What Is Machine Learning (ML) and How Does It Work?

Today, many big companies like Google, Facebook and Amazon use Artificial Intelligence (AI) . And resorts to Machine Learning to provide better experience to its users. But the question is, is this machine learning? What is Machine Learning (ML)? And how does it work? Also, what are the advantages, what are the disadvantages and what are the uses of machine learning? And what is its future? Let us know in detail.

मशीन लर्निंग क्या है?

मशीन लर्निंग (एमएल) कृत्रिम बुद्धिमत्ता (एआई) का एक अनुशासन है जो मशीनों को न्यूनतम मानवीय हस्तक्षेप के साथ भविष्यवाणियां करने के लिए पैटर्न की पहचान करते हुए डेटा और पिछले अनुभवों से स्वचालित रूप से सीखने की क्षमता प्रदान करता है।

मशीन सीखने के तरीके कंप्यूटर को स्पष्ट प्रोग्रामिंग के बिना स्वायत्त रूप से संचालित करने में सक्षम बनाते हैं। एमएल अनुप्रयोगों को नए डेटा के साथ खिलाया जाता है, और वे स्वतंत्र रूप से सीख सकते हैं, विकसित हो सकते हैं, विकसित कर सकते हैं और अनुकूलित कर सकते हैं।

मशीन लर्निंग पैटर्न की पहचान करने और एक पुनरावृत्त प्रक्रिया में सीखने के लिए एल्गोरिदम का लाभ उठाकर डेटा की बड़ी मात्रा से अंतर्दृष्टिपूर्ण जानकारी प्राप्त करता है। एमएल एल्गोरिदम किसी भी पूर्व निर्धारित समीकरण पर निर्भर होने के बजाय डेटा से सीधे सीखने के लिए गणना विधियों का उपयोग करते हैं जो एक मॉडल के रूप में काम कर सकते हैं।

Machine Learning

Machine Learning is a Futuristic Technology, which is in highest demand today. If you will do online job search. So you will also get to see the most jobs in these areas (Artificial Intelligence, Machine Learning and Data Science ). And the most money will also be available in these jobs. But this is only the beginning.

In the coming times, the use of these technologies will increase even more rapidly. Because the coming era is of smart machines. Everyone knows this. That’s why all the big companies in the world are investing in technologies like AI, AR ( Augment Reality ), VR ( Virtual Reality ) and Machine Learning. And are in a race to put themselves ahead of others.

आज, बड़े डेटा, IoT और सर्वव्यापी कंप्यूटिंग के उदय के साथ, मशीन लर्निंग कई क्षेत्रों में समस्याओं को हल करने के लिए आवश्यक हो गया है, जैसे कि

  • कम्प्यूटेशनल वित्त (क्रेडिट स्कोरिंग, एल्गोरिथम ट्रेडिंग)
  • कंप्यूटर दृष्टि (चेहरे की पहचान, गति ट्रैकिंग, वस्तु का पता लगाना)
  • कम्प्यूटेशनल बायोलॉजी (डीएनए अनुक्रमण, ब्रेन ट्यूमर का पता लगाना, दवा की खोज)
  • ऑटोमोटिव, एयरोस्पेस और मैन्युफैक्चरिंग (भविष्य कहनेवाला रखरखाव)
  • प्राकृतिक भाषा प्रसंस्करण (आवाज पहचान)

Overall Machine Learning is one of the top trending technologies of this time . And all the big companies from Google to Facebook are using machine learning. But how? After all, what is machine learning? ( What is Machine Learning In Hindi ) and how does it work? Come on, let’s understand.

What is Machine Learning?

Machine learning   is a part of Artificial Intelligence . In which Machine Learning Algorithms are studied. That is, work is done on various algorithms to teach machines. And through these algorithms the ability to learn and make the right decisions using the learned knowledge is developed in the machines. That  is, machines are taught to predict output using Past Experiences .

मशीन लर्निंग कैसे काम करती है?

एक मॉडल बनाने के लिए मशीन लर्निंग एल्गोरिदम को प्रशिक्षण डेटासेट पर ढाला जाता है। जैसे ही नए इनपुट डेटा को प्रशिक्षित एमएल एल्गोरिथम में पेश किया जाता है, यह भविष्यवाणी करने के लिए विकसित मॉडल का उपयोग करता है।

Data has the most important role in this. Because whatever Machine Learning Models learn, they learn from data only. And prepare the output (result) from the data itself. In such a situation, the more data a Machine Learning Model gets, the better output it gives. That is, the more data, the better the result.

