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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The world of AI and machine learning programming languages is advancing rapidly, and those who possess the skills to collaborate with them will be the frontrunners of the future. To further solidify their importance, a recent study forecasts a substantial 14% increase in global GDP by 2030. 

At the edge of this technological revolution, AI & ML offer a broad range of opportunities. From streamlining automation processes to enhancing efficiency and optimizing system simplification, the potential applications are endless. 

However, it is imperative to have a detailed understanding of machine learning programming languages to truly leverage their full potential.

Top Five Machine-Learning Programming Languages:

Discover the most influential programming languages in machine learning that are set to steer the future of this industry. Whether you’re an established programmer or a newcomer to the field of artificial intelligence & machine learning, these five languages are a must-know for anyone serious about staying ahead of the curve.

1. Java For Machine Learning:

Java is a highly advantageous & efficient programming language with an impressive capability of empowering development teams to carry out a range of diverse projects. This versatile programming language finds extensive application in the domain of data science, facilitating tasks such as data mining, processing, & the development of Machine Learning applications. Notably, Java easily integrates with search engine algorithms and fosters extensive, large-scale projects.

Though Java is predominantly used for desktop application development, its potential is immense & holds significant promise for various other emerging fields. To get better look into the usage of Java in Development, you must partner with a top Machine Learning Development Company and seek their expertise.

• 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 robust language, widely recognized for its versatility in facilitating machine learning programming operations. With its intuitive syntax & simple design, Python is an ideal choice for beginners, particularly individuals new to programming. 

Moreover, Python boasts multiple frameworks, libraries, and supports object-oriented and procedural development. Python is relatively easy to download & requires no cost, making it a convenient option.

What sets Python apart is the dedicated community of developers, unleashing cutting-edge features that make it an excellent choice of language for machine learning applications.

You can use it 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 legal use of resources are critical 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 program with a lot of array calculations.

However, individuals operating in an embedded environment, who cannot afford the overhead costs of the Java Virtual Machine, find it ideal.

• 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, a prominent programming language, offers a sophisticated interface that enhances web pages to provide a user-centric experience. As Machine Learning gains more momentum, TensorFlow.js has successfully integrated JavaScript into its framework. TensorFlow.js provides an open-source library that enables the development of machine learning models entirely through the browser, using JavaScript.

This feature, coupled with its full-stack functionality & versatile, multi-paradigm approach, makes it an ideal platform for JavaScript developers to explore. Moreover, it is widely popular for its ease of use in building intelligent applications.

• 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 a highly efficient language and statistical environment, specifically designed for analyzing & manipulating data. Its functionalities allow for the generation of publication-quality, mathematically precise plots.

The use of specific packages further provides an avenue for applications of machine learning algorithms, which aids in tackling diverse business-related issues. You can also look to work with a quality mobile app development company to get maximum benefits from this language. So, R is a widely popular tool for conducting data analysis across numerous industry sectors. It is used 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 selection of the optimal programming language for machine learning largely depends on various decisive factors such as industry requirements, the nature of your company’s machine learning programming language & others. A machine learning professional must conduct extensive experimentation, testing & practical experience to logically determine an appropriate language option for any specific problem.

Zazz, a reputable software development company has considerable expertise in the implementation of AI and Machine Learning solutions, making it a desirable partner for businesses pursuing cutting-edge technological advancements.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. 

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 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 that sustain the importance of an AI app development company.

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. This is why retail businesses hire web developers.

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. You can also hire ios app developers or consult an agency to get the process started.

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.

Zazz

Zazz is a distinguished Machine Learning Development Company based in the United States that has been consistently pushing the boundaries of software design & development innovation. Renowned as an industry leader, Zazz prides itself on its unwavering commitment & adopt latest technologies, refining seamless processes & nurturing creative talent. 

The agency’s success hinges on a “bottom-up” strategy that prioritizes users at the heart of its software development. Indeed, the users’ footprints, journeys, & engagement metrics are integral to shaping the overarching digital experience at Zazz – a practice that sets the agency apart from its competitors.

List of Services:

• Product Strategy
• Prototyping & Concept Development
• Product Design
• Android Application Development
• iOS Application Development
• Flutter App Development
• Healthcare App Development
• React Native App Development
• Web Development
• Blockchain Development
• UI/UX Design Services
• Machine Learning

For businesses of all sizes, Zazz offers top-notch solutions catering to your unique requirements. Their team possesses extensive experience & resources to deliver end-to-end assistance to your projects, whether you are a growing startup seeking to test an MVP or an SME looking to optimize processes.

With a proven history of designing & maintaining digital solutions, they are equipped to handle your digital transformation needs. Hire app developers to get insights into the path of your business success. Check out their website for case studies & schedule an appointment with their knowledgeable consultants.

AppStudio

Based in the tech city of Toronto, AppStudio is a software design & development company that provides end-to-end solutions for businesses seeking truly innovative applications. Specializing in mobile & web applications, AppStudio’s dynamic & multidisciplinary team is committed to delivering unforgettable digital experiences for their clients. Regardless of whether you are a fledgling startup or an established enterprise, AppStudio has the skillset & resources to match their services to your specific business requirements along with affordable prices.

List of Services:

• Product Strategy
• Prototyping & Concept Development
• Product Design
• Android App Development
• iOS Application Development
• Flutter App Development
• Healthcare App Development
• React Native App Development
• IoT App Development
• Web Application Development
• Blockchain Development
• Machine Learning

STXNext

We are Europe’s largest Python software house. Using Agile methodologies, we provide more than 200 Python, React Native & full-stack developers for you to make your project a success in this competitive era.

