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    What is Machine Learning and how does it work?    
 
 

Introduction to Machine Learning 

 




What is Machine Learning and how does it work?

Do you know what this machine learning is?  It sounds like a very technical term in hearing.  But if you understand about it properly then it is a very easy funda which is used in almost all the places nowadays.

 This is such a type of learning in which the machine itself learns many things without explicitly programmed it.  This is a type of application of AI (Artificial Intelligence) which provides this ability to the system so that they automatically learn from their experience and improve themselves.

 It may not seem possible to hear, but it is true because nowadays AI has become so advanced that it can make machines do many such things which were not even possible to imagine before.

 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 get complete information about it.

 There are thousands of such advantages of Machine Learning that we use in our daily work.  That's why today I thought why not provide you people with information about what is Machine Learning and how it works, which will make it easier for you to understand it better.  So without delay let's start and know about what is machine learning.

 

 

What is machine learning 

 Machine learning as I have already told that it is a type of application of artificial intelligence (AI) which provides this ability to the systems so that they can automatically learn and improve themselves if needed.

 To do this, they use their own experience and not explicitly programmed.  Machine learning always focuses on the development of computer programs so that it can access the data and later use it for its own learning.

 In this learning begins with observations of data, for example direct experience, or instruction, to find patterns in data and make it easier to make better decisions in the future.

 The main goal of Machine Learning is how computers automatically learn without any human intervention or assistance so that they can adjust their actions accordingly.

 

 

Types of Machine Learning Algorithms 

Machine learning algorithms are often divided into some categories.  Let us know about it and about their types.

 1.  Supervised machine learning algorithms: In this type of algorithm, the machine applies what it has learned in its past to new data, in which they use labeled examples so that they can predict future events.

 By analyzing a known training dataset, this learning algorithm produces a kind of inferred function which can easily make predictions about the output values.

 The system can provide target for any new input on giving them sufficient training.  This learning algorithm also compares the resulting output with the correct, intended output and finds errors so that they can modify the model accordingly.

 2.  Unsupervised machine learning algorithms: These algorithms are 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 hidden structure from unlabeled data.

 This system does not describe any right output, but it explores the data and draws these inferences from their datasets so that they can describe the hidden structures with the help of unlabeled data.

 3.  Semi-supervised machine learning algorithms: This algorithm comes between both supervised and unsupervised learning.  Since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data.

 Those systems that use this method can very easily improve the learning accuracy considerably.

 Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources so that it can train them and learn from them.  Otherwise, additional resources are not required to acquire unlabeled data.

 4.  Reinforcement machine learning algorithms: It is a type of learning method that interacts with its environment by producing actions as well as discovering errors and rewards.

 Trial and error search and delayed reward are all the most relevant characteristics of reinforcement learning.

 This method allows machines and software agents to automatically determine any ideal behavior that is within a specific context and so that it can maximize their performance.

 Simple reward feedback is very much needed for any agent so that it can learn which action is best;  This is also called reinforcement signal.

 Machine learning can analyze massive quantities of data.  Delivers faster, more accurate results than can be found where there are profitable opportunities or dangerous risks, plus it may take additional time and resources to train them properly.  .

 One thing no one can deny is that if we combine machine learning with AI and cognitive technologies, then large volumes of information can be processed in a more effective way.

 

 

 On the basis of categorization required Output of Machine Learning

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

 1. Classification: When inputs are divided into two or more classes, and produce a model to the learner that assigns unseen inputs to any one or more (multi-label classification) classes.  It is typically tackled in a supervised way.

 Spam filtering is a type of classification, where the inputs are email (or any other) messages with the classes being “spam” and “not spam”.

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

 3. Clustering: Here a set of inputs is divided into groups.  Groups cannot be known in advance, except for its classification, which makes it a typically unsupervised task.

 Always remember that Machine Learning comes into picture only when problems cannot be solved with typical approaches.

 

 

Artificial Intelligence VS Machine Learning

 Artificial Intelligence and Machine Learning are currently being used extensively in industries.  Often people use these two terms interchangeably.  But let me tell that the concepts of these two are completely different.  So let's know about the difference between these two. 

  Artificial Intelligence: Two words have been used in Artificial Intelligence “Artificial” and “Intelligence”.  Artificial means that which has been made by humans and which is not natural.  Whereas Intelligence means the ability to think or the ability to understand.   There is a misconception in the minds of many people 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 also that "It is a type of study in which it is known that how computers or any other system can be trained so that these computers themselves can do what humans currently do."  Doing much better.”   That's why it is intelligence where we can add all the capabilities of humans to machines. 

  Machine Learning: Machine Learning is a type of learning in which the machine learns on its own without explicitly programmed it.   This is a type of application of AI which gives that ability to the system so that they can automatically learn and improve from their experience.  Here we can generate a program that is designed by integrating the input and output of the same program.   A simple definition of Machine Learning is also that "Machine Learning" is an application in which the machine learns from experience E wrt some class task T and a performance measure P if the learners' performance is in that task which is in the class and which  P is measured and improves by experiences.”

