1- What is generalization?

The term ‘generalization’ refers to the model’s capability to adapt and react properly to previously unseen, new data, which has been drawn from the same distribution as the one used to build the model. In other words, generalization examines how well a model can digest new data and make correct predictions after getting trained on a training set.

How well a model is able to generalize is the key to its success. If you train a model too well on training data, it will be incapable of generalizing. In such cases, it will end up making erroneous predictions when it’s given new data. This would make the model ineffective even though it’s capable of making correct predictions for the training data set. This is known as overfitting. The inverse (underfitting) is also true, which happens when you train a model with inadequate data. In cases of underfitting, your model would fail to make accurate predictions even with the training data. This would make the model just as useless as overfitting.

2- The ideal solution

You would ideally want to choose a model that stands at the sweet spot between overfitting and underfitting. To achieve this goal, you can track the performance of a machine learning algorithm over time as it’s working with a set of training data. You can plot both the skill on the training data and the skill on a test dataset that you’ve held back from the training process. As the algorithm learns over time, the level of error for the model on the training data would decrease and so would the error on the test dataset. Training the model for too long would cause a continual decrease in the performance on the training dataset due to overfitting. At the same time, due to the model’s decreasing ability for generalization, the error for the test set would start to increase again. The sweet spot is the point just before the error on the test dataset begins to rise where the model shows good skill on both the training dataset as well as the unseen test dataset.

To limit overfitting in a machine learning algorithm, two additional techniques that you can use are:

• Using a resampling method to estimate the accuracy of the model
• Holding back a validation dataset

So, during your machine learning training, keep an eye on generalization when estimating your model accuracy on unseen data.

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1- Machine learning in Java

There’s a misconception that without learning Python or R, you can’t succeed in machine learning. However, the truth is that if you’ve got a Java development background, you can do without learning these popular programming languages. You should remember that Java gives support for development in any field you want, and data science is no different. By using third-party open source libraries, you can leverage your expertise as a Java developer to implement a data science algorithm and get things done. Though there’s no denying that Python or R come with their own set of advantages, you won’t need to learn them specifically to execute machine learning- or data science-related algorithms.

2- Leading machine learning libraries for Java

If you’re looking for some of the best machine learning libraries for Java, you’ll find Weka to be the most popular choice. Weka is suitable for data mining tasks, where algorithms can either be called from your own Java code or applied directly to a dataset. Weka contains tools for functions like clustering, classification, regression, association rules, and visualization.

Apache Mahout is another machine learning library for Java, which is designed to be enterprise-ready. This scalable and flexible ML framework comes with in-built algorithms to help you create your own algorithm implementations. Mahout’s distributed linear algebra framework allows statisticians, mathematicians, analytics professionals, and data scientists to implement their own algorithms.

ADAMS (Advanced Data mining And Machine learning System) is a flexible workflow engine that uses a tree-like structure to manage how data flows in the workflow. This means there exist no explicit connections that are essential. Using ADAMS, you can quickly build and maintain real-world workflows that are generally complex in nature.

Some other machine learning libraries for Java are ELKI (Environment for Developing KDD-Applications Supported by Index Structures), Deeplearning4j, JavaML, MALLET (MAchine Learning for LanguagE Toolkit), JSAT (Java Statistical Analysis Tool), and RapidMiner, to name a few.

If you’re a Java programmer or are adept in Java, the fastest route to a career in machine learning is enrolling in a machine learning bootcamp. Taught by industry experts and having ample hands-on training, such a bootcamp will help you fast-track your machine learning career dreams.

