Essay on Machine Learning

Essay on Machine Learning in English For Students

Essay on Machine Learning in English For Students

As technology evolves and the world’s population grows so does the amount of data being generated collecting sorting and analyzing all of this data can be pretty time-consuming

However, that’s starting to change with the emergence of something called machine learning

Machine learning is a type of computer science that allows computer programs to learn and improve all on their own

In the past computers could only do what we programmed them to do but with machine learning they can behave like humans and gain knowledge based on their past experiences

This process can get pretty complicated but here’s how it works on a basic level, let’s say we want a program that can tell the difference between apples and bananas

First we’ll give it some labeled pictures of each the program will then look for patterns in the fruits and start to remember them it can then use these memories to look at unlabeled pictures of apples and bananas and determine

Which is which all on its own believe it or not you probably encounter machine learning on a daily basis

Social media sites use it to create your feed based on your preferences and search engines use it to improve the accuracy of their search results it’s also being used on a larger scale

The medical industry is applying machine learning to things like predicting lifespans organizing patient data and even diagnosing certain diseases

As with many emerging technologies, there’s a concern that machine learning may eliminate certain jobs

This will vary from industry to industry but it does have the potential to get rid of or change a number of occupations including drivers bankers and potentially even certain doctors

While it may have some negative effects many believe that its positive applications will outweigh them.

Essay on Machine Learning in English For Students (700 Words)

Arthur C. Clarke once famously said, “Any sufficiently advanced technology is indistinguishable from magic.”

The world is full of data, lots of data–pictures, music, words, spreadsheets, videos, and it doesn’t look like it’s going to slow down anytime soon.

Machine learning brings the promise of extracting meaning from all that data.

I found that machine learning is not magic, but tools and techniques that you can use to answer questions with your data.

The value of machine learning is only starting to show itself. A lot of data in the world today is generated not only by people but also by computers, phones and other devices.

It will only continue to grow in the years to come. Traditionally, humans have analyzed data and adapted systems to changes in data patterns.

However, as the amount of data outweighs the ability for humans to understand it and manually write down those rules, we will increasingly turn to automated systems that can learn from the data and, importantly, in the data. Changes to adapt to a relocation scenario.

We see machine learning all around us in the products we use today. However, it is not always clear that there is machine learning behind this.

While things like tagging objects and people inside photos clearly have machine learning at play, of course, perhaps the biggest example of all is Google Search.

Every time you use Google Search, you’re using a system that has multiple machine learning systems at its core, from understanding the text of your query to adjusting results based on your personal interests. As far as,

Like knowing which of the first results you’ll see when you search for Java, depending on whether you’re a coffee expert or a developer, you probably are both.

Today, the immediate applications of machine learning are already quite widespread, including image recognition, fraud detection and recommendation systems, as well as text and speech systems.

These powerful capabilities can be applied in a range of fields from detection of diabetic retinopathy and skin cancer to retail and, of course, transportation in the form of self-parking and self-driving vehicles.

It wasn’t that long ago that machine learning was considered novel when it came to a company or product offering.

Now, every company is moving towards using machine learning in their products in some way or the other. It’s fast becoming, well, an expected feature.

Just as we expect companies to have a website that works on your mobile device or perhaps an app, the day will soon come when it is expected that our technology will be personalized, practical and self-improving.

As we use machine learning to make human tasks better, faster and easier than ever before, we can also look forward to a future when machine learning can help us perform tasks that we might never have done on our own. could not achieve.

Thankfully, it’s not hard to take advantage of machine learning today. Tooling has gotten pretty good. All you need is data, developers and a willingness to take the plunge.

I’ve shortened the definition of machine learning to just five words–using data to answer questions.

Specifically, we can split the definition into two parts—using the data and answering questions. These two pieces broadly outline the two sides of machine learning,

Both are equally important. Using data that we refer to as training, making predictions or making predictions while answering questions.

The training uses our data to inform the construction and fine-tuning of predictive models.

This predictive model can be used to serve predictions on previously undiscovered data and to answer those questions.

As more data is collected, models can be improved over time and new predictive models can be deployed.

As you may have noticed, the key component of this whole process is data. Everything hinges on data.

Data is the key to unlocking machine learning, just as machine learning is the key to unlocking that hidden insight into data.

This was a high-level overview of machine learning, why it is useful and some of its applications.

Machine learning is a broad field, spanning a whole family of techniques for inferring answers from data.

