As per market reports, machine learning technology is growing dynamically, and the worldwide investments in this sector have increased by 70%. It is expected to rise significantly in the coming years.

More and more businesses are adopting machine learning and enhancing their administrative exercises, and gaining meaningful returns. Nowadays, everyone wants user experience as per their requirements. This is why the building of machine learning apps is proving to be beneficial. 

Those who think there isn’t enough scope in machine learning app plans should think again! 

This article has listed some specific areas where businesses can benefit from machine learning mobile apps and some most exemplary machine learning app ideas that we are yet to see in the market.

Machine Learning Application for Healthcare Industry

Application for Potential Disease Outbreak 

COVID-19 outbreak has taught us many things, from the importance of digital advancements to building a future-proof medical infrastructure. However, if we could have recognized the outbreak in the initial stage, we must have dealt with it in a very different manner. 

Moreover, to recognize the same, we need technological assistance. This is where we need an app that can identify and alert us about possible threats and disease outbreaks. 

Building a machine learning web app for the future that can keep accounts of all the cases, examine data through machine learning algorithms, and alert the users about the potential threats. If ML-based apps like these could have been made earlier, you never know, maybe we could have detected this outbreak much earlier or at least taken necessary precautions to stop the spread.

This sort of machine learning apps can help hospitals and governments identify disease outbreaks and suggest taking instant measures for a cure.

Machine Learning Applications in Industrial Companies  

Gone are the days when everything was manual. Thanks to the digital advancements and adoption of Artificial Intelligence and Machine Learning, the usage of automated robots and enterprise management has made lives easy going and comfortable. Incorporating machine learning apps into the work process to run efficiently and flawlessly is a new norm. These modern and robust applications are ideal for improving the specific cycles.

Machine Learning is enabling the production process and enhancing every aspect of a business, from marketing and sales to maintenance. Employing machine-learning based solutions to handle hefty procedures, boosts efficiency and slashes costs.

All you need to do is hire a trusted machine learning development company to build an ML-based application that will enhance your enterprise process.  

Machine Learning Apps in Retail & E-commerce Industry

Building a Personalized Chatbot for E-commerce Websites

Chatbots go beyond machine learning, and they use another subset of AI called Neural Language Processing (NLP). The best thing about these ML algorithms is that they support and facilitate the process of human conversation while making it look organic. 

Many people think of chatbots as a customer service tool, but they can also be employed as virtual assistants, just like Alexa, Siri, Google Assistant, and more.

Building a personalized chatbot that utilizes machine learning algorithms to help customers by providing choices and recommendations is a game-changer for the retail and e-commerce industry. Based on the collected data of other buyers, machine learning chatbots can help customers compare two different products of similar classes.

Machine Learning Applications for Travel & Hospitality Industry

An Intelligent Travel Assistant App

Having an application that serves users to know more about travelling by gathering data and presenting it in a knowledge-based platform is a must nowadays. Many companies are integrating machine learning solutions to offer an enhanced user experience.  

This application will help the public recognize popular places based on multiple categories and provide insights about travel hacks and tips. In such applications, having a feedback column can help analyze various locations in a better way. And as painful as it can seem, people really enjoy planning their trips, so if you have an application handy that serves you with an AI-infused travel system that can generate super personalized suggestions, you can easily book your flights and hotels.

A must requirement for Intelligent Travel Assistant App is tourists’ data based on travelling to support developers in recognizing the components to be added in an app.

Machine Learning Implementation in Banking Apps

Some mind-blogging banking applications are already using Artificial Intelligence, but having applications based on machine learning can also be of great value for companies. For example, machine learning applications can help examine payment transaction records and keep a transparent and secured platform. 

Building a financial application will help in collecting user data about their money spending habits, setting a financial goal for the users, analysing the economic world and giving options for earning more money.

We all know that financial applications can be a little tricky, but choosing a trusted brand name in the market to build a machine learning application where users can connect and save all their financial credentials and bank accounts, is a game-changer.

Conclusion

Machine Learning has a great utility and power to turn things around for the better. If we look back, no one really thought there would be a tool that will make our lives easier. Over the years we have seen technology enter various parts of our lives and 2021 is the right time to adopt the digital domain with something interesting like Machine Learning.

