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What Is the Difference Between AI & ML?

AI and ML are often used interchangeably when discussing intelligent systems, big data, and predictive analysis. Although they both are related technologies, they differ at a broader level in several ways, such as scope and application. 

Businesses have increasingly started using AI & ML products to generate real-time insights, process and analyze enormous volumes of data, enable better decision-making, and make accurate predictions. So, what exactly are Artificial intelligence and Machine learning, what are the key differences and what are the career prospects of AI & ML? Let’s learn.


What is Artificial Intelligence?

Artificial intelligence is a broader concept that refers to the usage of intelligent machines that has the ability to mimic human intelligence to understand, respond and analyze data.  A few AI applications include expert systems, natural language processing, speech recognition, and machine vision.


What is Machine learning?

Machine learning is a part of artificial intelligence that helps computers learn from past data or experiences like humans do. It mainly focuses on developing algorithms that allow any system to learn from historical data or experiences. A few of the applications of ML include cyber fraud detection, face detection, self-driving cars.


Key differences between AI and ML

  1. Artificial intelligence enables machines to mimic human behavior, while Machine learning is a subset of AI that enables the machine to automatically learn from historical data. 
  2. AI aims to make a smart computer that can perform tasks similar to the human brain, while ML focuses on allowing machines to learn from past data to be able to provide accurate output.
  3. AI focuses on solving complex problems while ML focuses on getting accuracy.
  4. The scope of AI is wide as it includes Machine learning and deep learning, while Machine learning is just a part of AI.
  5. When introduced to new Data, AI focuses on reasoning, learning and self-correction while ML only focuses on learning and self-correction
  6. AI deals with 3 kinds of data – structured, semi-structured, and unstructured while Machine learning deals with only Structured and semi-structured data.


What is the Scope of AI?

The adoption of AI in India is still in its nascent stage The scope of AI is vast and has the potential to impact many aspects of our daily lives, from healthcare and transportation to education and entertainment. As technology continues to advance, the scope of AI is likely to expand, creating new opportunities and challenges for society to navigate.
AI encompasses the development and use of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, natural language processing, and problem-solving. The scope of AI can be categorized into several areas.

  1. Narrow AI
  2. General AI
  3. Machine learning
  4. Deep learning
  5. Natural Language processing

What is the Scope of ML? 

The scope of machine learning is vast and constantly evolving, as it is a rapidly growing field with the use of computer science, statistics, and artificial intelligence. Machine learning involves the development of algorithms and models that enable computers to learn from and make predictions or decisions based on large amounts of data without explicit programming.

Also Read: 6 Ways to Use Artificial Intelligence to Improve your Business Process


AI and ML Courses

As a rapidly evolving field, numerous AI (Artificial Intelligence) and ML (Machine Learning) courses are available for individuals interested in learning and mastering these technologies.

When choosing AI and ML courses to pursue, it’s important to consider factors such as your prior knowledge and experience, learning objectives, preferred learning format (online, in-person, or blended), and budget. It’s also essential to ensure that the course or program you choose is from a reputable source, taught by qualified instructors, and covers the latest advancements and best practices in the field.