Basically an ML model learns by analyzing the input data. That is, by finding the Patterns and Intrinsic Structures present in the data, it learns with the help of Algorithm. And takes appropriate decisions using his past experience. In this way we get Final Output. But if the model does not have enough data, the desired result will not be achieved. For this it is necessary to provide sufficient data to the model.

Purpose of Machine Learning

The purpose of machine learning is to create machines that can think, understand, learn and use the learned knowledge like humans. Also use your Past Experiences to find solutions to problems. and be able to work (independently) without human help. Such as Driverless Cars .

In simple language, the purpose of machine learning is to make machines smart. That is , to develop abilities like Human Brain (human-brain) inside the machines. So that they can improve themselves by learning like humans. And make the right decision by using your knowledge and experience. And most importantly! Can work without human intervention .

मशीन लर्निंग के प्रकार

मशीन लर्निंग एल्गोरिदम को कई तरीकों से प्रशिक्षित किया जा सकता है, प्रत्येक विधि के अपने फायदे और नुकसान हैं। इन विधियों और सीखने के तरीकों के आधार पर, मशीन लर्निंग को मोटे तौर पर चार मुख्य प्रकारों में वर्गीकृत किया जाता है:

Because there are many such areas, where it is almost impossible for humans to reach. such as Interstellar Space Missions . Similarly, it is a very challenging task to get the Rovers sent to other planets to run and work every inch by giving command from the earth. This is a very difficult and time consuming job. Machine learning is nothing less than a boon in such missions.
How does machine learning work?

When it comes to teaching machines, the question that definitely arises in the mind is how are machines taught? And how do they learn? How does machine learning learn? So come on! Step by step understand how machine learning works? The process of machine learning in Hindi  :-

1. Collecting Data

Whatever an ML program learns, it learns from the data that is given to it as input. That is, the learning, decision making and prediction of ML program all depend on this input data. That is why collecting data is the most important step of machine learning. Although it sounds as easy as it sounds, in reality it is not that easy.

For this, legitimate and reliable sources have to be searched. And absolutely Accurate and Quality Data has to be collected. Because the accuracy, authenticity and reliability of the data are the most important. It directly affects the results. That is why the more pure, accurate and high quality data a model will get , the more accurate it will give the result.

But on the contrary, if the model will get Incorrect, Irrelevant and Outdated data , it will give wrong and faulty results. That is, as the data is there, the same output will be available. That is why it is very important to provide accurate and authentic data to the model to get the desired results.

2. Prepare the Data

After collecting the data, it is prepared. That is, the machine learning model is prepared to be handed over. For this, all the data is kept together. and is distributed equally . Also, the entire data is arranged randomly  . so as not to affect the results.

Apart from this, the shortcomings in the data are removed. That is, Missing Values, Rows and Columns are corrected. And Duplicate and Unwanted Data is removed. After that the remaining data is divided into two sets. One Testing Set and another Training Set . Testing set is used to test the accuracy of the model. Whereas the Training Set is used to train the model.

3. Choosing a Model

After the data is prepared, the model is selected. That is, the right and relevant Machine Learning Model is selected according to the task. There are different Taskwise Models for this. As there is a separate model for Image Recognition. And different for Speech Recognition. Similarly, there are different models for the rest of the tasks. That is why it is very important to choose the right model.

4. Training the Model

After selecting the model, it is the turn to train him . That is, to teach the Machine Learning Model. For this, the already prepared data is input into the model. And he is trained. As the model learns, it becomes perfect. And gets better at predicting over time.

5. Evaluating the Model

After training the model is evaluated . That is, by testing it is seen that how accurate is it? For this, Fresh Data Input is done in the model. That is, the data which has been used earlier (in training), is not used. Rather, in the second step, the data which is saved as Testing Set is used. so as not to affect the results.

Because the model is well aware of the data that has been used in training . And has well understood the Patterns and Structures present in it. That’s why he will work with full accuracy. But this is not the right way to check accuracy . Because this does not allow accurate assessment of accuracy.