So, choose a trusted brand name like Zazz, which provides a robotic process automation service, and build excellent machine learning applications in various verticals. Our team of experts are experienced and qualified in their technical domain and offer fully customized applications development as per your business requirements. 

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AI and ML open up a new ecosystem of limitless possibilities, from improving productivity to automated processes and simplifying systems. Nevertheless, one must have a clear understanding and understanding for machine learning programming languages.

Top Five Machine-Learning Programming Languages:

We address you with the top 5 programming languages in ML that will shape the future, whether you are a developer or just engaged in pursuing a career in the field of Artificial Intelligence and Machine Learning.

1. Java For Machine Learning:

Java is an extremely useful, fast, and reliable programming language that allows a multitude of projects to be developed by development teams. It’s more than applicable to the area of data science, from data mining and data processing to the developing of Machine Learning applications.

Java also integrates well with algorithms for search engines and facilitates projects on a wide scale. Java is mainly used for desktop application development. It can use for: 

• Transaction management
• Billing applications
• Developing android applications
• Server-side applications
• Writing algorithms (stock market)
• Data research
• Web applications
• Writing desktop & enterprise apps

Pros:

• Straightforward to use, write, compile & debug
• Fully object-oriented language for standard & reusable code
• No need for any special platform to run
• Runs on several computers on a network working together
• Presence of robust security manager

Cons:

• Needs a significant amount of memory
• The predefined look of GUI apps are different from native apps.
• Single paradigm language
• Expensive Memory management

2. Python for Machine Learning:

Python is a language for high-level and multi-purpose machine learning programming languages. For a non-programmer, Python has the most straightforward syntax to understand and is a strong choice for beginners. It has a range of frameworks and libraries and encourages paradigms of object-oriented and procedural development. 

Python can be easily downloaded digitally as a programming language at no cost. What makes it acceptable for machine learning applications is the support of the python developers community and a multitude of features.

It can use for:

• Web development
• Software development
• Business applications
• Data science
• Developing chatbots

Pros:

• Easy to read, learn & write
• Simplicity makes it highly productive.
• An interpreted language to execute the code.
• Automatically assign the data type execution.
• Vast libraries support 

Cons:

• Line by line leads to slow execution
• Uses a large amount of memory
• Not memory-efficient & slow processing
• Lead to runtime errors

3. C++ for Machine Learning:

The fastest programming language is C++, ideal for AI projects that are time-sensitive. Efficiency and fair use of resources are vital in writing applications. For C++, there are resources for machine learning and deep learning open. A positive part of C++ to Python is that C++ runs much faster than Python, so C++ would be good for you if you are trying to run a programme with a lot of array calculations.

However, it is ideal for individuals operating in an embedded environment who can not afford the Java Virtual Machine’s overhead costs.

• Game development
• GUI based apps
• Database software
• Operating system
• Banking applications
• Cloud/distributed apps

Pros:

• Useful for low programming language
• Compatible with C
• Faster than other languages
• Closer to the hardware
• Reusability & readability

Cons:

• Emphasis on instructions or data
• Use of pointers
• Security issue
• Absence of garbage collector or built-in thread

4. JavaScript for Machine Learning:

JavaScript is a high-level programming language designed to provide a user-friendly experience and improve web pages. With TensorFlow.js, JavaScript has managed to make increasing significance into Machine Learning. TensorFlow.js is an open-source library that builds machine learning models completely in the browser using JavaScript. 

Due to its full-stack functionality, versatile and multi-paradigm approach, and it’s easy to learn the fundamentals for the JavaScript developers.  It is popularly used for:

• Web applications
• Web development
• Mobile applications
• Game development
• Presentation as a website
• Server apps

Pros:

• Run fast and immediately
• Simple to learn & implement
• Used anywhere on the web
• Highly interoperable 
• Reduce demand on a website server
• Ability to create rich features

Cons:

• Lack of client-side security
• Interpret different on different browsers
• Lack of debugging facility
• Single inheritance
• Rendering stopped