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1- An overview of data mining applications

If you’ve made up your mind to learn data mining, here are some applications of it, knowing which would help you choose your career path:

• Market Basket Analysis: This modeling technique is based upon a theory that buying a specific set of articles makes you more inclined to buy another group of articles. A retailer can use this technique to recognize a buyer’s purchase behavior, which can then be used to tweak the offerings or change the store’s layout to encourage buying. The use of differential analysis comparison of results between customers in different demographic groups or even between stores at different locations can help retailers make the necessary changes to boost sales.
• Fraud Detection: Unlike time-consuming and complex traditional methods of fraud detection, data mining can help find meaningful patterns in data. This information can then be used to collect sample records, classify them as fraudulent or non-fraudulent, and build a model using this data where the algorithm is designed to spot whether the record is fraudulent or not.
• Future Healthcare: Using data mining approaches such as machine learning, multi-dimensional databases, statistics, data visualization, and soft computing, the number of patients in each category can be predicted. This can help develop processes that ensure each patient gets appropriate care at the right time and the right place. Apart from helping to improve healthcare and reduce costs, data mining can also assist healthcare insurers in identifying fraud and abuse.
• Education: EDM or Educational Data Mining helps predict the future learning behavior of students, and even lets you study the effects of educational support as well as how advanced scientific knowledge can help in learning. Thus, data mining can help educational and training institutes focus on what they should teach and the ways to effectively teach apart from predicting the results of the students.

Apart from the above, many other industries like banking, transportation, manufacturing, etc. can also gain from data mining and data science.

2- How to learn data mining?

If you have your eyes set on the field of data science and want to master data mining, you can either get enrolled in a full-time course or find some bootcamps to join where you’ll learn all that you need to, albeit much faster than a traditional course. In case you plan to use bootcamps, remember that you’ll need a good statistical and machine learning foundation to understand what’s being taught and apply this knowledge to get useful information by cutting the noise of Big Data.

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Here’re the major ways through which the finance industry is leveraging the power of artificial intelligence and machine learning.

1- Better risk management

Probably the biggest impact of artificial intelligence and machine learning on the finance industry can be found when it comes to risk management. While traditional software applications can predict creditworthiness based on the static information obtained from financial reports and loan applications, implementation of machine learning technologies can help financial institutions to go much further. Algorithms identify the signs of probable future issues and analyze a client’s history of risk cases to help the authorities make an informed decision. They’re also able to identify present market trends together with relevant news items which can affect the ability of a client to pay.

2- Improved data security

Data security has always been at the top of the list of concerns for any financial institution. And if you consider the number of data breaches occurred during recent years, there’re reasons to be concerned. Traditional security tools aren’t capable of identifying modern sophisticated cyberattacks. To mitigate security risks, financial institutions implement advanced technologies like machine learning. Security solutions powered by machine learning are come with unique abilities to secure the financial data. The combined power of big data capabilities and intelligent pattern analysis gives machine learning security technology a robust advantage over traditional tools.

3- Enhanced customer experience

Like all other industries, the financial industry is also focusing on developing the top line by implementing advanced methods to offer custom services and better experience to customers. Many financial institutions have already introduced chatbots powered by artificial intelligence abilities that can analyze the voice of a customer and converse accordingly. With the help of machine learning and big data, these chatbots understand how to respond to the questions of customers’ – from transaction-specific questions to onboarding concerns. Additionally, technologies backed by artificial intelligence and machine learning are capable of making product recommendations and handling customer requests.

Parting Thoughts

In the finance industry, the disruption triggered by artificial intelligence and machine learning is increasing exponentially and toward greater economic impact than ever, both on the customers and the industry. By addressing all the major operational aspects and adding advanced features, these technologies are not only revolutionizing the entire industry but also improving the financial health millions of customers involved in the process. And from a business perspective, these technologies are driving a more fundamental and deeper shift in the finance industry.

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1- Targeting the audience

These days, one of the biggest problems experienced by businesses is that they fail to capture the attention of common people. The problem lies in the fact that advertisements often don’t connect with the audience. If you too are experiencing this issue, implementation of machine learning can help you sail through. You can use computer speech and vision to obtain valuable insights about your audience and use that information to create more targeted ads that result in more engagements which mean more profit.

2- Personalized customer service

Quality of customer service can make or break a business. With the help of machine learning tools and technologies, it’s now possible to combine years of data pertaining to customer services and merge it with NLP technology. The natural language processing algorithms make interactions with customers more personalized by leveraging that data. Each and every customer receives the most useful answers to their queries, which greatly increases the quality quotient of customer service. Additionally, the technology reduces the need for heavy investment that results in reduced customer servicing costs.