6. Parameter Tuning

After testing, it is the turn of Parameter Tuning. That is, of Hyperparameter Optimization . For this , the values ​​with the highest accuracy are found in the variables . That is, the values ​​that have the highest accuracy are detected. And by tuning these  parameters , the accuracy of the model is improved.

7. Prediction

After parameter tuning the model is completely ready. And he starts working with full accuracy. That is, even with Unseen Data , it starts making accurate predictions. That’s why it can be used for prediction . So in this way a machine learning model is trained and made decision-making.

मशीन लर्निंग के लिए सबसे अच्छी प्रोग्रामिंग लैंग्वेज कौन सी है?

अधिकांश डेटा वैज्ञानिक कम से कम इस बात से परिचित हैं कि  मशीन सीखने के लिए आर  और पायथन प्रोग्रामिंग भाषाओं का उपयोग कैसे किया जाता है, लेकिन निश्चित रूप से, मॉडल के प्रकार या परियोजना की जरूरतों के आधार पर भाषा की कई अन्य संभावनाएं भी हैं। मशीन लर्निंग और एआई टूल अक्सर सॉफ्टवेयर लाइब्रेरी, टूलकिट या सूट होते हैं जो कार्यों को निष्पादित करने में सहायता करते हैं। हालाँकि, इसके व्यापक समर्थन और चुनने के लिए पुस्तकालयों की भीड़ के कारण, पायथन को मशीन सीखने के लिए सबसे लोकप्रिय प्रोग्रामिंग भाषा माना जाता है। 

वास्तव में, गिटहब के अनुसार, पायथन अपनी साइट पर शीर्ष मशीन सीखने की भाषाओं की सूची में नंबर एक है। पायथन का उपयोग अक्सर डेटा माइनिंग और डेटा विश्लेषण के लिए किया जाता है और मशीन लर्निंग मॉडल और एल्गोरिदम की एक विस्तृत श्रृंखला के कार्यान्वयन का समर्थन करता है। 

Examples of Machine Learning

We see many examples of machine learning in our everyday life. But don’t pay attention to them. Because we didn’t know. But knowingly or unknowingly, we see the use of Machine Learning everywhere. Let us see some examples. The examples of machine learning in hindi :-

Google Search Engine

Google search engine is an integral part of our daily routine. And we use it everyday. But have you ever looked at Google’s Search Engine Results Page (SERP) ? If done, you would know that some ads are shown at the top of the page. Which are related to the same keyword that you search on Google! That is, whatever you search, you get to see in Ads .

For example, if you search by typing Best Machine Learning Course in India . So you will get to see advertisements only for Machine Learning Courses . This is, in fact, the miracle of machine learning.

Actually, the  Keyword or Query you type. He works as Input Data for Google. That is, Google uses it as an input. And learning from that gives you output. That is, shows Search Results and Relevant Ads. Similarly FAQs – Frequently Asked Questions are shown.

Amazon Shopping Portal

While shopping on Amazon, you must have noticed that the products you search or explore or like. The same you see in the recommendation. That is, recommended for shopping. Actually Amazon uses Machine Learning for this.

To put it simply, Amazon uses your data (keywords and activities). That is, whatever activities you do on the Amazon Portal. Such as searching products, clicking on them, reading specifications, reading ratings and reviews, sorting on the basis of colour, style and price etc. All these activities are recorded. And based on these products are recommended to you.

Youtube

YouTube uses machine learning to show relevant ads and suggest videos to its users. When you open the YouTube app, you get to see videos of the same type, which you generally watch or search. For example, if you watch Comedy Movies, then you get to see only comedy movies in the suggestion. Similarly, dance videos are suggested to users watching Dance Videos.

This is how ads are shown. That is, people who watch Movies and Web Series etc. They get to see more advertisements of Teasers and Trailers. And those who see Smartphone Reviews and Unboxing etc., are shown Ads of Smartphones. Similarly, people who see Mixed Content get to see all kinds of ads.

If I talk about myself, I do not have any interest in motor vehicles . Nor do I watch videos like this. That’s why I am never shown ads for vehicles on YouTube. Whereas my friend is fond of cars. And he is always watching videos of Cars and Bikes. That is why he gets to see advertisements of vehicles in every video.

