AI and mechanical engineering that helps create intelligent robots that can perform physical human-like tasks
- Computer vision – computer vision enables systems to see and perceive their environment through images and videos
Machine Learning Techniques
Machine learning also uses a variety of techniques to learn from data on its own. Important machine learning techniques are:
- Supervised learning – in this method, machine learning algorithms learn from labeled data
- Unsupervised learning – here the algorithms learn from unlabeled data to identify patterns and structures without much programming or guidance
- Reinforcement learning – this method helps the agents learn to interact with the environment by receiving rewards or penalties for their actions
- Deep learning – it uses artificial neural networks to learn complex patterns from large datasets
These were some important AI methodologies and machine learning techniques that differentiated both of them.
Career Prospects
Both the artificial intelligence and machine learning fields offer exciting career opportunities. While AI Engineer is the most sought-after AI job role, Machine Learning Engineer is a popular job in the machine learning field.
Following are various jobs you can consider for your AI career in these fields:
| Artificial Intelligence | Machine Learning |
| AI Engineer | Machine Learning Engineers |
| AI Research Scientist | MLOps Engineer |
| AI Consultant | Machine Learning Research Scientist |
| Robotics Engineer | Deep Learning Professionals |
| Natural Language Processing Engineer | Data Scientists |
You must understand both fields are closely related and it is not fixed that jobs mentioned under AI cannot work in the ML field.
AI and ML share the same fundamental concepts and job roles like data scientists, NLP engineers, cybersecurity analysts, etc. can be found in both fields.
Start a Career with the Best Certifications
The fields of AI and machine learning share common expertise i.e., you need to be good at computer science, mathematics and statistics, and other basic concepts like GAN, NLP, algorithms, etc. So, start learning the fundamentals with the best AI certifications that cover the essential concepts of AI and machine learning and build a strong foundation.
Both fields offer exciting and rewarding career opportunities. These are emerging technologies and AI professionals are in high demand. Below mentioned are the annual average salaries of top AI job roles (as per Glassdoor)
- Average Salary of AI Engineers – $133,281
- Average Salary of Machine Learning Engineers – $122,133
With top AI and machine learning certifications, students and professionals can earn even higher salaries, increase their chances of hiring, and enhance their credibility in the industry.
AI and ML is a growing and rewarding industry!
The skills you will gain in this field will lead you to prosperous and highly successful career paths. So, start mastering the fundamentals, validate your experience and skills with top certifications, and succeed in this career path.
