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Machine Learning Algorithms Cheat Sheet-6

By PerceptionBox

1 min read

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There is a bunch of people that don’t really know if there’s any difference between in such hyped terms like machine learning and deep learning and how these two are related to each other. Before we go any further, it’s worth mentioning that even though AI, ML, and deep learning often times are used instead of one another, especially by some businessmen, marketers or startuppers, but these terms are not all the same! So, please, stop muddle them!

Scratching the surface: terms and definitions

To start with, artificial intelligence is a much broader term that encompasses machine learning and its smaller subset of deep learning. Do you see where we’re going with this? If you have a computer working on a certain task with the help of algorithms, you can’t call it artificial intelligence. However, you can call it machine learning, but let’s not rush things and dive a bit deeper.

So, machine learning is the ability of a machine to learn from its experience going through a number of tasks. Imagine you have a data set that you can feed to the machine in order to make it learn from and recognize its objects. Here’s the most important part: if you try and use an algorithm to foresee the result of an event, you cannot consider this process to be machine learning. Instead, when you use the result to refine future predictions and make them more accurate, this is when machine learning development takes place.

But there’s another level of “intelligence” in machine training, too. Deep learning is considered to be just another way of implementing machine learning or its daughter category. Think of it this way: deep learning is an upgraded version of machine learning. Why? Because its algorithms are inspired by the performance of the human brain and aim at mirroring its activity. Similarly to a human being using their brains to spot patterns and cluster different kinds of data, deep learning tools can be educated to perform identical tasks for the machines.

“It is not until you change your identity to match your life blueprint that you will understand why everything in the past never worked.”

_ Shannon L. Alder

To start with, artificial intelligence is a much broader term that encompasses machine learning and its smaller subset of deep learning. Do you see where we’re going with this? If you have a computer working on a certain task with the help of algorithms, you can’t call it artificial intelligence. However, you can call it machine learning, but let’s not rush things and dive a bit deeper.

So, machine learning is the ability of a machine to learn from its experience going through a number of tasks. Imagine you have a data set that you can feed to the machine in order to make it learn from and recognize its objects. Here’s the most important part: if you try and use an algorithm to foresee the result of an event, you cannot consider this process to be machine learning. Instead, when you use the result to refine future predictions and make them more accurate, this is when machine learning development takes place.

But there’s another level of “intelligence” in machine training, too. Deep learning is considered to be just another way of implementing machine learning or its daughter category. Think of it this way: deep learning is an upgraded version of machine learning. Why? Because its algorithms are inspired by the performance of the human brain and aim at mirroring its activity. Similarly to a human being using their brains to spot patterns and cluster different kinds of data, deep learning tools can be educated to perform identical tasks for the machines.

Scratching the surface: terms and definitions

To start with, artificial intelligence is a much broader term that encompasses machine learning and its smaller subset of deep learning. Do you see where we’re going with this? If you have a computer working on a certain task with the help of algorithms, you can’t call it artificial intelligence. However, you can call it machine learning, but let’s not rush things and dive a bit deeper.

So, machine learning is the ability of a machine to learn from its experience going through a number of tasks. Imagine you have a data set that you can feed to the machine in order to make it learn from and recognize its objects. Here’s the most important part: if you try and use an algorithm to foresee the result of an event, you cannot consider this process to be machine learning. Instead, when you use the result to refine future predictions and make them more accurate, this is when machine learning development takes place.

But there’s another level of “intelligence” in machine training, too. Deep learning is considered to be just another way of implementing machine learning or its daughter category. Think of it this way: deep learning is an upgraded version of machine learning. Why? Because its algorithms are inspired by the performance of the human brain and aim at mirroring its activity. Similarly to a human being using their brains to spot patterns and cluster different kinds of data, deep learning tools can be educated to perform identical tasks for the machines.

To start with, artificial intelligence is a much broader term that encompasses machine learning and its smaller subset of deep learning. Do you see where we’re going with this? If you have a computer working on a certain task with the help of algorithms, you can’t call it artificial intelligence. However, you can call it machine learning, but let’s not rush things and dive a bit deeper.

So, machine learning is the ability of a machine to learn from its experience going through a number of tasks. Imagine you have a data set that you can feed to the machine in order to make it learn from and recognize its objects. Here’s the most important part: if you try and use an algorithm to foresee the result of an event, you cannot consider this process to be machine learning. Instead, when you use the result to refine future predictions and make them more accurate, this is when machine learning development takes place.

To start with, artificial intelligence is a much broader term that encompasses machine learning and its smaller subset of deep learning. Do you see where we’re going with this? If you have a computer working on a certain task with the help of algorithms, you can’t call it artificial intelligence. However, you can call it machine learning, but let’s not rush things and dive a bit deeper.

So, machine learning is the ability of a machine to learn from its experience going through a number of tasks. Imagine you have a data set that you can feed to the machine in order to make it learn from and recognize its objects. Here’s the most important part: if you try and use an algorithm to foresee the result of an event, you cannot consider this process to be machine learning. Instead, when you use the result to refine future predictions and make them more accurate, this is when machine learning development takes place.

But there’s another level of “intelligence” in machine training, too. Deep learning is considered to be just another way of implementing machine learning or its daughter category. Think of it this way: deep learning is an upgraded version of machine learning. Why? Because its algorithms are inspired by the performance of the human brain and aim at mirroring its activity. Similarly to a human being using their brains to spot patterns and cluster different kinds of data, deep learning tools can be educated to perform identical tasks for the machines.

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