Machine Learning : Full Explanation

Machine Learning : It is a type of learning in which the machine itself learns many tasks without being explicitly programmed. Artificial intelligence is a type of application that provides this capability to systems so that they can automatically learn from their experience and improve themselves. Since machine learning can easily handle multi-dimensional and multi-variety data in a dynamic environment, it is very important for all technical students to have complete knowledge about it.

Nowadays, due to the advance of Artificial Intelligence, many such things can be done with machines which were not even possible to imagine earlier.Nowadays machine learning is used in almost all places. Machine Learning has many advantages which we use in our daily work. Therefore, today in this article complete information about Machine Learning will be provided, which will make it easier for you to understand it better.

What is Machine Learning?

Machine learning is a type of artificial intelligence by which a machine learns and predicts things automatically with the help of its experience and data. In simple words we can say that, Machine learning is a study in which a computer is capable of learning on its own beyond the human brain.

In 1959 Machine learning was invented by Arthur Samuel. With the help of machine learning, the machine makes predictions and takes very important decisions. Machine learning is a branch of computer science that gives the machine the ability to do its own work and develop itself.

Machine learning is a small part of artificial intelligence, in today's time machine learning is one of the most trending technologies in the world. From Google to Facebook, all the big tech companies use machine learning.

Whatever you search on Google, Google takes your data and shows you ads and search results accordingly using machine learning. Just like the kind of video you watch on YouTube, YouTube also recommends similar videos to you.

All the examples you saw above are examples of ML (Machine Learning). The job of machine learning system is to take input data and learn something from it and give output. In machine learning, the machine is trained by giving input data to a computer program that what kind of output is to be predicted.

Machine learning enables the machine to think, understand and learn like a human, so that the system or machine can easily complete a task like a human. In machine learning, algorithms are used to improve a computer or machine by providing the ability to think and understand the system. Its algorithm is used in many tasks such as medicine, email filtering, speech recognition and computer vision etc.

What are the types of machine learning?

Machine learning algorithms are divided into some categories. Now let's get complete information about the category of Machine Learning Algorithms here. 

1. Supervised Machine Learning Algorithms: In this type of algorithm, the machine applies what has been learned in the past to new data, using labeled examples to predict future events.

By analyzing a known training dataset, this learning algorithm generates a kind of inferred function that can easily make predictions about the output values. Given enough training, the system can provide targets for any new input. This learning algorithm compares the resulting output to the correct intended output and finds errors so that they can modify the model accordingly.

2. Unsupervised Machine Learning Algorithms: This type of algorithm used when the information to be trained is neither classified nor labeled. Unsupervised learning studies how systems can infer a function so that they can describe a structure hidden from unlabeled data.

The system does not describe any true outputs, but it explores the data and pulls these estimates from its dataset to describe hidden structures with the help of unlabeled data. 

3. Semi-supervised machine learning algorithm: This algorithm falls between both supervised and unsupervised learning. Since for the training they use both labeled and unlabeled data - usually a small amount of labeled data and a large amount of unlabeled data.

Systems that use this method can very easily significantly improve learning accuracy. Generally, semi-supervised learning is chosen when the acquired labeled data requires efficient and relevant resources so that it can train and learn from them. Otherwise, no additional resources are needed to obtain unlabeled data.

4. Reinforcement Machine Learning Algorithm: It is a type of learning method that searches for errors and rewards along with building actions with their environment. The most relevant features of reinforcement learning are trial and error discovery and delayed reward.

This method allows machines and software agents to automatically determine any ideal behavior in a specific context and so that it can maximize their performance. Simple reward feedback is very much needed for any agent to learn which action is the best; It is also called reinforcement signal.

Machine learning can analyze massive quantities of data. Provides more accurate and faster results to determine whether there are profitable opportunities or dangerous risks, as well as additional time and resources to properly train them.

One thing no one can deny is that if we combine machine learning with AI and cognitive techniques, large amounts of information can be processed more effectively.

Categorization of Machine Learning on the basis of Required Output

This is another type of classification of machine learning tasks, when we only consider the desired output of the machine-learning system. So let's know about it-

1. Classification: When the input is divided into two or more classes, and the learner builds a model that assigns unseen input to any one or more (multi-label classification) classes. This is typically tackled  with in a supervised manner.

Spam filtering is an example of classification, where the inputs are email (or any other) messages that have "spam" and "not spam" classes.

2. Regression: This is a type of supervised problem, a case where the outputs are continuous rather than discrete.

3. Clustering: In this classification a set of inputs is divided into groups. The groups cannot be known in advance, except in its classification, which makes it a generally unhelpful task.

Always remember that machine learning comes to the fore only when problems cannot be solved in specific ways.

Why is machine learning used?

Nowadays machine learning is being used in most of the sectors but here we will discuss some special places where machine learning is used.

1- Machine learning is used to recognize the image of objects, persons, places and pictures. Face detection technology is used to identify photos.

