In today’s scenario, Artificial Intelligence and Ma1chine-learning both are the broad concepts, used in every era of Industries, Healthcare, Retail, Travel, Finance, Social Media, and Research etc. These are the buzz words and often seem to be used interchangeably.
Machine learning is a very interesting concept and also very helpful to make many real-time applications. Artificial Intelligence and Machine learning both are different from each other.
The concept of machines that allow machines to carry out the different task in a way that we would consider as “smart” know as Artificial intelligence.
Machine Learning is a current application of Artificial Intelligence. This approach based on the idea that we should be able to give machines access to data through which they can learn for themselves.
You have likely used machine learning Google Maps for suggesting Traffic Route, making an online purchase (on Amazon or Walmart) or in any other way. This post will try to give some basics about it and it’s applications where the ML technology works like a charm.
So, move forward and start learning something interesting…
A headline on New York Times about Machine Learning – “Brain like Computers, Learning from Experience”
Machine learning is a technology which is design to build intelligent systems. These systems have the ability to learn from the past experience. It can analyze historical data for further learning. It provides result according to its experience.
Machine learning is a field of computer science that gives the ability to learn without being explicitly programmed to computers.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of Machine learning. We can use algorithms on data to identify hidden patterns and gain insights. After, these patterns and gain insights help to improve the performance of the model.
Machine learning involves both statistics and computer science. Statistics allows one to draw inferences from the given data. To implement efficient algorithms we can also use computer science. It represents the required model, and evaluate the performance of the model.
Types of Machine Learning
We can divide Machine learning into three main categories, depending on the algorithm and its objectives:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
The supervised approach is similar to human learning under the supervision of a teacher. The teacher provides good examples for the student to memorize, and the student then derives general rules from these specific examples. Here, we use past data to train our machine and based on that data accuracy or we can say the result can be check.
We teach machine, based on that we test the accuracy of Machine Learning Algorithm on testing data. For example, to implement a Price prediction algorithms based on machine learning, we have 12 year past data of the Stocks. We use 4 years data for training to the machine and the rest 8 years data be used to test and analyze the accuracy of our algorithm. In this learning, we can verify the result or we can say outcome because we have input data and output data as well that’s why its called supervised learning.
In this learning, we do not target any particular outcome like supervised learning. Here, machine tries to group different objects based on the similar attribute they have, based on its learning.
Here, we have only input data. The outcome or the result totally depends on the intelligence of the machine. These are called unsupervised learning because unlike supervised learning there are no correct answers and there is no teacher.
This type of learning is very different from other types of machine learning. Here we do not train Machine, we simply write the algorithm in such a way that machine can take decisions and can improve the outcomes until it reaches an accuracy level. Markov Decision Process is the best example of Reinforcement Machine Learning. Reinforcement Learning is used especially in Decision-making process.
Machine Learning Applications
“Machine Learning – The Hot Technology That Helps in Nurturing the Growth of Cool Products”
Let’s Come on the more interesting part of the blog that is Real-Time Applications of the Machine learning.Here, we can see how machine learning covers all the era of the real world.In present time machine learning involved in Healthcare, Travel, Retail, Finance and many other sectors to make work easier with the low amount of resources.
There are 9 major applications of machine learning…
- Image Recognition
- Speech Recognition
- Medical Diagnosis
- Statistical Arbitrage
- Learning Associations
Image recognition is the most common application of the machine learning.There are many scenarios where we need to identify the object as a digital image. The measurements describe the outputs of each pixel in the image for Digital images.
This contains two types of recognition…
- Face Detection
- Character Detection
The translation of spoken words into a text known as Speech Recognition. It is also known as “automatic speech recognition”, “computer speech recognition”, or “speech to text”.
Here, we use a speech recognition software that recognizes spoken words and the measurement of the spoken words can be a set of numbers that represents signals.We can divide signals into portions that contain distinct words or phonemes.Speech recognition applications include voice user interfaces such as voice dialing, call routing, domotic appliance control.
For medical diagnosis, Machine learning provides techniques, methods, and tools that help in solving diagnostic and prognostic problems in a variety of medical domains.In medical diagnosis, it is used to find the existence of a disease followed by its accurate identification.
We have separate categories for each disease under consideration and one category is in the case where no disease is present. Here, machine learning improves the accuracy of medical diagnosis through analyzing data of patients.
