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Machine Learning

How is Machine Learning Making The World A Better Place?

“Machine learning has enormous advantages and applications—it is capable of making the world a better place—even it can destroy it in no time. Everything depends on how you take it and apply it to the sole purpose of humanity and others’ life. ”  

Introduction

The world is already a better place not only for humanity but for everyone around. But what creates a big difference is machine learning and its application that shows new ways to live a life of many dreams coming true. The world has become fast-paced, and competitions are too high. Surviving is not the key here but innovative, following the latest trends and automation to sustain any business.

And if you see the latest machine learning applications, you can witness how the world shapes into a better place—and how you can make it easier either by creating or following the norms.

Let’s dive in and explore how machine learning plays a significant role in making the earth a better place to live.

Machine learning Applications in Covid-19

Humanity would have been extinct, uncountable dead bodies lying here and there if there would have been no machine learning. The Covid-19 virus is that dangerous. After the 1st and 2nd waves, the third wave will hit in the next few months.

Imagine what would have been the case if there were no facilities to track daily records of newly infected, death, and recovered rates. Consider the ease of monitoring vaccinations, doses and scheduling them with Arogya Setu from your mobile device.

And when you open the app, it asks a few basic questions to know you better about your state and with whom you have come in contact using simply Bluetooth and GPS. Even it gives you regular information about the person you have come in contact with and asks you to take necessary precautions before Covid-19 attacks you and you become the next super spreader.

You only see the information on the news channel or the website. Has this question ever popped into your head “how is everything trackable, and can we know the exact situation of Covid cases?” It’s all feasible because of machine learning algorithms and their suitable applications by data scientists and machine learning engineers.

Every time you take an RT-PCR test, the results land at your registered mobile number within 24 hours. Not only you, but 10 -15 lakhs tests happen a day, then how is the government able to track everything and save individuals from deadly Covid-19—that’s the power of machine learning towards healthy humanity and the earth. 

Machine Learning Applications in Education

Education leads to wisdom, being innovative and practical at the same time. But if you lack one, you suffer for a lifetime. It’s as simple as that to sustain in this highly competitive world.

Machine learning played a crucial role in scaling up the educational system. Today it is a whole new level than it was years back. Using the LMS allows busy professionals to learn at their convenience while scaling up their careers.

But due to the worst Covid-19 hit, schools and other educational institutes somehow managed to bring their whole educational system into the track, launching their LMS platforms and live classes.  

The real magic with the LMS platform is: it tracks each data of each person. Lecturers won’t have to teach again what you missed, but you can attend them online till you grasp the core concept of it. It also tracks how many modules are left and that you have completed. Even it gives the lecturers the freedom to collect the assignment and share their reviews just like it happens in the classroom.

Banking and Financial Industry To Safeguard Your Financial Activities

Imagine for a moment what if all the ATMs near you stop working for days, can’t dispense cash for you, and UPI and internet banking go on hold for an unlimited time due to server issues. Then how would you see the world now? Going crazy, right? Even the bank staff will fall crazy, and so their computers handle the whole people in their areas.

But to manage hassle-free and keep track of every transaction, machine learning plays a vital role in the banking industry. It tracks the loans, cash, balances, cheque, UPI transactions from third-party apps, credit and debit cards, internet banking, etc. When there is a withdrawal or deposit, it sends a text message to the registered number about the latest balance.

When finance is involved, there are risks, data tampering, unauthorized access, fraudulent activities. Still, machine learning has self-learning activities that notify the users and the respective bank at the same time when there is withdrawal. Even with OTP, the two-factor verification adds an extra layer of security to prevent unauthorized access and data tampering and keep you safe from money laundering activities.

Online Recommendation Engines For Getting You Your Favorite Stuff

Ever came across this question, ‘Why is the eCommerce business one of the most successful business models globally, and why are more and more entrepreneurs into this model lately?’ Yes, e-Commerce is the futuristic business model for which most businesses are in threat.

The secret to their success is that more and more entrepreneurs are the eCommerce business-savvy recommendation. When you buy an iPhone or click on the ‘add to cart option’ the next moment, it shows you ‘people who have also brought these items’ with a cover and tempered glass. This way, it persuades you to buy more items at a time with some handy discounts.

Even online platforms like Netflix, Amazon Prime Videos, Hotstar+Disney, youTube, and Spotify use an innovative engine recommendation system based on experience to recommend the next watch. Every time you watch, it tracks the data about how much time you spent watching, how much is left, so when you open it, it shows you the currently watching, top ten grossing based on most people watching, and so on.

