Machine Learning and it’s impact on our generation

The words ‘Machine Learning’ consists of two words “Machine” and “Learning”. The word ‘Machine’ stands for an apparatus that can perform any kind of task, like a computer, printer and so on. When we add the word ‘Learning’ after it, together it means how a machine learns.

So What is “Machine Learning” ?

Although There isn’t a well acceptable definition ,but let me show you some . Example How people Define Machine Learning

According to Arthur Samuel (1959):-machine Learning is the “Field of study that gives computers the ability to learn without being explicitly programmed’’.

According to Tom Mitchell (1998):-A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

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As you all know A Better way to understand ,I am trying to explain with some Example :-

Here you can see the upper one in which we give data & program to the Computer then we get output, this is something like How programming works. Let suppose you make a program to add two number and if you given three input instead of two ,you will get an error.It means programming fixed to a certain limit or is like (‘chatur of 3idots’ ,who can only memorise the things) but if you see the lower one where we give data & output to the Computer then we get the program ( for example we give data 2 & 5 and output 7, we give data 8 & 3 and output 11 (Training data )computer try to understand what is happening here and after sometime computer learn “ooh there is Addition” After that we will give data 1 & 2 (Testing data) and we get result 3, it means my computer learn how to add , Now if we gave more than two input ,we get result because my machine learn how to add numbers.

**Look this Example , this really help you to understand “What is Machine Learning ?”

For example, I take you to a pet shop which has 4 dogs and 4 cats and I teach you which of them is a dog and which of them is a cat. After this, I bring in a different breed of dog which was not present in those 8 animals. You can predict that it is a dog. So here I didn’t teach you that the other breed of the animal was a dog but you were still able to predict it as you were already taught previously. A machine works in a very similar manner. The 8 animals are equivalent to the ‘training data’ as it is used for training the machine. The other breed of dog is equivalent to the ‘testing data’ and is used for testing our machine

Machine Learning Methods

In machine learning, tasks are generally classified into broad categories. These categories are based on how learning is received or how feedback on the learning is given to the system developed

Two of the most widely adopted machine learning methods are supervised learning which trains algorithms based on example input and output data that is labeled by humans, and unsupervised learning which provides the algorithm with no labeled data in order to allow it to find structure within its input data

1. Supervised Machine Learning :-

In supervised learning, the computer is provided with inputs with their desired outputs.The purpose of this method is for the algorithm to be able to “learn” and draw the patterns , After that you will give a completely new input and machine predict the output.

Example:-

Here in the table there are four columns in which three of them is input(i.e Age, salary, Gender) and the output is “Purchased”.In this example there is a data of a person (Age of the person, salary of the person ,Gender of the person and purchased (if person purchase indicated by ‘Y’ and if he or she not purchased indicated by ’N’)With this data put into an unsupervised learning algorithm ,After this if a new data of a person is came to machine (Age =25, Salary=8L ,Gender=M) then machine definitely predict whether the the new person purchased or not .

# Regression:-

This is a type of problem where we need to predict the continuous response value . we predict the number which can vary from -infinity to +infinity
Example , We can predict -
1. what is the price of House in the Specific city ?
2. what is value of the Stock ?
3. How many total Run can be made in a Cricket game ?

# Classification:-

This is a type of problem where we predict the categorical response value where data can be separated into specific “Class”,Classification can be multi class(more than two class) we predict one of the values in a set of values .
Example ,we can predict-
1.Whether the Mail is Spam or Not ?
2.Will it rain today or Not ?
3.Whether a person purchase or Not ?

2. UnSupervised Machine Learning:-

In unsupervised learning, There is only input no output, so the learning algorithm is left to find commonalities/grouping among its input data.

The Aim of unsupervised learning is too discovering hidden patterns within a input dataset, but it may also have a goal of feature learning, which allows the computational machine to automatically discover the representations that are needed to classify raw data.

Example:-

Unsupervised learning is commonly used for transactional data. You may have a large data of customers and their purchases list, but as a human you will likely not be able to make sense of what similar attributes can be drawn from customer profiles and types of purchases. With this data put into an unsupervised learning algorithm, it may be determined that men of a certain age range who buy Protein powder are likely to be Gymnast, and therefore a marketing campaign related to Gym and products related to Gym can be targeted to that audience in order to increase their number of purchases.

# Clustering:-

Clustering is a type of problem where we group similar things together
Example:- Google Photos

You Notice in Google Photos our photos are automatically categorized into different group like(Friends, Family ….and so on )

#Association:-

Method for discovering interesting relations or pattern between variables in large databases.
Example:-

In supermarket if we collect data of all previous sales and put all the data in our algorithm ,then we found {onion, potatoes}={Burger} in the sale data of Supermarket which indicates that ,if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing

3. Reinforcement Learning: Reinforcement Learning is a process of machine learning in which the machine algorithm learns from its previous experience to give the best output.
A reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximise its reward and minimise its penalty.