In the medical era, Doctors and medical practitioners can take the advantages of machine learning in…
- Drug Discovery/Manufacturing
- Personalized Treatment/Medication
The application of machine learning in Finance domain helps banks offer personalized services to customers at lower cost, better compliance and generate greater revenue.Statistical arbitrage refers to automated trading strategies that are short-term and involve a large number of securities.
Machine learning helps in different aspects of Statistical arbitrage/finance domain…
- Fraud Detection
- Focused Account Holder Targeting
It is the process of developing various associations between products. A good example is how similarly unrelated products may reveal an association with another. When analyzed in relation to buying behaviors of customers.
In this application, we have done basket analysis which means, the association between the products people buy.This application used in retails. Retailers are implemented big data technologies like Hadoop and Spark to build big data solutions and need a solution that can analyze the data in real-time and provide valuable outcomes.Further that outcomes translate into tangible outcomes like repeat purchasing.
Machine learning algorithms intelligently process this data then automate the analysis to make this supercilious goal possible for retail giants like Amazon, Target, Alibaba, and Walmart.
Here, we can see two examples of Learning associations…
- For Product Recommendations
- For Improved Customer Service
We can easily understand classification with an example of Bank Loan Application. If a customer needs a loan from a bank then he/she apply for the same. Before any procedure bank firstly checks the customer background and all that the customer is able to repay the loan or not by considering some factors like earning, age, savings and financial history.
This information is taken from the past data. Hence, Seeker uses to create a relationship between customer attributes and related risks.
Prediction purely depends on the classification we made, based on some specific data or we can say some factors. After the classification, we can easily predict the probability of any event.
Let’s consider above example of bank loan, after the classification of the customer it will make it easy to predict the probability of the fault repays based on that factor analysis.
Consider another example related to the Retails. Earlier we were able to get outcomes of sales reports of the last month/last year/Any festival etc that known as historical reporting. It makes difficult to predict the upcoming sale demand but nowadays with the help of machine learning retailer and manufacturer able to predict the upcoming demand in a business.
Extraction means separation of desired information or data. It is an application of machine learning or we can say it is a process of extracting the structured data from the unstructured data. It takes input as a document and produces structured data. This output is in a summarized form such as an excel sheet or a table in a relational database. Nowadays extraction is becoming a key in big data industry.
The present time we have a huge amount of data but the problem is we have mostly unstructured data now the challenge arises to extract structured data from the unstructured data based on some patterns so that the data can be stored in RDBMS (Relational database management system).
Regression is a way that determines the statistical relationship between two or more variables. A change in a dependent variable associated with or depends on, a change in one or more independent variables.
In regression Application, we use the principle of machine learning to optimize the parameters. With the help of this, we can also cut the approximation error and calculate the closest possible outcome.
For function optimization, we can also follow machine learning principle and can choose to alter the inputs to get a better model. This gives a new and improved model to work and known as response surface design.
Some other Applications of Machine learning are as follows…
Machine Learning Applications in Retail
Retail sector widely used Machine learning for forecasting the generation of the upcoming sales for the business that makes work easier for both retailer and manufacturer as well.
Machine Learning Applications in Travel
Travel sector also used machine learning for dynamic pricing update, sentiment analysis etc.
Machine Learning Applications in Healthcare
Healthcare sector had different challenging issues but Machine learning resolved those issues. It has changed the whole scenario of the field. In present time it used in research work, medical diagnosis, drug discovery, manufacturing and it is also used to operate a patient.
Machine Learning Applications in Finance
It also used in finance to achieve the different task like-to detect any fraud, to review the best customer through holding the current or we can say updated information about the customer.
Machine Learning Applications in Media
Machine learning is the heart of all social media platforms for their own and user benefits. It personalizes news feed to rendering targeted ads.
Machine learning is an incredible concept or technology in the field of artificial intelligence. While it does have some limitations or some disadvantages as well. These Machine-Learning Applications have several ways to improve our lives and make easier our work.
It is a wide topic so we can not understand it within a blog. It consists of different approaches that help to achieve the desired task. We used these approaches to implement a model. We will discuss them later. So, this is all about the basics of Machine learning and some important application used in real world.
I hope this blog will help to get some basic idea about machine learning and you can understand how machine learning and Artificial intelligence changing the world.
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1 thought on “How To Use Machine Learning In Real World Applications?”
Nice piece of work 🙂