मशीन लर्निंग उपयोग के मामले

प्राकृतिक भाषा प्रसंस्करण (एनएलपी)  और  कंप्यूटर विज़न (सीवी) जैसे अनुप्रयोगों के लिए एआई में प्रगति   वित्तीय सेवाओं, स्वास्थ्य सेवा और ऑटोमोटिव जैसे उद्योगों को नवाचार में तेजी लाने, ग्राहक अनुभव में सुधार करने और लागत कम करने में मदद कर रही है। मशीन लर्निंग में विनिर्माण, खुदरा, स्वास्थ्य देखभाल और जीवन विज्ञान, यात्रा और आतिथ्य, वित्तीय सेवाओं और ऊर्जा, फीडस्टॉक और उपयोगिताओं सहित सभी प्रकार के उद्योगों में अनुप्रयोग हैं। उपयोग के मामलों में शामिल हैं: 

  • उत्पादन।  भविष्य कहनेवाला रखरखाव और स्थिति की निगरानी
  • खुदरा।  अपसेलिंग और क्रॉस-चैनल मार्केटिंग
  • स्वास्थ्य और जीवन विज्ञान।  रोग की पहचान और जोखिम संतुष्टि
  • यात्रा और आतिथ्य।  अद्भुत मूल्य
  • वित्तीय सेवाएं।  जोखिम विश्लेषण और विनियमन
  • ऊर्जा।  ऊर्जा की मांग और आपूर्ति का अनुकूलन 

Machine Learning Algorithms

Various algorithms are used to teach machine learning models. Now you will ask what are these algorithms. So these are actually sets of rules , which the machines learn by following. Although there are many types of Machine Learning Algorithms. But if we talk about the main algorithms, then these are the following :-

1. Supervised Learning Algorithm

Models are taught on the basis of experiences in Supervised Learning Algorithms . That is, some datasets (examples) are given to the first model. And after that with the help of those examples Output Predict is done. For example, to identify Zebra, the model is given datasets like this : –

  • black and white
  • 4 to 5 feet height
  • striped body
  • quadrupedal animal
  • no horns

Once these examples are given, the model is taught that it is a zebra. That is, if any such details appear in the input data, then its prediction has to be done zebra.

After that, whenever the model sees any such details (black-white color, 4 to 5 feet height, striped body, four legs and hornless animal) in the input data, it will immediately recognize it with the help of its past experience. that it is a zebra. And gives correct prediction.

Unsupervised Learning Algorithm

This is the exact opposite of Supervised Learning Algorithm . That is, in this neither model is given any example. Nor is any output reported. Only input data is given. And on the basis of that the model makes predictions. That is, by analyzing the input data that the model gets, it makes predictions itself.

Semi-Supervised Learning Algorithm

Semi Supervised Algorithm is the link between Supervised and Unsupervised Algorithm . In this both types of data are used. That is, both Seen and Unseen data is given to the model in the form of Input Data. And on the basis of this data, the model makes predictions.

Reinforcement Learning Algorithm

This is the Most Complex and Self Dependent Algorithm . It is used in highly advanced and self-dependent robots and machines. In this algorithm, the model learns from the dynamic environment and feedback. And decides himself what to do? At the same time, he keeps improving himself continuously.

Take for example Google ‘s Self-Driving Car . This car always moves on new roads. And goes to new areas. That’s why he has to face new challenges every time. Such as unknown paths, turns of the way, slopes, climbs, signs, rivers, mountains, culverts, railway gates, etc. In such a situation, the car always learns new things. And keeps improving herself. At the same time, the mistakes he makes, taking them as feedback, keeps improving his mistakes.

So these were the Main Algorithms of Machine Learning, which are the most used. But apart from these, there are many algorithms, which are used in different tasks and applications. The names of these algorithms are Linear Regression , Logistic Regression, Naive Bayes Classifier, Decision Making , Random Forest, Cluster Analysis , SVM etc.

Machine Learning Applications

Now the question is what is the use of machine learning? What are the usage of machine learning? And what is it used for? So as you all know that machine learning is an integral part of our life. And we use it everyday. But how? Come, let’s know about some applications. Top Applications of Machine Learning :-

1. Robots

Machine learning is most commonly used in robots. Especially, in robots that work in place of humans (in restaurants etc.), machine learning has been used for a long time. But now is the era of Advanced Robots . Nowadays such robots are being made, which behave exactly like humans. Robots even more advanced and smarter than Sofia Robot .

