When searching for a Machine Learning Course in Gurgaon, you must have encountered three terminologies that sound the same: Artificial Intelligence, Machine Learning, and Deep Learning. It is not surprising that beginners confuse these three because they are very much related, but they are different.
It is essential to know the difference before embarking on the journey of learning all these skills. In this blog, we will try to differentiate between AI, ML, and Deep Learning in the easiest way possible.
What is Artificial Intelligence (AI)?
AI is the most inclusive of all three. AI is defined simply as creating machines that are intelligent enough to act, think, or make decisions like humans. AI is therefore the general aim or the umbrella term for several technologies.
Examples of AI in everyday life are:
- Voice assistants such as Siri or Alexa
- Customer support chatbots
- Autonomous vehicles
- Facial recognition on phones
AI is not just one technology. It is a discipline of computer science that encompasses various sub-disciplines, and Machine Learning is one of these sub-disciplines.
What is Machine Learning (ML)?
Machine Learning is a subfield of AI. Rather than writing out a set of instructions in advance that the computer follows, ML enables the computer to develop its own understanding based on data. The more data it gets, the smarter it becomes.
For instance, an email program applies ML by using past data of thousands of emails to detect any patterns. It learns from those patterns and then applies its findings to incoming emails.
Methods of ML include:
- Linear regression
- Decision tree
- Random forest
- Support vector machine
The algorithms learn patterns within data without being programmed to look for anything specific. That is why ML is employed in finance, healthcare, marketing, e-commerce, and nearly any field you can think of today.
What is Deep Learning?
Deep Learning is a subset of Machine Learning that uses neural networks, an imitation of how the human brain works. However, Deep Learning requires massive amounts of data as well as advanced computing hardware to operate, but it can address much more complex problems compared to the standard Machine Learning approach.
Illustrations of how Deep Learning works:
- Facial recognition from photographs
- Instant translation between languages
- Enabling intelligent chatbots and other AI technologies
- Disease detection through medical imaging
Deep Learning involves many layers of neural networks; hence, Deep Learning is referred to as “deep.” Every layer learns something else, and as a whole, they can recognize very complicated patterns that traditional ML techniques cannot.
AI vs ML vs Deep Learning: The Simple Difference
And here is how to recall it easily:
- AI is the ultimate objective – artificial intelligence of machines.
- ML is the approach to achieving AI – learning by machines from the data.
- Deep Learning is the approach of ML – involving neural networks for more complex tasks
Imagine a set of nesting boxes. AI would be the largest box. ML would be a box within AI. Deep Learning would be a box within ML.
Why This Matters for Your Career
Today, organizations are looking to hire individuals who have knowledge of these technologies, and that too, in an applied sense. Positions such as Data Scientists, Machine Learning Engineers, and AI Developers are amongst the top-paying jobs in the technology world today. However, in order to land these positions, you will need proper training and experience.
This is where the correct learning pathway comes into play. Rather than attempting to cobble together various YouTube videos and reading articles on the subject matter, having a proper course allows one to learn about AI, Machine Learning, and Deep Learning in an effective manner.
Final Thoughts
While it may be intimidating at first when one hears about AI, ML, and DL, because the concepts are interchanged and often confused with each other, knowing how they relate to each other will make things much simpler.
The concept of AI deals with creating smart machines. ML provides a process by which such machines can learn from experience rather than obeying pre-programmed instructions. Deep Learning is an extension of machine learning, making use of neural networks to solve intricate problems.