3- Personalize product recommendations

If you’re into e-commerce environment, then you probably know that the customers like to have personalized product recommendations delivered to them. For them, it improves their overall shopping experience and for you, it brings a new opportunity to sell more products. By leveraging the power of predictive analysis and machine learning, you can look beyond what the consumers searching for and try to connect those dots on what they most likely want. Matching customers to specific products or services will increase the chances of more conversions and thus, more profit.

4- Dynamic pricing

Change of pricing based on the level of demand or a need can bring a good opportunity to increase your revenue stream. For instance, Uber uses machine learning to create dynamic prices. It uses the technology to optimize the ride-sharing aspect and to minimize wait time. It can temporarily change pricing in an area to obtain a higher revenue stream and can lower rates where the demand is much lower. Machine learning can utilize available data to predict areas where demand may occur, which you can leverage to attract more customers, increasing your bottom line.

Final Thoughts

These days, businesses are capturing data from a huge number of sources and with the help of machine learning tools and technologies, they’re becoming able to develop a better brand exposure to obtain successful outcomes. Machine learning has already started impacting almost every part of the business domain. So, it’d be wise to integrate this technology with your existing technologies to improve profit.

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• Artificial intelligence is the intelligence demonstrated by machines. Any machine that understands its environment and is able to take actions that increase its chances of achieving some goals, can be described as an artificial intelligence-enabled machine. On the other hand, machine learning is one of the present applications of artificial intelligence.
• When machine learning goes beyond simple programming and can mirror and interact with people, even on the fundamental level, artificial intelligence comes into the picture. Though artificial intelligence needs machine learning to optimize decision, the former is the step beyond the latter. Artificial intelligence utilizes what it has obtained from machine learning to simulate intelligence.
• In artificial intelligence, a machine learns by gathering knowledge and understanding how to apply it. Here, the goal is to increase the chances of finding an optimal solution. It’s the study of training computers to try to do things which a human can do better at present. On the contrary, in machine learning, algorithms obtain the skill or knowledge via experience. It depends on big datasets to keep on reminding the data to identify common patterns.
• Based on capabilities, artificial intelligence can be distributed into two categories namely general AI and narrow AI. Based on learning methods, Machine learning can be distributed into three categories namely supervised learning, unsupervised learning, and reinforcement learning.
• The objective of artificial intelligence to develop smart systems like humans that can solve complex problems. The goal of machine learning is to let a machine learn from massive datasets so that they can provide accurate output.
• The key applications of artificial intelligence include customer support using chatbots, Siri, intelligent humanoid robot etc. The key applications of machine learning include Google search algorithms, online recommender system, Facebook friend tagging suggestions etc.
• Artificial intelligence holds a broad range of scope while machine learning comes with a limited scope.
• Artificial intelligence deals with structured, unstructured, and semi-structured data while machine learning works with only structured, and semi-structured data.

Both machine learning and artificial intelligence can leave valuable business implications. In the context of the coming future, both are imperative to our society. A robust understanding of both of these fields will be extremely important to comprehend the rapidly changing business world and how the devices we use everyday work. The promises and value of both these fields are being materialized because of each other. If you’re an aspiring candidate looking to step into these fields, this is probably the best time to begin your journey. As advancements and adoptions of both artificial intelligence and machine learning continue to accelerate, one thing can be assumed for sure – the impact will be profound.

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Modern businesses understand the power of big data, but they also understand that it can be even more powerful when merged with intelligent automation. And this is exactly where the power of machine learning comes into the picture. Machine learning systems help businesses in a multitude of ways including managing, analyzing, and using the captured data far more strategically than ever before.

In simple terms, machine learning is a set of technologies which empower connected computers and machines to learn, develop, and improve based on their own learning through various methods. These days, all the large corporations, giant tech organizations, and data scientists are foreseeing that big data is going to make a tremendous difference in the machine learning landscape.