Google Search Algorithms & Personal Assistants As Helping Hand For All Problems

Google is a repository of uncountable data that has limitless potential. That’s why the whole world runs and trusts Google for all the required information and answers. Google uses NLP to understand human queries and personal assistants ( Google Voice Assistant, Amazon Alexa, Apple Siri) to give them the best possible solutions.

Whenever a person searches using voice assistants or search engines, these ML algorithms behind the search engines break the whole queries into small chunks using NLP, look for the exact information, and the most relevant one that people have visited the most.

But the fundamental transformation that happened when personal assistants got into the real action was the best thing that happened to humanity. Take Google, for example. Say ‘Ok Google!’ to activate your assistance and ask your queries without typing, or ask Google to send a message, dial someone. Or play your favorite songs, book a table for the next meeting or remind you about the birthday; Google does everything flawlessly.

It needs your voice and can read news for you, say about the forecast, the traffic, whatever you want from Google—ask away. The same applies to other search engines and personal assistants. And guess what, you all have them on your mobile devices.

Email Spam and Malware Filters To Save You From Online Frauds

Online frauds are the most common thing in today’s world, and cyber attackers use email to target their new potential customers. They use lottery, money, and other things to steal your credit card information and ask you to share your OTP to use your data against yourself.

So with updating algorithms, whenever a mail lands in your inbox, Google uses various filters to decide where it is supposed to land. If it contains a few words, spam, or many users marked spam to the senders, Google filters learn it using ML algorithms. It automatically sends that email to the spam folder to protect you from different fraudulent activities and online phishing.

Though it’s not a new thing already, it’s getting robust with time, now more trustable with safe keeping from online frauds.

Final Words

These are a few top-notch applications of machine learning to make the world a better place to live and give us a healthy lifestyle. Yet, many applications are breathtaking. Machine learning has essential applications for protecting human life, as evidenced by the recent Covid-19.

The applications of machine learning made the distance between two places almost negligible today. You can go on video calls to meet your favorite persons virtually, even attend meetings virtually over zoom calls and Microsoft team meetings that add an extra layer of data security to protect your privacy.

So use machine learning in the right way; it has the potential to get you the best things you have ever imagined. It has life-changing applications in many ways, so you can make the world even better, a happier place than it is now. 

Machine Learning

Types of Machine Learning

Machine learning methods are a set of tasks aimed at testing hypotheses, finding optimal solutions using artificial intelligence.

There are three methods

Supervised learning: In this case, an array of data on a specific task is loaded into the analytical system and a direction is set – the goal of the analysis. As a rule, you need to predict something or test some hypothesis.

For example, we have data on the income of an online store for six months of operation. We know how many products were sold, how much money was spent on attracting customers, ROI, average check, number of clicks, bounces and other metrics. The task of the machine is to analyze the entire data array and issue a forecast of income for the upcoming period – month, quarter, six months or a year. It is a regressive problem-solving method.
Another example. Based on the array of data and selection criteria, it is necessary to determine whether the text of the letter to the e-mail is spam. Or, having data on the performance of schoolchildren in subjects, knowing their IQ on tests, gender and age, you need to help graduates decide on career guidance. The analytical engine seeks out and checks common features, compares and classifies test results, grades in the school curriculum, and a mindset. Based on the data, it makes a forecast. These are classification tasks.

Unsupervised learning: Learning is based on the fact that the person and the program do not know the correct answers in advance, there is only a certain amount of data. The analytical engine, processing information, itself looks for interconnections. Often we have unobvious solutions at the end.

For example, we know the data on the weight, height and body type of 10,000 potential buyers of jumpers of a certain style. We load information into the machine in order to divide clients into clusters in accordance with the available data. As a result, we will get several categories of people with similar characteristics in order to release a jumper of the desired style for them. These are clustering tasks. Another example. To describe any phenomenon, you have to use 200-300 characteristics. Accordingly, it is extremely difficult to visualize such data, and it is simply impossible to understand them. The analytical system is tasked with processing an array of characteristics and choosing similar ones, that is, compressing the data to 2-5-10 characteristics. These are dimensionality reduction problems.

Deep learning. Deep machine learning is necessarily Big Data analysis. That is, it is not possible to process so much data with one computer, one program. Therefore, neural networks are used. The nature of this training is that a big field of data is divided into small segments, the processing of which is delegated to other devices. For example, single processor only collects information on a task and transfers it further, four other processors analyze the collected data and transmit the results further. The other processors in the chain are looks for solutions

For example, an object recognition system works on the principle of a neural network. First, the entire object is photographed (obtaining graphic information), then the system breaks down the data into points, finds lines from these points, builds simple shapes from lines, and from them – complex two-dimensional and then 3D objects.

