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Machine Learning vs Deep Learning vs AI: Which has the most potential?

Updated on
Jan 10, 20246 min read
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    Table of content

  • From Bust to Boom
  • arrow
  • Artificial Intelligence
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  • Machine Learning
  • Deep Learning
  • The Next Level of AI
  • Critical Differences Between AI, ML, and Deep Learning
  • Conclusion
  • From being in Research to Reality

Many misconceptions exist about machine learning, deep learning, and artificial intelligence (AI). Most people believe that when they hear the word AI, they automatically associate it with machine learning or vice versa. These words are not interchangeable, despite their similarity. Artificial intelligence, machine learning, and deep learning are the buzzwords of this century. Their broad range of applications has revolutionized technology in industries as diverse as healthcare, manufacturing, business, education, finance, information technology, and more! 

The machine learning market will increase at a CAGR of 44 percent during the projection period, from $1 billion in 2016 to $9 billion in 2022. The global machine learning market was worth $8 billion in 2019 and will grow at a 39 percent CAGR to USD 117 billion by 2027.

Forty-six percent of respondents have used machine learning in many areas and consider it essential to their business. North America has the highest rate of machine learning usage (80%), followed by Asia (37%) and Europe (37%). Companies are estimated to have 35 AI projects on average by 2022. 

According to 20% of the business, the second most crucial use of AI is automating procedures like invoicing and checking contracts. By 2027, 80 percent of retail executives want their business to have used AI-powered intelligent automation. At the same time, deep learning has access to over 14 million photos. It uses deep learning models for complete image identification. These words are not interchangeable, despite their similarity. There is, however, a notable change between them all. 

Read ahead to get a clear and apt distinction between AI and ML.

From Bust to Boom

Since a few computer scientists gathered around the phrase at the Dartmouth Conferences in 1956 and created the subject of AI, AI has been a part of our imaginations and simmering in research laboratories. In the decades afterward, AI has been hailed as the key to our civilization's brightest future and derided as a blunder by over-reaching propellerheads. Until 2012, it was a little of both.

AI has expanded in recent years, particularly since 2015. It is due to GPUs becoming more widely available, making parallel processing faster, cheaper, and more powerful. It also has to do with a deluge of data of all kinds. the entire Big Data movement) – photos, text, transactions, mapping data, you name it.

Let's take a look at how computer scientists went from a bust — until 2012 — to a boom that has resulted in hundreds of millions of people using applications every day. 

Artificial Intelligence

Artificial intelligence is a broad word that refers to a variety of technologies. In its most basic form, artificial intelligence (AI) is a machine that can mimic or reflect the traits of human intelligence. For decades, artificial intelligence has been a theory and a part of the narration in movies and science fiction novels.

Artificial intelligence is now a reality. Businesses across industries are already using AI to automate, predict, and optimize operations that people previously performed. It saves money and time for the company and makes staff happier by removing them from tiresome, repeated activities.

AI is divided into three groups or types:

  1. Artificial Narrow Intelligence (ANI)
  2. Artificial General Intelligence (AGI)
  3. Artificial Super Intelligence (ASI)

Artificial Narrow Intelligence, the first type of AI, is generally referred to as "weak" AI. At the same time, the other two are known as "strong" AI. Weak AI, also known as ANI, differs from the other two forms of AI in that it can only do a single job. Strong AI, such as AGI and ASI, can do several jobs. 

Machine Learning

Where does artificial intelligence get its intelligence? ML stands for Machine Learning. ML is a subset of the broader fields of Artificial intelligence. ML aims to teach computer systems to understand without being coded to do different tasks. It may also refer to the techniques and models used to educate and train artificial intelligence to do specific tasks.

Machine learning has three main characteristics:

  1. Datasets: A dataset consists of a set of data points or samples. A number, image, word, audio file, or video could represent each data point. ML models are trained using datasets.
  2. Features: Features are data points that help machine learning models understand what they're looking for.
  3. Algorithms: A ML model's algorithm is the process or set of rules it employs to parse data and arrive at a conclusion or answer.


At its most basic level, a machine learning model employs an algorithm to read thousands or millions of data points and then arrive at a conclusion or prediction. To read the datasets properly, the ML model requires an algorithm to inform it on what to do. You must train the ML model before it can read data and draw conclusions. Experts create a machine learning model using a dataset and features so that they may utilize its method to make conclusions based on real-world data.

Computer vision has proven to be one of the most useful applications of machine learning. Computer vision (CV) has various applications. One of the most exciting is the development of self-driving, autonomous vehicles.

Deep Learning

Machine learning includes deep learning as a subset. Deep understanding differs from other forms of machine learning in the way the algorithm learns and the amount of data it consumes. Deep learning necessitates massive data sets, yet only minimal human participation is required.

Deep learning uses large, multi-layered neural networks to simulate the structure of the human brain. Through linking channels, it transmits data between neural networks. Labeled data sets can help deep machine learning models learn, but they aren't required. You can use unsupervised or supervised learning to teach deep learning models. A deep understanding of AI can learn from unstructured or unlabeled data, which is one of the most exciting elements of the technology. The capacity to create an unsupervised learning model is the future of AI. 

The Next Level of AI

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Critical Differences Between AI, ML, and Deep Learning

Although AI, ML, and deep learning are all related to the same thing, it's crucial to know the differences.

  1. AI is a broad term for algorithms that analyze data to uncover patterns and solutions. Artificial intelligence is similar to how humans solve problems. ML is used in the majority of AI initiatives.
  2. Machine learning is a type of artificial intelligence that uses data and an algorithm to solve problems.
  3. Deep learning is a sort of machine learning that uses neural networks to learn from and predict unstructured data.


Artificial intelligence offers a wide range of applications transforming the technological landscape. While establishing an AI system as intelligent as humans is still a long way off. Machine learning currently allows computers to surpass people in computations, pattern recognition, and anomaly identification. Deep learning necessitates massive data sets, yet only minimal human participation is required. Deep understanding uses large, multi-layered neural networks to simulate the structure of the human brain in our blog. 

We hope this blog helps you understand the difference and make an informed decision about AI and ML. 

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From being in Research to Reality

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