Inherently, machine learning is an advanced subset of artificial intelligence that learns new things from databases on its own in a programmed manner. It’s based on the idea that says machines can learn from data, find out useful patterns, and become capable of making decisions without much human intervention.

While machine learning has been around for decades, nowadays it has become possible to automatically and quickly produce models which can analyze more complex, bigger datasets and deliver more accurate results quickly – even on a massive scale. And by creating these kinds of models, a business stands a better chance of finding profitable opportunities out.

Machine learning doesn’t involve any prior assumptions. Once they’re provided with the required data, machine learning algorithms can process that data and identify patterns. Then those patterns can be used on new datasets. Generally, this technology is applied to high-dimensional datasets. It means the more data you can provide, the more accurate your predictions will be. And this is exactly where the power of big data comes in.

As the industry and sciences are experiencing a phenomenal rise in data generation, this scenario has presented a great opportunity for machine learning and big data to come together and create machine learning techniques which have the ability to manage modern data types by attaining computational and statistical intelligence for navigation of massive amounts of information with no or minimal human intervention.

Machines learn from extensive calculations performed over datasets, meaning the more the data, the more effective the learning. With the emergence of big data together with the advancements in computing technologies, machine learning has already evolved from that of the past. With the steadily increasing proliferation of big data analysis into machine learning, machines and devices will get smarter and should be able to perform in a more advanced manner. This will eventually lead to improvement and advancement in machine learning solutions.

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But before delving deeper, let’s have a quick look at what are AI and machine learning basically.

1- AI and machine learning – What they are

Fundamentally, AI or artificial intelligence refers to the intelligence demonstrated by the machines. And ML or machine learning is a way using which professionals achieve AI. Machine learning can be considered as the ability of machines to learn utilizing statistical techniques without being programmed explicitly.

2- Ways AI and machine learning are facilitating average people’s lives

The concepts of AI and machine learning aren’t completely foreign to us as they have been heavily explored by popular media. There are lots of movies which have shown us a world where AI-enabled robots and machines hold the dominating power. And these have triggered, to a good extent, lots of negative impressions about AI and machine learning among the average people. However, despite how negatively movies demonstrate the power of AI and machine learning, these technologies are truly transforming average human lives into better ones. Let’s explore the most common aspects that are being impacted by AI and machine learning.

2.1- Banking and financial services

It’s hardly possible to count the number of people that have bank accounts. In addition, just consider the number of their associated facilities like credit cards which are in circulation. Now imagine how many hours human employees of these institutions would have to invest to sift through the transactions that are performed every day? And how much time and effort it would take to identify an anomaly? With the help of AI and machine learning, a huge number of banks and financial institutions have become able to review the quality of various applications and to analyze and predict risks, in an effort to make informed decisions. The so-called traditional industry is implementing AI and machine learning to increase user engagement. High-end technologies like predictive analysis, chatbots, voice recognition etc are helping minimize the gap between potential customers and financial institutions. These days, it’s possible for any customer to contact any of these establishments anytime and from anywhere and receive real-time replies.

2.2- Healthcare services

Both AI and machine learning have already acquired a significant part in our well-being and health. From being utilized for faster patient diagnosis to the prevention of illnesses – these technologies are being used on a regular basis by lots of healthcare service providers. These days, it’s possible to predict the potential health hazards a person may be susceptible to, depending on his/her genetic history, socio-economic status, age etc – which was simply unimaginable before the emergence of AI and machine learning. With the help of AI and machine learning-powered programs, healthcare service providers can cross-reference symptoms against databases that contain millions of cases of illnesses to expedite the process of diagnosing disease and illness, saving lives through faster and appropriate treatment. These technologies are also being adapted to expedite research works toward cures of different diseases.