For each of these methods, there are various algorithms for adjusting the parameters in order to achieve the best possible agreement with the known data. These algorithms are the real learning processess in machine learning. Examples are gradient escape, reverse propagation, and genetic algorithms.

Some algorithms perform better or worse depending on the purpose of the application. This can also be influenced by data. Some special applications even require modification of the algorithms themselves. In many cases, very good results can be achieved using standard algorithms. In some cases, however, it may be necessary to modify the algorithm or develop your own.

Machine Learning

How Machine Learning Works

It is easy to look at machine learning as a magical black box, in which you insert data and make predictions. With that, there is nothing magical about machine learning, writes IDG News. In fact, it is important to understand how the different parts of machine learning work, to get better results. So, join us on a tour.

As in many other IT contexts, such as devops, the term “pipeline” is used in machine learning. It is a visual parable of how data flows through a solution. The pipeline can be roughly divided into four parts:

  1. Collect data, called a little funny for “ingesting” (inta) in English.
  2. Prepare data, such as data wash and normalization if needed. Normalization in this context should not be confused with normalization of relational databases, but it is about adapting different value scales to each other.
  3. Model training.
  4. Provide predictions.

Here are more detailed descriptions of the four phases:

Decide on data

Two things are needed to get started with machine learning: data to train a model and algorithms that control training. Data can come from different sources. This is often about data from any business process that is already being collected, either continuously or in archived form.

In some cases, you have to work with streaming data. Then you can choose between managing data streaming or first storing it in a database. In the case of streaming data management, there is another choice between two options: Either you use new data to fine-tune an existing model or you build new models from time to time and train them with new data.

How Machine Learning Works

These decisions affect the choice of algorithms. Some algorithms are suitable for fine-tuning models, others not. In the latter case, you may start with new data.

Data washing is often about scales

There can be a lot of confusion in the data that is taken from a lot of different sources. One thing that often needs to be arranged is to normalize the data, ie to convert different data values ​​to the same scale.

A simple example is that 2.45 meters in high jump can be considered as worthwhile as 8.95 meters in long jump, as both are world records. In order to understand that the values ​​are equally valuable, they need to be converted, normalized, for example to 1.0 in both cases.

But in some cases normalization is not appropriate. It applies whether the scale actually matters. If you want to compare female and male height jumpers, it may be appropriate to normalize so that 2.45 meters for men will have the same value as 2.09 meters for women, as both are world records. But if you want to compare height jumpers regardless of gender then you should not normalize the values.

During the data preparation phase, it is also important to analyze how bias can affect models. This may include, for example, how to select data to use or how to normalize data.

Time for hard training

The next phase is the actual training of a model. It involves using data to generate a model from which predictions can be made. The key activity during training is to make settings, which is called “hyperparameterization” in English.

A hyperparameter is a setting that controls how a model is created based on an algorithm. A very simple example is if you want to divide a number of worlds into categories. In that case, a hyperparameter can be the number of categories you want. One way to arrive at good hyperparameters is to simply try them out. But in some cases, these settings can be optimized automatically.

Sometimes the training can be run in parallel on several processors, which of course provides performance benefits. It doesn’t have to be different processors, but you talk about workers. Workers in this case are simply different copies of a program that runs at the same time in different places.

The parallelization can mainly be done in two different ways: first, different “workers” can work with different parts of a data set, and different “workers” can work with different parts of the model.

Time for delivery

The final phase is to use the pre-trained model, which can be called the “predict and deliver” phase. Now you run the model on new data to generate a prediction. For example, if it is about face recognition, then incoming data is a digital image of a face. Based on training with other images on the faces, the model can now make new predictions. How you handle all the different activities in the different phases, or the different parts of the pipeline, varies. Using cloud services increases the chance of handling multiple parts in the same place, such as training data, pre-trained models, and so on.

In some cases, decisions must be made in cases where the different parts should be handled on servers or client devices. One advantage of running processing on a client, such as a smart mobile, is that accessibility is increased for the user. One potential disadvantage is the poor quality of the prediction, as there are less hardware resources, another poor performance, thus it takes longer to generate a prediction.

Iterative working method

To illustrate the whole flow of machine learning with a pipeline, ie a pipe, is a bit misleading. It is often about iterative work, that is, certain phases are repeated and refined. The type example is that a model is trimmed with new data.

The advantage of thinking of a pipeline with delimited parts is that it becomes easy to focus on the different parts as delimited areas that work in different ways.

A general observation that machine learning is actually as good can be called data analysis, or even math, as AI. What you call machine learning for AI may be because it is a technology that makes it possible to draw conclusions that humans, at least in most cases, cannot.