2- It is used to do voice search, in which the user can get information about anything by speaking into the mic. Big search engines like Google provide voice search facility to the user by using machine learning.

3- It is used to know the traffic conditions. Here we will try to understand through an example. If a user wants to go to a new place then he uses Google Maps which shows him the correct route as well as provides information about the traffic conditions which is possible only because of machine learning.

4- It is used in entertainment and e-commerce by companies like Amazon and Netflix to provide output data to the user in exchange for input.

For example, whenever a user searches for a product on Amazon, he gets to see multiple products in the search result. This has been possible only because of machine learning, in which the user provided input data to Amazon, in return the user got the output data.

5- Machine learning is used in medical science to diagnose diseases. Machine learning is used in medical science to detect disease in simple language, with the help of which diseases of the patient can be detected and that disease can be treated and prevented.

6- Machine learning is used to predict the stock in the stock market, which share will have a low value and which share will have a high value, thereby reducing the chances of loss to the investor. Although this figure is not accurate, but the investor does have an idea.

7- It is used for online fraud detection, with the help of which both the data and money of the user remains safe. Machine learning can easily detect fake accounts and fake IDs, thereby reducing the chances of fraud. Moreover, machine learning helps the user to completely secure all the online transactions.

8- It is used to create a virtual personal assistant. Virtual Personal Assistant is a tool that receives commands through the voice of the user and gives output to the user through that command. Examples of this are Google Assistant, Alexa, Cortana, Siri.

9- It would not be wrong to say that nothing is possible without machine learning.

What is AI vs machine learning?

Presently Artificial Intelligence and Machine Learning are being used extensively in every field. Often people use these two words interchangeably. But let us tell you that the concepts of these two are completely different.

Artificial Intelligence: Two words used in Artificial Intelligence "Artificial" and "Intelligence". Artificial means that which is made by man and which is not natural. Whereas intelligence means the ability to think or understand.

Many people have this misconception that artificial intelligence is a system, but in reality it is not true. AI is implemented in the system. Although there are many definitions of AI, one definition is that "it is a type of study in which a computer or some other system can be trained so that these computers can do the work themselves. Right now machines have not become faster than humans, it is the intelligence of humans that  converts into fastest machines.

To get complete information about Artificial Intelligence, open this link : 

# What is Artificial Intelligence with Example?

Machine Learning: It is a type of learning in which the machine learns itself without being explicitly programmed. It is a type of application of AI that gives the system the ability to learn and improve on its own from experience. Here we can make a program which is created by integrating the input and output of a single program.

A simpler definition of machine learning is also that "machine learning" is an application in which the machine learns from experience such as some class task T and a performance measure P if the learners are performing in the task that is in the class and which is called P. is measured and corrected by experience.

What is the difference between AI and ML?

Here we will try to understand the difference between Artificial Intelligence and Machine Learning.

Artificial Intelligence

➧ Full name of AI is Artificial intelligence, where intelligence can be defined as, an ability where knowledge is acquired and applied.

➧ The aim of AI is to increase the chance of success and not its accuracy.

➧ AI perform the smart work like a computer program .

➧ The main goal of AI is to emulate natural intelligence so that it can solve complex problems.

➧ AI itself is decision making.

➧ AI develops a system which can mimic humans so that it can respond properly in any circumstances.

➧ Finding the optimal solution of a problem is the top priority of AI.

Machine Learning

➧ The full name of ML is Machine Learning which is defined as a type of specialty by which knowledge and skills are acquired from experience.

➧ The aim of ML is to increase accuracy and they do not pay much attention to success.

➧ ML is a simple concept machine that accepts data and learns from it.

➧ The main goal of ML is to learn data from a certain task so that it can maximize the performance of the machine for that specific task.

➧ ML allows a system that can learn new things from its own data.

➧ ML is more involved in creating self learning algorithms.

➧ ML believes in finding a solution to any problem whether it is optimal or not.

➧ ML (Machine Learning) leads to knowledge.

How does machine learning work?

You might find it very interesting to hear how machine learning works. So let's know. You all must have done online shopping, where every day millions of people visit the e-commerce website and buy their favorite things.

Because here you see an unlimited range of brands, colors, price ranges and much more to choose from. But we also have a good habit that we do not buy our things this way, rather we see many things first and choose the right one. We have to open many items to see it.

Just this habit of ours is targeted by many advertising platforms, causing us to see items in the recommended list that we have been searching for before. In this you do not need to be surprised because no human is doing this, rather this task is programmed in such a way that it can record our activities.

For this machine learning is very useful for us because it reads our behavior and programs it accordingly from its experience. Therefore, the better the data available, the better prepared the learning models are. And accordingly the customers will also benefit.

If we talk about tradition advertising then newspapers, magazines, radio were prominent in it but now technology is changing and it is also getting smart which it is doing with targeted advertising (online advertising system).

This is a very effective way to show your ads only to the target audience, so that the conversion rate is high. It is not just about online shopping, but a lot of work is done in healthcare industries with machine learning.