2.3- Email

Almost every person uses email these days for a huge number of purposes. It may sound unlikely but your email inbox is a place where advanced technologies take place on a regular basis. There are two key aspects where email service providers use AI and machine learning. First comes the advanced spam filter. Unlike plain rule-based filters that aren’t much effective against spam as spammers can update their messages quickly to work around them, advanced spam filters continually learn from a wide range of signals like message metadata, words in the message etc to prevent spam. Another aspect is smart email categorization. You’ve probably seen that Gmail uses an approach to categorize the emails into primary, promotion, social inboxes. This is made possible with the help of AI and machine learning together with manual intervention from users. When some messages are marked in a constant direction by a user, a real-time increment to that threshold is performed by Google in order to achieve appropriate categorization.

2.4- Transportation industry

There’s a heavy influence of AI and machine learning on the present transportation industry can be found. These technologies have been instrumental in lowering threats triggered by reckless driving via the deployment of automation and sensory management. There are vehicles that can understand their surrounding parameters and thus, can take precautionary measures whenever needed to ensure passenger safety. Apart from vehicles, AI and machine learning technologies are to be deployed soon to prevent traffic congestion on roads and for traffic management.

2.5- Taking over tedious and hazardous jobs

AI and machine learning can seem to be a boon to humanity when we consider the fact that they liberate humans and enable them to focus on tasks in which they excel. These technologies take care of a wide range of tedious tasks that have to be performed in order to attain different results. Machines excel in performing cumbersome tasks, leaving enough time and room for humans to focus on more creative aspects of a business. In the financial sector, for example, AI and machine learning help financial analysts to get some relief from the monotonous nature of their jobs and concentrate on deeper analysis and research of all-round customer experience. In the context of hazardous jobs like bomb disposal, welding etc, AI and machine learning are helping the professionals to a great extent. These days, machines are taking over those jobs with the help of human intervention.

2.6- Social networking

Almost everyone has experienced it several times. When a user uploads pictures to Facebook, the faces get highlighted automatically and the service suggests friends to tag. If you wonder how it can find out which of your friends are in the picture, Facebook uses AI and machine learning techniques to recognize faces. It also uses these technologies to personalize their users’ newsfeed and ensure that they are viewing posts that interest them. Apart from Facebook, almost all other social networking platforms including Pinterest, Instagram, Snapchat etc leverage AI and machine learning to maximize user experience.

2.7- Online shopping

Online shopping has become almost an inevitable part of life for today’s tech-savvy customers. Have you ever wondered how e-commerce websites quickly return with a collection of the most relevant items related to your search? AI and machine learning are technologies that make it possible. Personalized recommendations on their home page, product pages etc are also examples of their deployment. Fraud protection is another aspect where these technologies perform a great job. Here, AI and machine learning are deployed to not only avert fraudulent transactions but to lower the number of legitimate transactions that are declined because of being falsely marked as fraudulent.

2.8- Home security and home automation

When it comes to home security, these days, a significant number of homeowners are deploying cutting-edge systems are deploying high-end cameras and security systems powered by AI and machine learning. These systems are capable of building a catalog of the frequent visitors of a home and thus, can detect uninvited guests instantly. Smart homes also offer a multitude of different types of useful features such as providing notification when the kids come back from school etc. When combined with appliances, AI and machine learning can make household management and housework seamless. From allowing the refrigerator to communicate with the oven to replenishment of food and supplies – all have become possible.

Final Thoughts

From the above examples, it can be concluded that a significant number of things, which were simply unimaginable before the emergence of AI and machine learning, have become possible these days. However, similar to other technologies, AI and machine learning also come with a significant number of negative concerns. The biggest one of them is that these technologies will replace humans in performing several tasks, making people jobless eventually. However, if these technologies are looked upon as tools rather than replacements, businesses should be able to attain a huge industrial growth. According to many experts, AI and machine learning have an opportunity to work together with humans. By nature, humans are good at raising the right questions while AI and machine learning are good at dealing with huge amounts of information. By working together they can leave a huge business impact. The future of these technologies isn’t exactly clear today, but they’ll surely have an impact on society as they are doing right now. We’ll have to wait to see whether that impact turns out to be positive or negative but it can be said that these technologies have a huge potential to make the lives of the people easier to a great extent.