5. R for Machine Learning:

R is one of the best coding languages and environments of statistical purposes for analyzing and manipulating data. Using R, we can quickly generate a well-designed plot of publication standard, including appropriate mathematical symbols and formulas. Packages make it simple to apply machine learning algorithms to crack down on business-related issues. It use for:

Pros:

• Open-source
• Array of packages
• Quality plotting & graphing
• Platform independent
• Machine learning operations
• Continuously growing

Cons:

• Weak origin
• Data handling
• Basic security
• Complicated language
• Lesser speed

Wrapping Up Note:

The best language for machine learning depends on the field in which it will implement, the nature of the machine learning programming language in which your industry/company uses, and many other factors. For any specific machine learning problem, experimenting, testing, and experience help a machine learning professional decide on an appropriate programming language option. 

Zazz is a leading software development agency with hands-on expertise in Machine Learning. Our veteran team helps businesses solve complex challenges by enabling data-based decision-making and developing innovative data-driven business models. 

Our end-to-end ML app development services aim at providing you with a customized experience to meet your business needs. Lets discuss with our team for futuristic & innovative market-ready business solutions. 

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The adoption of machine learning is a necessity for banking, on the one hand there is the ability to generate certainty in an extremely competitive and variable market, while on the other hand there is its transversal application to identify opportunities and add value in areas ranging from Customer service risk management.

Unlike Analytics, which consists in the analysis of data, machine learning is a type of artificial intelligence that learns on its own, without there being a program that dictates what to analyze and how to do it. Through a series of algorithms, the system processes billions of data, structured and unstructured, to identify complex patterns and predict future behaviors.

Machine learning has an extensive applicability in banking, because it feeds on data and there is no other industry that knows both its clients and the financial one. These are 5 Machine Learning applications in banking.

Credit Risk Modeling

When there is a loan application, the bank evaluates whether the person or the company is in a position to pay the loan plus interest at a certain time, for this they use profitability measures, leverage and many other variables such as liquidity to calculate risk, a complex task that can sometimes be inaccurate.

From Machine Learning, the artificial intelligence that makes up the system is capable of generating credit risk models, based on financial data and credit and consumer behavior of its customers. They can also identify when to increase or reduce a customer’s credit line, by calibrating the bank’s risk tolerance.

Fraud detection

Although fraudulent actions only represent a tiny fraction of a bank’s global transactions, the scalability index and its effects on a financial institution’s reputation can be enormous. Conventional schemes to identify fraud respond to pre-established rules, which are not efficient in real time.

Through Machine Learning, behavior patterns are extracted from the data, which become a set of parameters or rules, which applied within new data, allow to identify suspicious actions and prevent fraud before they occur, in this way Work proactively.

Customer segmentation

When analyzing the interaction of each client with the bank, it is possible to identify their level of affinity and relationship, possibly some have in mind to change financial institution, which implies that in the last period they have stopped using financial services.

There are an infinite number of behaviors, which when analyzed through Machine Learning, can be defined in segmented groups, on which to establish specific strategies to seek to retain them or expand their portfolio of services or financial products.

Each time a client adopts a behavior that fits a pattern, it will enter into the segmentation, in order to generate a better customer experience.

Recommendation Engine

In the ecommerce or television on demand industry, machine learning constantly generates recommendations based on customer behavior and interactions.

The same logic can be applied in the financial industry, since digital channels such as mobile banking applications or online banking are spaces for customer interaction, where financial product or service recommendations can be created based on customer behavior and your needs.

Incorporating machine learning as part of the Banking Core System of a financial institution is a complex task in itself, which is exacerbated by the incompatibility in the programming languages ​​and the architecture on which it is designed.

The automation of the processes

The first stop on the road to smarter banking is cost reduction. Entities that do not overcome the current efficiency challenge will hardly be able to gather enough resources to face the emergence of new services and banking channels, in different ways of providing traditional and redoubled quality requirements.

Within the banking processes there is a wide set of tasks that are large time consuming and cost generating. The automation think one of the solutions for efficiency gains.

In this field it seems clear that the ideal is the conjunction of various technologies. For example, from Bank of New York Mellon it is pointed out that one could try to move forward, through robotic process automation,  which prevents the human being from having to take over the most repetitive tasks, which are usually time consuming and prevent to workers provide all the value they can potentially develop.