Essay on Machine Learning in English For Students (1000 Words)

Imagine yourself in the future a future where robots and humans live together in harmony you come home tired go straight to bed and rock bottom

The ai of your house looks at you analyzes everything about you using the sensors around the lights of the house go doc the air-conditioner is adjusted to the temperature of your body needs so that you can get the optimal sleep that you deserve

Time slips away and it’s morning already the sun is shining and you are happy because you had a wonderful sleep

Now it’s time to go back to work your ai sets up everything needed for your morning necessities water is set to the optimal temperature news or music is played according to your mood which is calculated using the sentiment analysis algorithms

You finish your work and the cab is booked as soon as you are ready to go back to work how did that feel wasn’t it fascinating well

Let me tell you that all of this is becoming a reality ever so slowly but it is moving in the right direction how is all of this happening it is biggers of artificial intelligence

So what is ai you may ask them? Well ai is to create an artificial being that can mimic the things that our human does it is to create a human which does not live it exists

Now, what has machine learning got to do with any of this? Let me tell you that it does machine learning is a subset of artificial intelligence that helps us achieve one particular goal to teach

One characteristic of us humans is that we can learn if we are able to teach our computers to learn – then we are one step closer in reaching our goal of the ai that can mimic a human

So what is machine learning formally machine learning is the scientific study of algorithms and statistical models that computer systems used to perform a specific task without the use of explicit instruction relying on patterns and inferences instead?

It is a subset of artificial intelligence and when our machine learning models feel we mimic the way our brain works using deep learning we create artificial neural networks that can process information the same way that we humans do

What are the different types of machine learning

Supervised learning
Unsupervised learning
Reinforcement learning

What is supervised learning?

Supervised learning is the machine learning task of learning a function that map’s an input to an output based on the input and output pairs

It infers a function from label training data consisting of a set of training examples

How can we simplify

This think of a teacher teaching the students the student knows what they learn and it has also been taught by the teacher

Now think you want your model to differentiate between a penguin and a pigeon how would you do that

It’s very simple you collect data you clean it and you feed it to the computer

Make it learn from the data and then ask it whether it is a penguin or a pigeon and it will be able to tell you the difference

Well there are some famous models such as a linear and the logistic regression random forest naive bias and decision tree

What is unsupervised learning?

Unsupervised learning is a type of self-organized learning that helps find previously unknown patterns in data set without pre-existing labels

It is also known as self-organization and allows modelling probability densities of given inputs

Let’s simplify that

Think of a student who has everything to study from but no teacher what does that student do the student has to study by himself that’s the same thing with unsupervised learning

Think you have a basket of fruits but you do not know the names of the fruits but you do know that there are different fruits in the basket what do you do next it’s simple you program the model to do the same

So that it can differentiate between the fruits in the basket you make the model own so that it can differentiate between the fruits for the colour the size and the shape of the fruit

So if you find out that there are three major fruits in the basket they are apple orange and strawberries you group them together

Is the simplest example of unsupervised learning this would seem similar to an interview process a company comes in knows nothing about you but takes a look at certain abilities of yours and groups you into different departments this is a live example of unsupervised learning

We have some unsupervised learning algorithms such as the keens class ring a priori algorithm hierarchical and association rules

So the last type of learning reinforcement learning

Reinforcement learning is an area of machine learning concerned with how software agents or take actions in an environment so that they can optimize or maximize some notation of the cumulative reward

Word is this type of learning in simple words you do not have data to teach nor does the student know how to study

The student then slowly tries to study it picks up the pencil it opens up a book it writes the pencil facing backwards walks out of the room thinking that it is called studying it does everything that it can think of

But if the student does a good job or an action like writing on the book properly he gets a reward

That is how the student knows that he has done a good thing else the student is punished it’s simple as that

Applications that can be used for machine learning

Facebook uses machine learning to learn about the different people on the website and in your photos and recommend tagging them directly

Learning how to predict the weather from the data collected over the years is such an amazing application

Machine learning models can learn which he means are real and which he means our faith using the nave pass algorithm

So they can act for you as a spam protector virtual assistants like google and city are trained to recognize your voice only for certain keywords like hey siri

They can do the work that you asked them to do google has also gone as far as making their assistant make calls for you

But here’s the catch the voice is just so real it does not feel like it is a robot can google be the one to pass the turing test and bring out an ai that shocks the world

Recommendation systems are another application where an association rule algorithm is used to find out what users want to buy and recommend them accordingly

Machine learning has surrounded us literally everywhere so why not use it to your advantage become somebody who makes such models.

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