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1- What is machine learning?

Put simply, machine learning is a subset of AI (artificial intelligence) and enables machines to step into a mode of self-learning without being programmed explicitly. Machine learning-enabled programs are able to learn, grow, and change by themselves when exposed to new data. With the help of this technology, computers can find valuable information without being programmed about where to look for specific piece information. Instead, they achieve it by utilizing algorithms which iteratively learn from data. Machine learning is unique within the field of artificial intelligence because it has triggered the largest real-life impacts for business. Due to this, machine learning is often considered separate from AI, which focuses more on developing systems to perform intelligent things. While the core concept of machine learning isn’t a new one, the ability to apply complicated mathematical calculations to big data automatically – quickly and iteratively – is a recent development.

2- Key characteristics of machine learning

In order to understand the actual power of machine learning, you have to consider the characteristics of this technology. There are lots of examples that echo the characteristics of machine learning in today’s data-rich world. Here are seven key characteristics of machine learning for which companies should prefer it over other technologies.

2.1- The ability to perform automated data visualization

A massive amount of data is being generated by businesses and common people on a regular basis. By visualizing notable relationships in data, businesses can not only make better decisions but build confidence as well. Machine learning offers a number of tools that provide rich snippets of data which can be applied to both unstructured and structured data. With the help of user-friendly automated data visualization platforms in machine learning, businesses can obtain a wealth of new insights in an effort to increase productivity in their processes.

2.2- Automation at its best

One of the biggest characteristics of machine learning is its ability to automate repetitive tasks and thus, increasing productivity. A huge number of organizations are already using machine learning-powered paperwork and email automation. In the financial sector, for example, a huge number of repetitive, data-heavy and predictable tasks are needed to be performed. Because of this, this sector uses different types of machine learning solutions to a great extent. The make accounting tasks faster, more insightful, and more accurate. Some aspects that have been already addressed by machine learning include addressing financial queries with the help of chatbots, making predictions, managing expenses, simplifying invoicing, and automating bank reconciliations.

2.3- Customer engagement like never before

For any business, one of the most crucial ways to drive engagement, promote brand loyalty and establish long-lasting customer relationships is by triggering meaningful conversations with its target customer base. Machine learning plays a critical role in enabling businesses and brands to spark more valuable conversations in terms of customer engagement. The technology analyzes particular phrases, words, sentences, idioms, and content formats which resonate with certain audience members. You can think of Pinterest which is successfully using machine learning to personalize suggestions to its users. It uses the technology to source content in which users will be interested, based on objects which they have pinned already.

2.4- The ability to take efficiency to the next level when merged with IoT

Thanks to the huge hype surrounding the IoT, machine learning has experienced a great rise in popularity. IoT is being designated as a strategically significant area by many companies. And many others have launched pilot projects to gauge the potential of IoT in the context of business operations. But attaining financial benefits through IoT isn’t easy. In order to achieve success, companies, which are offering IoT consulting services and platforms, need to clearly determine the areas that will change with the implementation of IoT strategies. Many of these businesses have failed to address it. In this scenario, machine learning is probably the best technology that can be used to attain higher levels of efficiency. By merging machine learning with IoT, businesses can boost the efficiency of their entire production processes.

2.5- The ability to change the mortgage market

It’s a fact that fostering a positive credit score usually takes discipline, time, and lots of financial planning for a lot of consumers. When it comes to the lenders, the consumer credit score is one of the biggest measures of creditworthiness that involve a number of factors including payment history, total debt, length of credit history etc. But wouldn’t it be great if there is a simplified and better measure? With the help of machine learning, lenders can now obtain a more comprehensive consumer picture. They can now predict whether the customer is a low spender or a high spender and understand his/her tipping point of spending. Apart from mortgage lending, financial institutions are using the same techniques for other types of consumer loans.

2.6- Accurate data analysis

Traditionally, data analysis has always been encompassing trial and error method, an approach which becomes impossible when we are working with large and heterogeneous datasets. Machine learning comes as the best solution to all these issues by offering effective alternatives to analyzing massive volumes of data. By developing efficient and fast algorithms, as well as, data-driven models for processing of data in real-time, machine learning is able to generate accurate analysis and results.