However, from this American bank it is noted that machine learning goes a step beyond where the robotization of tasks could go by itself: they seek to identify patterns that allow, as time goes by, to improve processes. That is, your contribution as a search engine for automation improvement possibilities is very important to achieve the desired efficiency gains.

The change in risk management

Another area in which to reveal hidden patterns may be important for the improvement of banking processes is risk management. This translates into a better understanding of who the riskiest debtors may be and the conditions that must be demanded in exchange for the granting of financing

In that sense, a report by the consultant McKinsey points out that machine learning shows superiority over the various traditional statistical methods based on regression models, mainly because it can identify patterns that do not respond to a predefined functional form .

Against fraudulent behaviors

Efficiency gains and the expansion of services would be meaningless without security in banking operations. The expectations of the different participants must be answered at all times. Let us not forget that fraudulent behaviors undermine the trust of the parties and the credibility of the organizational frameworks in which the banking operations are carried out.

Therefore, the bank is looking for tools that make it easier to find out when we can face a fraudulent operation. In general, they have been based on the analysis of transaction data and its interveners. The objective is usually to qualify a transaction as legal or suspicious.

However, we must face a reality that complicates the classification: almost all operations are legal. Any method that classifies all transactions as legal will almost always succeed. However, those few fraudulent operations will systematically escape.

What is intended with machine learning is to ensure that machines can make increasingly refined predictions that detect cases of fraud. In short, we are looking for algorithms capable of learning to detect suspicious patterns.

An example of this is in the project in which BBVA and the Massachusetts Institute of Technology (MIT) are collaborating in relation to the application of machine learning to the detection of card fraud. It seeks to reduce the number of operations mistakenly classified as suspicious by introducing more than 200 new categories to its analysis.

These types of initiatives are already being noticed in the pockets of bank users. For example, Natwest has also tested machine learning in the fight against bank fraud. And, for the moment, it   has estimated at 7 million pounds sterling the losses avoided to its customers. It also emphasizes that, although very few transactions are affected by fraud, the amount may be high.

Automated Financial Operations

Regarding machine learning in financial operations, we find two types of applications: those for professionals and bank users. The former are oriented, fundamentally, to the provision of key information in decision making. The second ones have as their main objective the advice.

Machine learning in a financial capital

For example, in the professional field, JP Morgan is working on different lines based on machine learning, such as the analysis of market sentiment, data-based trading decisions, value investment or clustering. We are, therefore, talking about the application of learning in making professional investment decisions regardless of the approach and whether they are carried out in the short or long term.

But it is in the relationship with customers where the other major point of interest occurs. And here the protagonists are the robo advisors, virtual assistants that allow the user to access specialized advice. The advisors theft poses a complex dilemma for the big banks.

On the one hand, they cannot afford or be out of the technological race because of their design or take them away from their offer, given the competitive pressure of fintech. On the other, they do not want their efforts to divert clients to other entities.

The balance is being found in the progressive incorporation of roboadvisors. At the beginning, they tend to focus solely on, for example, their own investment funds or the clients of their digital native subsidiary. Subsequently, they expand their offer as they observe the reactions of the public and competitors, technologically developing these tools and assume regulatory challenges (such as the PSD2 directive).

Machine learning in banking and virtual assistants

Banking goes far beyond investment and financing operations. It is a companion of many facets of private and business life. And one of the aspects that customers value most is that their entity helps them to give fluidity to all their daily or extraordinary challenges.

One of the important missions of machine learning is to become a tool that allows transforming all the data that banks have of their clients into advisory services and improving their financial life in the broadest sense.

An example of this is the virtual voice assistant Erica, from Bank of America, who continuously analyzes the data to offer the client solutions for their financial life. In addition, it is an interactive tool to which the client can ask questions or requirements.

This type of attendees is very important for the development of digital banking because many of the clients who opt for physical offices do so because of the possibility of having someone to contact in case of doubt, complaint, need for information, etc. They are, therefore, a key piece in the process that tries to bring some of the best features of the physical world to digital.