Researchers and scientists have now developed models that train machines to identify major diseases, such as cancer. For this, they have given cancer cell images to these machines, which actually contain different variations of the cancer cell. Thereby these ML systems are used during the testing of patients to detect cancer cells. Which was taking a long time for humans to do. With this, a large number of patients can be tested for cancer in a very short time.

Apart from this Machine Learning is used for IMDb Rating, Google Photos, Google Lens. Although machine learning is used in many fields, it is up to you where and how you want to use machine learning.

To build the right model in machine learning, the computer needs the right amount of data like text, images, audio. The better and better the quality of data in it, the better will be the model learning. For this the algorithm is designed in such a way that the machine is able to perform future tasks from past experience.

Future of Machine Learning

The future of machine learning is indeed very bright. It is one of those techniques whose limits are determined by human beings like us. That is to say, the bigger our imagination, the more we can use machine learning for our tasks.

Apart from this, there are many such areas where the use of machine learning has started and its demand will increase even more in the future. The future of machine learning will be very bright and at the same time it will also help us to lead a comfortable life.

In many large hospitals, it is used only to detect cancer cells inside the patient, due to which timely treatment of the patient also saves his life. In the future, many things will become automatic with its use.

Many things that our old generation thought were impossible have now become our present. Also, with the passage of time, we are also experiencing things that were once a dream.

Looking at the current situation, it seems to me that machine learning can be like a catalyst in changing our future, which will prove to be very helpful for us. We have become so dependent on machine learning that life cannot be imagined without them.

For example, when we book a taxi in Ola or Uber, it already shows us the cost of the journey, how far, which route information. So we can say that the future of Machine Learning is going to be really very unique.

If you are a student then you should work on this topic, if you want to learn machine learning then you must have pre-requisite knowledge about some topics like- Statistics & Probability, Algebra, graph theory, Calculus, Programming Languages –> Python, C, C++, Ruby, MatLab.

Advantages of Machine Learning

1- Machine learning helps to upgrade and modernize the machine, so that the machine can think and understand like humans and can easily complete any work like humans.

2- It is able to predict the output data with the help of old input data, so that the user can know the future data. Although this data is not completely accurate, but the user definitely gets an idea of ​​what will happen in the future.

3- It helps in providing better education to the students, so that students can easily get higher level of education. Machine learning provides such technology to the students, with the help of which students can easily research about anything.

4- It helps in diagnosing the diseases of the patient, so that the correct disease can be detected. In today's time, doctors have such technology and equipment which can easily diagnose the diseases of the patient. All this has been possible because of machine learning.

5- It can review and analyze more data than humans and can also make more accurate predictions than humans. Apart from this, in machine learning, old data is stored as history, which is also used to predict future data.

6- In this, any work can be completed easily because most of the tasks in machine learning are automatic. The machine learning algorithm knows what to do at what time, which reflects its thinking power.

Disadvantages of Machine Learning

1- Machine learning requires a large amount of data to be fully trained, which greatly increases the chances of errors in the results. Due to the huge amount of data, it takes a long time to complete the tasks.

2- The algorithm of machine learning takes a lot of time to develop, which wastes a lot of time. In addition, the machine requires a large amount of resources to fully develop its algorithm.

3- In this, the output data can be estimated with the help of old input data, but this result or data may not be completely correct, due to which the user has to face problems.

4- The size of the data in this is very large, due to which the system requires a large amount of memory space.

History of Machine Learning 

In the year 1950, Alan Turing came up with the idea of ​​whether machines can also think like us humans, after this he created The Imitation Game, in which he created two humans and a computer in three different rooms and the first person was in form. of a text message. Used to ask questions, now both robots and humans were answering the question being asked by earlier humans, but now being in three different rooms, the question he was asking earlier, he could not understand whether he was answering or the computer. Alan Turing believed that if the first person could not understand whether the second person was answering or the computer, it would prove that computers could also think like humans.

In the year 1952, computer scientist Arthur Samuel created a game called Seven Checkers in the IBM company, in which the game was getting better by learning itself.

In 1958, computer programmer Frank Rosenblatt created an algorithm called the Perceptron, which was used to capture patterns and recognize patterns. Today's Finger Print Lock and Face Lock work on this principle.

In the year 1979, some people from Stanford University together made a robot named Stanford Cart. Its special feature was that it could change its course by detecting everything that came in its way.

In 1985, a computer programmer named Terry Sejnowski created a program called Net Talk, its special feature was that this program could learn and speak English words on its own. Later there were many changes in it and today we know it as Google Assistant and Siri.

You can also Read : 

# What is Data Mining? : Full Explanation

# Python : It's easy to learn

# Data Analytics and Jobs Opportunities

# What is Cloud Computing? Its Uses and Benefits

# Big Data : Know everything here

Last Word

Hopefully, after reading this article, you must have got answers to many of your questions related to machine learning like what is machine learning, where is it used and why is it important to learn it. If I say personally then you should learn ML because its demand will increase a lot in future, in today's time the demand for Machine Learning Engineers is very high.