2.7- Business intelligence at its best

Machine learning characteristics, when merged with big data analytical work, can generate extreme levels of business intelligence with the help of which several different industries are making strategic initiatives. From retail to financial services to healthcare, and many more – machine learning has already become one of the most effective technologies to boost business operations.

Whether you are convinced or not, the above characteristics of machine learning have contributed heavily toward making it one of the most crucial technology trends – it underlies a huge number of things we use these days without even thinking about them.

3- Why the adoption of machine learning is getting thwarted?

It isn’t possible to predict whether machine learning-enabled systems will replace human workers or not. But it can be said that the biggest factor which is slowing down the advancements of cutting-edge technologies like machine learning is the lack of human skills. A new survey conducted by Cloudera reveals that for 51% of business leaders across Europe, it’s the skills shortage that was holding them back from implementation.

Machine learning, in a similar way like data science, is progressing in a clearly different way. As this technology trend involves capturing, collating, and interpreting data, an effective machine learning professional needs to a master of a huge number of disciplines – from mathematics and statistics to programming – all are required. As you may already imagine, machine learning is pretty complicated stuff and thus, it has become actually difficult for business leaders to find the right candidates who can help them to meet their digital transformation goals.

Those who are interested to become a machine learning professional should choose their learning avenue wisely. Though there are different types of avenues available including self-learning, traditional approach, bootcamps etc, most of them come with their own disadvantages. Given the broad spectrum of machine learning domain and its rapid advancements, aspirants need to understand that no course is actually comprehensive enough. If you too are interested in stepping into this field with real-life knowledge and possess the core skills to some extent, joining a bootcamp like the ones offered by Magnimind Academy would be a good idea.

Final Takeaway

These days, machine learning is gaining serious momentum throughout the world and it has become one of the key responsibilities of senior executives to steer their business in the right direction by leveraging its true characteristics. We are at the verge of entering a world where machines and humans will work in harmony to collaborate, campaign, and market their products/services in an innovative way which is more personal, effective, and informed than ever before. In order to attain this, it is the time for business owners to think about how they can leverage machine learning characteristics, how they want the technology to operate and behave to take the business forward. It’s also important to roll out an effective and transparent strategy encompassing machine learning. It’ll help the teams to understand how they can perform their tasks more effectively by embracing the power of machine learning.

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This post aims at giving you an overview of what is a natural language processing system, how it works, and some of its most common applications. Let’s delve deeper.

1- What is a natural language processing system?

At its core, natural language processing is a subset of artificial intelligence that helps machines comprehend, interpret, and manipulate natural language used by humans like text and speech. Its main objective is to fill the gaps between computer understanding and human communication. Natural language processing is an emerging technology which drives different forms of artificial intelligence we’re used to experiencing. While natural language processing is nothing new and has been studied for a significant number of decades, these days it’s advancing rapidly thanks to the availability of big data, enhanced algorithms, powerful computing, and an increased interest in the communications between humans and machines.

2- How a natural language processing system works

Performing natural language processing is difficult mainly because of the complex nature of human language. Understanding the human language comprehensively needs an understanding of the concepts and the words, and how they’re connected in order to deliver the intended results. While we can master a language quite easily, the imprecise characteristics and ambiguity of the natural languages are the two biggest aspects that make a natural language processing system difficult to be implemented.

In order to understand how a natural language processing system works, it would be helpful to understand how we use language. Each day, we generate hundreds, for example, of words in a declaration which are interpreted by other people to do numerous things. For us, it’s simple communication, but everyone knows that the words come with a deeper context. There’s always some context which we derive from what we speak and how we speak it. Whenever we say something to another person, that person can understand what we are actually trying mean. The reason is humans learn and develop the ability to understand things through experience. Here, the question is how we can offer that experience to a machine. The answer is we need to provide it with sufficient data to help it learn through experience.

