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Artificial intelligence vs Machine learning vs Deep learning

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Data science has been termed as “sexiest job of the 21st century” by Harvard. And with all the recent hype around these terms like machine learning, artificial intelligence (AI), deep learning & data science, it is becoming confusing to separate them each other understand their true meaning.

They are also referred as electricity of this century therefore these industries offer some of the best opportunities for all kind of professionals.

Artificial intelligence, machine learning & deep learning are one of the hottest topics on planet earth today and people from all streams are trying to get into these promising fields.

As a result, if you look at the google trend for some these terms like “what is machine learning”, “what is artificial intelligence (AI)” or “what is deep learning”, you will find a constant increase in interest around these terms.

what is machine learning

what is machine learning

what is artificial intelligence

what is artificial intelligence

what is deep learning

what is deep learning

A lot of companies selling products or services based on machine learning or artificial intelligence or deep learning, tend to use these terms interchangeably which further contributes to confusion. A lot of people believe that machine learning, deep learning & artificial intelligence are same.

And this is far from truth.

Have you been wondering about

  • What is machine learning
  • What is deep learning
  • What is artificial intelligence

OR

  • What is the difference between artificial intelligence and machine learning
  • What is the difference between machine learning and deep learning
  • Artificial intelligence Vs machine learning Vs deep learning

You will find this post useful.

In this post, I am going to cover the broad definition of machine learning, deep learning & artificial intelligence (AI).

So, before we move ahead with the differences between machine learning, artificial intelligence and deep learning, we need to understand these concepts at a high level.

What is artificial intelligence (AI)

  • It is the higher umbrella category covering all aspect of the space where machines are expected to use intelligence for decision making.
  • IBM Watson is a common example of AI tool.
  • It encompasses machine learning and deep learning fields.
  • Artificial intelligence is a higher-level field which has been derived from Math, Computer Science, neuroscience and artificial psychology.

What is machine learning

  • Machine Learning is the application of Artificial Intelligence.
  • Machine Learning is a subset of AI.
  • Machine Learning use statistical analysis & computing to deliver results.
  • In machine learning, you define the features you need to make predictions or a perform a task like email classification.

What is deep learning

  • Deep Learning takes the automation a step ahead and you don’t need to define the features.
  • Deep learning has gained momentum in last few years especially after rise of cloud computing & GPUs because it requires large amount of data and cloud platforms with GPU facilitates rapid processing of data.
  • It also known as deep artificial neural networks.
  • It is practically a subset of Machine Learning and but very different than rest of the algorithms.
  • It is inspired by neuron and attempt is to make artificial neurons mimicking human brain.

You can think of “Science” subject from your school, it consisted- physics, chemistry and biology.


Similarly, artificial intelligence is the science in this case.

Artificial intelligence consists of machine learning.

Machine learning consist of various models including deep learning.

Deep learning is a special technology.

So, the question of machine learning vs artificial intelligence (ai vs ml) doesn’t arise because artificial intelligence encompasses machine learning.

Similarly, you can say that machine learning encompasses deep learning but there are few differences between most popular machine learning algorithms like logistic regression and deep learning.

To elaborate on some of these differences between most popular machine learning algorithms and deep learning, I have added this table. But remember, deep learning is subset of machine learning.

Machine Learning

Deep Learning

Need Lesser Data than Deep Learning.

Need More Data.

Can work comfortably with CPU

Can work with CPU but performance issues, need GPU for optimum performance.

Need to manually define the features with supervised machine learning models.

Doesn’t need manual definition of features, model can automatically figure that out.

Good and recommended when you need to control the feature definition and recreation.

Recommended when your focus is on output and not on ability to define feature but is not recommended if feature definition is important.

Popular applications include classification models like fraud detection, email spam or not spam.

Popular applications include image classification, text classification.

Some of the common examples of machine learning algorithm are – linear regression, logistic regression, KNN, k-means, random forest etc.

Some of the common examples of deep learning algorithm are- Convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), generative adversarial network (GAN) etc.

If you still have any confusion, please leave your comment.

And yes, don’t forget to share.

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