 

 

 What is the difference between Machine Learning and Traditional Programming?

 1.  Traditional Programming: Here we feed DATA (Input) + PROGRAM (logic) into the machine, to run the machine and finally we get the output according to our data and program. 

  2.  Machine Learning: While here we feed DATA(Input) + Output into the machine, and on running it, the machine develops its own program (logic) during training, which can be later evaluated during testing.  could.

 

 

How machine learning works 

 

 You may find it very interesting to hear how Machine Learning works.  Then let's know.  All of you must have done online shopping, where millions of people visit ecommerce websites every day and buy their favorite things.

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

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

 Machine learning is very useful for this thing because it reads our behavior and accordingly programs itself from its experience.  Therefore, the better the data available, the better the learning models will be ready.  And the customers will also benefit accordingly.

 If we talk about Tradition Advertisement then newspapers, magazines, radio were prominent in it, but now technology is changing and it is also becoming smart which it is doing with Targeted Advertisement (Online Ad System).

 This is a very effective method that shows their advertisements only on the target audience, so that the conversion rate is high.

 It is not only about online shopping, but a lot of work is done in health care industries with machine learning.

 Researchers and scientists have now prepared such models that train machines to recognize major diseases like cancer.  For this, they have fed cancer cell images to these machines, which in reality have different variations of cancel cells.

 Due to which these ML systems are used to detect cancer cells during the tests of patients.  Which was a lot of time taking to do for humans.  Due to this, cancer test of a large number of patients can be done in a very short time.

 Apart from this, Machine learning is used for IMDB ratings, Google Photos, Google Lens.  It just depends on you where and how you want to use Machine Learning.

 To make the right models in Machine Learning, computers need the right amount of data such as text, image, audio.  The better and better quality data is in it, the better model learning will be.  For this, algorithms are designed in such a way that from past experience the machine is able to perform future actions.

 

 

Advantages of Machine Learning

 By the way, there are many advantages of Machine Learning about which we hardly know.  But here I know about some important advantages.

 i.  Machine learning has many wide applications such as in the banking and financial sector, healthcare, retail, publishing etc industries.

 ii.  Google and Facebook are able to push relevant advertisements using machine learning.  All these advertisements are based on the past search behavior of the users.  That's why it is also called targeted ads.

 iii.  Machine learning is used to handle multi-dimensional and multi-variety data that too in dynamic environments.

 iv.  By the use of machine learning, there is time cycle reduction and efficient utilization of resources can also be done.

 v.  Even if one wants to provide continuous quality, large and complex process environments, there are still some such tools in it due to machine learning.

 vi.  By the way, many things come under the benefits of Machine Learning, which can be of great use to us, such as the development of autonomous computers, software programs, etc.  As well as such processes which can later be automation of tasks.

 

 

 Disadvantages of Machine Learning

 By the way, there are also some disadvantages of Machine Learning, about which let us know. 

  i.  A major challenge of machine learning is Acquisition.  In which, data is processed based on different algorithms.   And it is processed before using it according to the input of any respective algorithms.  Therefore it has a significant impact on the results which are achieved or obtained. 

  ii.  Another word is interpretation.  Which means that the results are also a very major challenge.  From this it has to be determined that how much is the effectiveness of machine learning algorithms.  

 iii.  We can say that the uses of machine algorithm are limited.  Also there is no surety that algorithms will always work in all imaginable cases.   Because we have seen that in most cases machine learning fails.  Therefore it is very important to have some understanding about the problem so that the right algorithm can be applied.  

 iv.  Like deep learning algorithm, machine learning also requires a lot of training data.  We can say that it is very difficult to work with such a huge amount of data.   

v.  A very notable limitation of machine learning is that they are more susceptible to errors.  Brynjolfsson and McAfee have told about its actual problem that when they make an error, then it is very difficult to diagnose and correct them.  This is because it has to pass under the underlying complexities.  

 vi.  There are very few possibilities in this with machine learning system to make immediate predictions.  Also do not forget that they learn mostly from historical data only.  Therefore, the bigger the data and the longer the ML is exposed to the data, the better it can perform.

   vii.  Not having much variability is also another limitation of machine learning.

 

Future of machine learning

 The future of machine learning is really very bright.  This is one of those technologies whose limits are set by humans like us.  This means to say that the bigger our imagination, the more we can use machine learning for our works. 

  Many things which our older generation used to think impossible are now our present.  Also, with the passage of time, we are also expereince of such things which were once a dream.  

 Personally, I think that machine learning can be like a catalyst which is going to be helpful for us to change our future.  We have become so much dependent on machine learning that life without them seems out of imagination.

   For example, when we book a taxi in Ola or Uber, then it already shows us information like the cost of the trip, how much distance, which route.  That's why we can say that the future of Machine Learning is going to be really unique.

 

        


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