Machine Learning (or Artificial Intelligence) can not be explained accurately in less than a thousand words. It’s like trying to explain the evolution of human race in a single essay. You can try, but lots of important information would be left untouched.

My goal is not to discuss all about machine learning in this article (my understanding of the subject is also very limited), but rather to share few details that will change your status from being a novice to a slightly informed individual on the subject of machine learning.

What is Machine Learning?

Doing away with the big term; it’s basically having a computer perform complex task based on available data and previous experience. Take for instance; your Facebook feeds is a product of basic machine learning. It will only show status and post from people you’re familiar with or those you’ve interacted with. This explains why users have dynamic feeds based on their interest.

The algorithm (sorry, no better words) that sort users feed has learn to show only status and post that will appeal to them based on their interest. If you have a crush on Facebook, and you check his or her profile often, you’d realize that every of their post would always show up in your news feed.

That’s a very basic example of how machine learning works.

It basically analyze big data, makes sense of it, then make decision based on the data and previous experience. The goal of machine learning or artificial intelligence generally is to get a computer (software or hardware) to perform a particular function or task without getting gigantic instruction.

It’s like giving a computer a mind of it’s own. Traditionally, programming has always revolved around logic; if A happens then proceed to B, if not, execute C, if B is executed, Run D while E records users action from point A.

Traditionally programming has always been a this or that instruction. Machine learning takes it beyond that. It tells the computer; you have A, B, C, D and E at your disposal, make sense of what can be done with the resources and give a good result.

That sounds complex because it is. Machine Learning is complex.

Another example of machine learning product that a novice can easily relate to is Google Search Engine Result Page. Google automatically rank results based on hundreds if not thousands of factors. The ranking is done by algorithm that does not only understand the data available to be processed, but also understands the behavioral pattern, location and interest of the person doing the searching.

This explains why a somebody searching for “best restaurant” from Nigeria (Lagos) will get a different result from somebody searching with the same strings but from Paris (France).

Back to the Facebook example of application of machine learning (Facebook because I know most people are familiar with this). Have you ever uploaded a picture on Facebook, and you try tagging your friend, but facebook automatically recognize the person in the picture even before you input the person’s name? Well, that’s machine learning.

If you’re having a bad internet connection, and a picture fails to load on Facebook, a text usually appear in the image box, the text would be something like this: “image probably contains, animal, female human, plant” Something like that…

Machine learning (Artificial intelligence) at work.

What’s the Craze All About?

ML and AI has been on the upward trend lately. Lot’s of seasoned programmers are delving into the field of machine learning. As a matter of fact; ML is currently considered a buzzword of the season.

Machine learning is the future of technology. Getting computer to run on their own with little or no human input is the next big thing. It’s a very huge field niche in computer science, and it overlap several other complex field such as mathematics, statistics, physics, software engineering and several other fields based on your preferred sub-niche.

From the paragraph above, you would deduce that a deep understanding of mathematics, statistics, software engineering is needed to attain a “beginner” status in the field of Machine learning.

Although it’s not a new field, but learning resources available for this field is still in the primitive stage, meaning, you have to combine the pieces from different fields together to make sense of how things works.

-Data is money.

Although very people can make sense of “jargons” (data), the very few people that can make sense of it get paid a lot of money to put their knowledge into action. Averagely, a machine learning programmer in the US earns about $150,000 per year. While data scientist earns about $160,000 per year on the average.

With that being said, it’s not hard to conclude on what the craze about machine learning is all about. People want to build something super for the next generation of human race, they always want to get paid handsomely while doing what they love.

Most machine learning programmers generally have less than 10 (ten) years of experience, it’s a field that is just gaining traction and as such, knowledge in the field put you ahead of millions of people walking the surface of the earth.

What Can You Make Of This Information?

For one, you know what machine learning and artificial intelligence is all about now. You won’t look clueless when a discussion about the subject comes up. Although it’s advisable that you stay silent when experts are talking about the subject so you can learn more.

If you’re the curious type, your interest is probably piqued by now, and you would go on searching for resources on how to become a machine learning programmer.

In the future, I intend to write about available resources to get started with learning machine programming.

That’s machine learning in less than a thousand words. Use the comment box for specific questions.

Source: http://www.growth.ng/machine-learning-for-novice/

iwriterng:
Machine Learning (or Artificial Intelligence) can not be explained accurately in less than a thousand words. It’s like trying to explain the evolution of human race in a single essay. You can try, but lots of important information would be left untouched.

My goal is not to discuss all about machine learning in this article (my understanding of the subject is also very limited), but rather to share few details that will change your status from being a novice to a slightly informed individual on the subject of machine learning.

What is Machine Learning?

Doing away with the big term; it’s basically having a computer perform complex task based on available data and previous experience. Take for instance; your Facebook feeds is a product of basic machine learning. It will only show status and post from people you’re familiar with or those you’ve interacted with. This explains why users have dynamic feeds based on their interest.

The algorithm (sorry, no better words) that sort users feed has learn to show only status and post that will appeal to them based on their interest. If you have a crush on Facebook, and you check his or her profile often, you’d realize that every of their post would always show up in your news feed.