Just imagine that there is such a robot at your house, which does the housework without giving your command. Cleans the whole house before you wake up in the morning. As soon as he gets up, he serves tea in front of you. Cooks food. Washes and irons your clothes. Does your office work. Provides tuition to the children. Understanding your Emotions and reacts accordingly . And after being discharged, it charges itself and gets back to work, so how will it be?

Actually such advanced robots are being made nowadays. Who behave exactly like humans . They can decide on their own, when, what they have to do? And how to do? That is, these Robots do not need to give repeated commands for every task. All this is the result of Machine Learning.

2. Driverless Cars

All the driverless cars in the world today are all due to machine learning. Although till now only companies like Tesla , Waymo, Nauto, Argo AI, Optimus Ride and Motional  were making Self Driving Cars . But now almost all the car companies have jumped into this area. And everyone is making their own driverless cars.

Let us tell you that driver-less cars are completely dependent on machine learning. A car is well trained before it is put on the road. And with the help of Machine Learning Algorithms , it is taught how to control the car ? How to follow traffic rules? And how to drive safely ?

3. Product Recommendation

When you visit e-commerce sites like Amazon, Flipkart and Myntra! And search or explore any product! So some products are recommended to you by these companies . This is actually a result of machine learning. As we mentioned above in the example of Amazon, how these companies recommend products to their customers using machine learning.

4. Social Media

Machine learning is used for Friends Suggestion on social media . For example, take Facebook’s Friends Suggestion feature. This feature suggests the names of only those people whom you know or who are in your Contact List. Similarly, Twitter and Instagram also suggest you the accounts of the same people ! Those who are in your Contact List or whom you know.

Not only this, social media platforms like Facebook and Instagram also use Image Recognition . That is, even after recognizing the face of the person in the photo, Friends suggest. For example, if you upload a group photo to Facebook. So Facebook will scan the faces present in that photo and search it in its database . And the person who will not be your Facebook friend in them, his name will be suggested to you as a friend.

5. Online Advertisement

Machine learning is widely used in the advertising industry. For this your online behavior is tracked . And ads are shown to you according to your likes and dislikes. Like if you like green tea. Then you will be shown ads for the same.

Not only this, if you buy any product on regular basis (every week or every month). So you will start seeing the advertisement of that product as soon as the week or month is over.

Apart from this, if you will leave by searching or seeing a product. And even if you do not shop, you will get to see advertisements of the same product everywhere (Facebook, Twitter, Instagram, YouTube). For example, let’s say you went to Amazon. And searched Budget Laptop there. After that saw many laptops , but did not buy. Now wherever you go, you will get to see advertisements of Laptops.

6. Personal Assistants

You must be using Google Assistant , Alexa or Siri ? These are all Personal Assistants , who assist you as well as entertain you. And many other things too. Such as calling someone, messaging, sending email, turning on/off Light/AC, ordering food, booking a taxi, booking a flight and so on. They actually use Machine Learning.

Virtual Assistant accepts the data (commands) given by the user as input data. And gives output using Speech Recognition, Speech to Text Conversion , Text to Speech Conversion and Natural Language Processing. Also, he keeps learning new things all the time. And keeps improving himself.

7. Image Recognition

One of the major uses of machine learning is for image recognition. It is used on a large scale. Governments use it to identify their citizens ( Aadhaar Card ). Along with this, the Intelligence Department and the Police also use it to ensure the identity of the criminals. Apart from this, the feature of Face Unlock which is in our phone. He is also an example of this.

8. Spam Filtering

Machine learning is used for spam filtering not only in email , but also in social media and websites . However, Spam Filtering for email is nothing new. But now it has improved a lot. Similarly, Spam Comments are filtered on Blogs and Websites.

9. Language Translation

You must have used Google Transliteration . Especially when you have to translate from English or any other language to Hindi. But have you ever wondered how Google would translate so many languages?

Actually this is the wonder of Machine Learning. The Sentence or Paragraph you enter. Google translates it into another language with the help of Natural Language Processing Algorithm .

10. Fraud Detection

Nowadays, most of the people do things like Shopping to Mobile Recharge, TV Recharge, Bill Payment and Fund Transfer online. And also prefer to do money transactions online. That is why online frauds have increased a lot. But with the help of Machine Learning these Frauds can be stopped.

Actually most of the companies nowadays, which provide services like Online Trading , Online Payment and Digital Wallet . They  use Machine Learning for Fraud Detection . That’s why when the user makes an online payment, the machine learning model detects whether it is safe or not?