Intellias 

With 1,600+ employees worldwide, Intellias has expanded from a small, one-man business to a global company that delivers software development services from Ukraine, Poland & Germany. In a number of domains, their clients include Fortune 500 companies as well as leading technology innovators.

Fayrix

Fayrix provides world-class custom & offshore software development. Relying on 14 years of experience and a talented team of 1500+ IT professionals including 700+ developers, we are ready to execute software development projects of any scale. 

In addition to flexible terms and competitive rates, we offer our clients different models of collaboration – time and material, dedicated teams & project-based outsourcing.

Neoteric

Neoteric specializes in supporting startups & enterprises with complete product development services & cater to every stage of the process & utilize AI to optimize performance. Their commitment is to aid partners in building successful software ventures by extending product teams & providing resources and expertise.

NeuroSYS

As a full-cycle software development company, they combine analytical & consulting skills to provide individually customized solutions for your business. Having an innovation-driven culture, we also provide services in AI & Deep Learning, as well as develop solutions for implementing these technologies on a commercial basis..

MobiDev

MobiDev excels at creating tailored software solutions, with expertise spanning from web & mobile development on iOS/Android & cross-platforms, to AR, IoT, hardware integration, AI & data science. They also specialize in microservices & cloud infrastructure, with a deep understanding of emerging trends & technologies. Trust them to deliver unique, expert & successful results.

Altoros

With over 400 employees, Altoros provides the methodology, technological building blocks, & end-to-end solution development needed to support digital transformation at scale for Global 2000 companies. By combining cloud-native apps, customer analytics, Blockchain & AI, they create competitive advantage products.

Netguru

At Netguru, their expertise lies in creating innovative digital products that empower users to experience a new way of doing things. They have partnered with top startups, Fortune 500 companies, & globally recognized brands to solve real-world challenges via their state-of-the-art software & product design. Their distinguished clientele has revolutionized industries such as banking, music streaming, language acquisition & transportation. Their products have garnered the attention of influential tech media outlets including TechCrunch, Business Insider & Product Hunt, solidifying their reputation as a leading player in the digital product development space.

In addition to changing how we interact with machines, machine learning is changing our interaction 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.Machine learning, a field of computer science, was first coined by Arthur Samuel in 1959 as the ability for computers to learn without explicit programming. This innovative discipline employs a range of mathematical techniques & algorithms to analyze data & improve its performance, allowing computers to refine their solutions and predictions through experience. Ultimately, machine learning systems are trained to recognize patterns & respond accordingly & have revolutionized numerous industries & are still transforming the way we interact with technology. But how does machine learning work?

Computers were originally designed to follow algorithmsAlgorithms are a crucial component of computer software & consist of a sequence of processes encoded in computer languages. To create these algorithms, computer programmers frequently collaborate with industry experts to design flowcharts outlining essential business procedures, which can be translated into efficient computer programs. These flowcharts carefully outline the steps required to achieve a desired outcome in the proper order, as well as any pertinent decision-making factors. By doing so, businesses can streamline their operations & achieve optimal efficiency in their day-to-day tasks.

Real-world scenarios often lack clear-cut procedures & definitive outcomes. Machine learning functions on a probabilistic basis, where tasks are conducted based on incomplete data & the results are assigned probabilities to determine their potential accuracy. This approach allows for a more versatile & adaptable approach to decision-making. It is appropriate for problems involving classification (classifying objects into groups), regression (discovering relations between variables) & grouping (grouping objects based on similar characteristics).

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:
• Share prices or exchange rates in the future
• What movies will a person like?

Machine learning is supported by many mathematical techniques, but the following are the most important:

Linear and polynomial regression

Regression is a statistical technique for analyzing the relationship between numerical variables. The iterative refinement process involves minimizing the errors between the predicted & actual output values. The fundamental principle in regression is that the output variable can be expressed as a weighted sum of several numerical output variables. A well-crafted regression model can unveil significant insights into the data & helps researchers & practitioners make informed decisions with accuracy & confidence.

Decision trees

Flowcharts shaped like trees illustrate ramifications of possible outcomes. 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.It is possible to use decision trees to solve classification & regression problems.

Neural networks

Based on how the nervous system & brain process information, this concept was developed. A large no. of interconnected processing elements work together to solve a specific problem, typically a classification or pattern matching problem. 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

Belief networks, or graphical models, are employed to depict knowledge about an unknown sphere. These models represent causal relationships between variables through a probabilistic graph. Nodes on the graph represent random variables, while probabilistic dependencies are demonstrated through the edges between them. This allows experts to understand complex relationships within complex systems. 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

There is an assumption that future states are solely determined by the present state, not by the sequence of events leading up to it. The behavior of a baby can be modelled using a Markov chain, where the various observable states, such as “play”, “eat”, “cry” & “sleep”, form a comprehensive list of all possible states. These states together with other behaviors make up the “space of states”. 

In addition, a Markov chain provides the probability of moving or “transcending” from one state to another. A classic example is the likelihood of a baby falling asleep while playing without crying in the next five minutes. As such, Markov chains provide a valuable tool for mapping and predicting the behavior of complex systems such as infant behavior.

As a result of the vast number of machine learning algorithms available, this technology is restricted to companies with the expertise to select the right algorithm for the job. The adoption of machine learning will be more widespread once these technologies are simplified and delivered as a service. It is this goal that IBM Watson is striving to achieve with its machine learning services.

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. Leveraging proprietary cognitive assistance for data science, their technology expertly assesses each ML algorithm against the data received to provide the optimal solution that fits your specific requirements.