That’s a very basic example of how machine learning works.

It basically analyze big data, makes sense of it, then make decision based on the data and previous experience. The goal of machine learning or artificial intelligence generally is to get a computer (software or hardware) to perform a particular function or task without getting gigantic instruction.

It’s like giving a computer a mind of it’s own. Traditionally, programming has always revolved around logic; if A happens then proceed to B, if not, execute C, if B is executed, Run D while E records users action from point A.

Traditionally programming has always been a this or that instruction. Machine learning takes it beyond that. It tells the computer; you have A, B, C, D and E at your disposal, make sense of what can be done with the resources and give a good result.

That sounds complex because it is. Machine Learning is complex.

Another example of machine learning product that a novice can easily relate to is Google Search Engine Result Page. Google automatically rank results based on hundreds if not thousands of factors. The ranking is done by algorithm that does not only understand the data available to be processed, but also understands the behavioral pattern, location and interest of the person doing the searching.

This explains why a somebody searching for “best restaurant” from Nigeria (Lagos) will get a different result from somebody searching with the same strings but from Paris (France).

Back to the Facebook example of application of machine learning (Facebook because I know most people are familiar with this). Have you ever uploaded a picture on Facebook, and you try tagging your friend, but facebook automatically recognize the person in the picture even before you input the person’s name? Well, that’s machine learning.

If you’re having a bad internet connection, and a picture fails to load on Facebook, a text usually appear in the image box, the text would be something like this: “image probably contains, animal, female human, plant” Something like that…

Machine learning (Artificial intelligence) at work.

What’s the Craze All About?

ML and AI has been on the upward trend lately. Lot’s of seasoned programmers are delving into the field of machine learning. As a matter of fact; ML is currently considered a buzzword of the season.

Machine learning is the future of technology. Getting computer to run on their own with little or no human input is the next big thing. It’s a very huge field niche in computer science, and it overlap several other complex field such as mathematics, statistics, physics, software engineering and several other fields based on your preferred sub-niche.

From the paragraph above, you would deduce that a deep understanding of mathematics, statistics, software engineering is needed to attain a “beginner” status in the field of Machine learning.

Although it’s not a new field, but learning resources available for this field is still in the primitive stage, meaning, you have to combine the pieces from different fields together to make sense of how things works.

-Data is money.

Although very people can make sense of “jargons” (data), the very few people that can make sense of it get paid a lot of money to put their knowledge into action. Averagely, a machine learning programmer in the US earns about $150,000 per year. While data scientist earns about $160,000 per year on the average.

With that being said, it’s not hard to conclude on what the craze about machine learning is all about. People want to build something super for the next generation of human race, they always want to get paid handsomely while doing what they love.

Most machine learning programmers generally have less than 10 (ten) years of experience, it’s a field that is just gaining traction and as such, knowledge in the field put you ahead of millions of people walking the surface of the earth.

What Can You Make Of This Information?

For one, you know what machine learning and artificial intelligence is all about now. You won’t look clueless when a discussion about the subject comes up. Although it’s advisable that you stay silent when experts are talking about the subject so you can learn more.

If you’re the curious type, your interest is probably piqued by now, and you would go on searching for resources on how to become a machine learning programmer.

In the future, I intend to write about available resources to get started with learning machine programming.

That’s machine learning in less than a thousand words. Use the comment box for specific questions.

Source: http://www.growth.ng/machine-learning-for-novice/

In God we trust, everyone else bring data.

Yeah, Lets programme a President into Aso Rock.

Why vote sef, when the country can actually run on Auto-Pilot

Well said OP.


Anyway, in addition, before delving into machine learning...data space must be put into consideration.

I once thought about something, I wrote the program and sent it off into the web for exploration. Happily, it started...the machine I was using wasnt a great one. Before you know what something began to ring in my head. Space!

I had to abandon the project...for only big companies like google, facebook etc had the kind of space that project needed and I realized why they make headlines a lot in this field.

Many things are possible with / in machine learning, but learning needs big brain space...with big data comes big analysis and big accuracy.

The space stuff is frustrating some of my AI projects sha.

badthinds:
Well said OP.


Anyway, in addition, before delving into machine learning...data space must be put into consideration.

I once thought about something, I wrote the program and sent it off into the web for exploration. Happily, it started...the machine I was using wasnt a great one. Before you know what something began to ring in my head. Space!

I had to abandon the project...for only big companies like google, facebook etc had the kind of space that project needed and I realized why they make headlines a lot in this field.

Many things are possible with / in machine learning, but learning needs big brain space...with big data comes big analysis and big accuracy.

The space stuff is frustrating some of my AI projects sha.

That's the problem with ML, you need lots of space.

Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without explicit programming. It involves algorithms that analyze data, identify patterns, and make decisions. Supervised learning uses labeled data for training, while unsupervised learning finds patterns without labeled data. Reinforcement learning involves an agent learning from trial and error. Machine learning applications range from image recognition to language processing. To enhance career prospects in this field, individuals often pursue a data science and machine learning certification for in-depth knowledge and skill validation.