Example:-

Self-driving car or a program playing chess, interacts with its environment, receives a reward state depending on how it performs, such as driving to destination safely or winning a game. Conversely, the agent receives a penalty for performing incorrectly, such as going off the road or being checkmated.

Impact on our generation:-

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world’s leading companies

1. Intelligent Gaming:-

Some of you may remember 1997 when IBM’s Deep Blue defeated Gary Kasparov in chess. But if you weren’t old enough then, you might remember when another computer program, Google DeepMind’s Alpha go, defeated Lee Sedol, the Go world champion, in 2016
Go is an ancient Chinese game, much more difficult for computers to master than chess. But AlphaGo was specifically trained to play Go, not by simply analyzing the moves of the very best players, but by learning how to play the game better from practicing against itself millions of times

2. Google

Google Images:-

Google is one of the pioneers of deep learning from its initial foray with the Google Brain project in 2011. Google first used deep learning for image recognition and now is able to use it for image enhancement.

YouTube:-

Google has also applied deep learning to language processing and to provide better video recommendations on YouTube, because it studies viewers’ habits and preferences when they stream content

Google Photos:-

Categorization of different pictures based on different places, differentiating each person’s pictures from the given pictures, making beautiful collages from the pictures.You Notice in Google Photos our photos are automatically categorized into different group like(Friends, Family ….and so on )

Google Map:-

Google Maps today introduced live bus delay forecasts powered by machine learning in hundreds of major cities around the world. Google gets real-time data on bus locations from some transit agencies today.

Google Assistant:-

Google’s assistant is nothing but googles voice assistant. When it announced, it is just an extension but now it developed to personal use like voice commands like ok google. Initially, google picked out the related data and knew where you work, what are your meeting and travel plans, etc. so that it can info about the things which are important to you.

Gmail:-

Have you ever wondered how mails are categorized as primary, social, promotions and updates. This is where Machine Learning is used to categorize the mails. Spam folder is also a work of Machine Learning as it classifies the mails which are spam or not and put the spam mails in the spam folder.

3. Social Media

Facebook:-

Deep learning is helping Facebook draw value from a larger portion of its unstructured datasets created by almost 2 billion people updating their statuses 293,000 times per minute. Most of its deep learning technology is built on the Torch platform that focuses on deep learning technologies and neural networks.

Add Friend suggestion:-
Facebook uses the clustering algorithm to find friends on the basis of current Workplace or city , school and college etc.

Advertisement on Facebook:-
If you browse for a certain product in the E-Commerce websites, Facebook will show an ad related to that product on your news feed. That is implemented using Machine Learning

4. Entertainment

Bollywood :-

The main purpose of using Machine Learning in Bollywood to predict the Success of movie , Using Logistics Regression algorithm, a supervised machine leaning algorithm, a model has been developed for predicting Hit Bollywood movies. The model developed has been able to predict the Hit movies with an accuracy up to 80%. The proposed model is unique in the sense that it uses MusicRating of a movie as a predictor which is a unique feature of Bollywood movies.

Netflix :-

Big data analytics is helping Netflix predict what its customers will enjoy watching. They are also increasingly a content creator, not just a distributor, and use data to drive what content it will invest in creating. Due to the confidence they have in the data findings, they are willing to buck convention and commission multiple seasons of a new show rather than just a pilot episode

5. HeathCare :-

AI and deep learning is being put to use to save lives by Infervision. In China, where there aren’t enough radiologists to keep up with the demand of reviewing 1.4 billion CT scans each year to look for early signs of lung cancer. Radiologists need to review hundreds of scans each day which is not only tedious, but human fatigue can lead to errors. Infervision trained and taught algorithms to augment the work of radiologists to allow them to diagnose cancer more accurately and efficiently

6. Manufacturing :-

Self Driving Car :-

With the help of reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximise its reward and minimise its penalty

Self Driving BMW (Best Example of Reinforcement Learning)

BMW has big data-related technology at the heart of its business model and data guides decisions throughout the business from design and engineering to sales and aftercare. The company is also a leader in driverless technology and plans for its cars to deliver Level 5 autonomy — the vehicle can drive itself without any human intervention — by 2021

Conclusion

For the past few years, Machine Learning is most important in every field.In every field such as Social media , Bollywood, Manufacturing,Healthcare, and so on .Each and Every company want to hire machine Learning engineers because Everyone want work smartly .So the demand for jobs in this field is so high. So, my advice is study Machine Learning and make it as a long term goal .