Machine learning is a useful tool in the day-to-day work and banking services. In addition, it is intended to be the great gateway for artificial intelligence in the financial sector.

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• NeuroSYS

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Machine learning – the branch of artificial intelligence – is changing not only the way we interact with machines but also how we interact with the environment around us. In the last decade, machine learning has given us cars that drive by themselves, voice recognition, effective web searches, personalized recommendations, and a vast understanding of the human genome. The term “machine learning” dates back to 1959 when Arthur Samuel, an IBM researcher defined it as “the ability (for computers) to learn without being explicitly programmed,” and this field encompasses a variety of mathematical techniques where computers learn and redefine their solutions based on a simple “training” of data. But how does machine learning work?

Computers were originally designed to follow algorithms. An algorithm is simply a series of stages encoded in a computer language. Expert computer programmers would contact process experts to plan business operations in flowcharts which would then be implemented as computer programs. A flowchart explicitly positions the tasks that must be performed, in the order in which they should be performed, along with any decision that needs to be made along the way. Flowcharts are excellent for modeling repetitive and predictable processes where decisions are made on unambiguous data. These systems were created to be decisive.

But not all processes follow clear and immutable rules, and many real-world decisions do not lead to a single unambiguous answer. Machine learning systems are probabilistic: tasks are executed and decisions are made on incomplete information and the results are assigned probabilities of being correct. Machine learning is suitable for problems that involve classification (dividing objects between two or more classes) regression (discovering relationships between variables) and grouping (grouping objects with similar characteristics). Which brings us to certain uses such as:

Recognize patterns:

• Objects in real scenarios
• Facial Identities or Expressions
• Spoken language
• Extract knowledge:
• Of free format, audio or video texts
• Email spam detection

Discover abnormalities:

• Unusual sequences of financial transactions
• Unusual patterns of sensor readings
• Make predictions:
• Future value of the shares or exchange rates
• What movies will a person like?

Many mathematical techniques support machine learning, the most important are these:

Linear and polynomial regression

Regression deals with modeling the relationship between numerical variables that is iteratively refined using an error measure in the predictions made by the model. The basic assumption is that the output variable (a numerical value) can be expressed as a combination (weighted sum) of a set of numerical output variables.

Decision trees

These tree-shaped flowcharts use ramifications to illustrate any possible outcome of a decision. Many of these tree diagrams use binary branching (two options) based on current values ​​or data attributes. For large volumes of data, many of these multiple decision trees can be created, which together form a consensus decision on the results. Decision trees can be used for classification and regression problems.

Neural networks

This concept is inspired by the way the nervous system works, as well as the brain to process information. A large number of highly interconnected processing elements work in unison to solve specific problems, usually of classification or pattern matching. Each neuron “votes” on the outcome of the decision, which could urge other neurons to vote, so the votes are counted creating a classification of the results depending on the support each has received.

Bayesian network

These graphic structures, also known as a belief network, are used to represent knowledge about an uncertain domain. The graph is a probabilistic map of causes and effects where each node represents a random variable, while the edges between the nodes represent probabilistic dependencies. For example “red sky at night” could lead to a 75% chance of “good weather.” These conditional dependencies are frequently estimated using statistical and computational methods.

Markov chains

They are mathematical systems that go from one “state” (situation or group of values) to another: it is assumed that future states depend only on the present state and not on the sequence of events that precede it. For example, if you make a model of the Markov chain about the behavior of a baby, you could include “play”, “eat”, “cry” and “sleep” as states, which together with other behaviors could form a “space of states ”which would be a list of all possible states. Additionally, a Markov chain tells you what is the probability of jumping or “transcending” from one state to another, for example, the probability that a baby that is playing falls asleep in the next five minutes without crying before.

The plethora of algorithms available for machine learning has restricted this technology to those companies with the experience required to select the right tool for the project at hand. For machine learning to be more widely adopted, these technologies need to be simplified and delivered as a service. This is the goal of the IBM Watson machine learning service.

Created on Apache Spark, Watson machine learning intelligently and automatically builds models using open machine learning libraries and the most understandable algorithm groups in the industry. Its patented cognitive assistance for data science technology marks each machine learning algorithm against the data provided to recommend the best solution for the need.

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