If someone tries to attack Hacker or Fraudster . So the machine learning model detects it instantly. And alerts the user. It also stops him from moving forward. So that the user’s data remains safe and there is no fraud with it. In this way machine learning plays an important role in preventing online frauds.

Artificial Intelligence Vs Machine Learning

Many people think of artificial intelligence and machine learning as one and the same. But this is not true. Actually, there is a big difference between these two. Come, let us know what are the differences between Artificial Intelligence vs Machine Learning :-

Artificial Intelligence

Artificial Intelligence is made up of two words – Artificial and Intelligence . Here Artificial means artificial or man-made and Intelligence means intelligence. Thus Artificial Intelligence means artificial intelligence. That is, the intelligence or understanding developed by humans. The purpose of artificial intelligence is to develop the ability to think and make decisions in machines. So that they can work independently without human help.

Machine Learning

Machine learning means to learn or learn by machines. It is actually a part of Artificial Intelligence. The purpose of which is to develop the ability of learning in machines . That is, the way we humans learn from the environment and experiences around us. Similarly, the ability to learn has to be developed in machines too. So that they can take the right decision. And work with complete accuracy.

Don’t understand? Come, let me explain you in easy language. Actually Artificial Intelligence is a brain. And learning machine learning using that brain. That is, the way both brain and learning are different things. In the same way AI and ML are also different things, which are related to each other like brain and learning. But the concept of both is different.

Future of Machine Learning

Today Machine Learning is being used in almost every field. That is , machine learning is being used in every sector from Robotics to eCommerce, Banking, Health Care, Education, Marketing, Insurance, Telecommunication, Agriculture, Anatomy, Bioinformatics and Economics. And getting the benefit of it. But this is just a start.

Today the speed at which the use of Machine Learning is increasing. Looking at that, it seems that the future of machine learning is very bright. And in the coming time its use will increase even more rapidly. Also, the areas which are deprived of the benefits of machine learning, will also start using machine learning.

However, due to this many jobs will be lost. But new jobs will also be created in their place. As nowadays there is most demand for jobs in the field of Artificial Intelligence, Machine Learning, Data Science , Blockchain , Virtual Reality and Augment Reality. And in future this demand will increase even more rapidly. But the biggest concern about AI and ML is that machines are out of control and humans are taking over.

Machine Learning : Summary

Machine learning is a part of artificial intelligence, which gives the ability to learn computer programs . Its purpose is to build Smart Machines . Machines that can learn from their experiences like humans. And use the knowledge learned to make the right decisions.

I hope, through this article, what did you know about Machine Learning? How does it work? What are its benefits? And what are the uses? You will get complete information about this subject. If you liked this article then like and share it.

Machine Learning : FAQs 

Question 1. What is Machine Learning?

Answer:  Machine learning  is a branch of Artificial Intelligence  . In which Machine Learning Algorithms are studied. That is, the ability to learn and use the knowledge learned is developed in machines. So that machines can also take right decisions using their Past Experiences like humans.

Question 2. What is the meaning of machine learning?

Answer: Machine learning means learning by machines. That is, the way we humans learn from the environment and experiences around us. Similarly, machine learning is called machine learning.

Question 3. What are the types of machine learning?

Answer: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning and Reinforcement Learning are the main types of Machine Learning.

Question-4. Are Artificial Intelligence and Machine Learning the same thing?

Answer: No. Artificial Intelligence and Machine Learning are both different concepts.

Question-5. What is the difference between Artificial Intelligence and Machine Learning?

Answer: There is a big difference between artificial intelligence and machine learning. The concept of these two is different. Artificial intelligence is a brain, whereas machine learning is ‘learning’ with the help of that brain. That is, just as learning is a part of the brain, in the same way machine learning is also a part of artificial intelligence.

Question-6. What is the use of machine learning?

Answer: Machine learning has many uses. Such as Speech Recognition , Face Recognition , Product Recommendation, Natural Language Processing and so on.

Question-7. In what areas is machine learning being used today?

Answer: Today Machine Learning (ML) is being used in almost every field . Even in areas like Banking to eCommerce, Marketing, Insurance, Health Care, Share Market, Education, Agriculture, Telecommunication, Bioinformatics, Anatomy, Space Missions and Robotics, Machine Learning is being used extensively.