The first working step of a natural language processing system relies on the system’s application. For instance, voice-based systems like Google Assistant or Alexa need to translate the words into text. Usually, this is done using HMM (Hidden Markov Models) system. The HMM utilizes mathematical models to determine what a person has said and translate that into text utilizable by the natural language processing system. Next step is actual understanding of the context and the language. Though the techniques slightly vary from one natural language processing system to another, they follow a fairly similar format on the whole. The systems attempt to break every word down into its noun, verb etc. This happens via a series of coded rules which depend on algorithms which incorporate statistical machine learning in order to help determine the context.

If you are thinking about the working procedure of a natural language processing system other than speech-to-text, the system skips the initial step and directly moves into analyzing the words utilizing the algorithms and grammar rules.

The final outcome is the ability to categorize what a person says in many different ways. The results get utilized in different ways depending on the underlying objective of a natural language processing system.

When you’re learning how a natural language processing system works, it’s also important to obtain an overview of its key components. Let’s have a quick look at each of them.

• Syntactic analysis: Syntax stands for the words’ arrangement in a sentence so that they can make grammatical sense. In natural language processing, syntactic analysis is utilized to assess the way the natural language gets aligned with the grammatical rules. Here, grammatical rules are applied by using computer algorithms to a group of words in order to derive meaning from them.
• Semantic analysis: Semantic analysis refers to a structure developed by the syntactic analyzer that assigns meanings. Here, computer algorithms are applied to understand the interpretation and meaning of words and the way sentences are structured. It’s important to note that this component only abstracts the real meaning or dictionary meaning from the given context.

Two popular methods are applied to implement a natural language processing system – machine learning and statistical interference.

3- Some most common applications of natural language processing systems

Natural language processing systems are being steadily implemented by a wide range of businesses, regardless of the domain and industry. Here’re some most common applications of this technology.

3.1- Chatbots

Chatbots are highly responsible for mitigating customer frustration about customer care call assistance. They offer virtual assistance for resolving simple problems of the customer where no skill is required. These days, chatbots are gaining lots of popularity and trust from both the consumers and the developers.

3.2- Language translation program

Natural language processing systems are often implemented to help language translation programs that can translate from one language to another (for instance, English to German). The technology allows for rudimentary translation before a human translator gets involved. This cuts down the time required for translating documents.

3.3- Sentiment analysis

Here, natural language processing systems are used to understand and analyze the responses to business messages posted on social media platforms. It helps the business to analyze the emotional state and attitude of the person commenting or engaging with posts. Widely used on social media and web monitoring, sentiment analysis is implemented by using a combination of statistics and natural language processing by assigning values to the texts and then attempting to identify the context’s underlying mood.

3.4- Search autocomplete

Search autocomplete is another application of natural language processing that a lot of people use on a regular basis. Internet search engines and some personal search engines of companies have integrated this application to boost user experience. Sometimes, users may know just one keyword instead of the entire search term or phrase. Search autocomplete helps them to locate the correct search term and get the answers faster.

3.5- Descriptive analytics

Capturing reviews for products/services comes with a multitude of benefits. They can not only boost confidence in potential customers but seller ratings can also be activated using it. Businesses use natural language processing-equipped tools that can pull together consumer feedback and analyze it, pointing out how frequently different types of pros and cons are mentioned.

3.6- Search autocorrect

It’s quite normal to make mistakes when typing something and fail to realize it. If the search engine on a business’s website doesn’t identify the mistake and comes up with ‘no results’, it’s natural for potential buyers to assume that the store doesn’t have the answer or information they are looking for. With the help of natural language processing systems chances of these occurrences can be reduced by equipping the website with a search autocorrect feature. It identifies errors and comes up with appropriate results without needing users to perform any additional steps, similar to that of a Google search.

3.7- Form spell check

Spell check is one of the most commonly used applications of natural language processing systems. It’s simple to use and can eliminate lots of headaches for both agents and users. Not every user takes the time to compose grammatically perfect sentences when writing to a sales agent or a customer help desk. With the help of natural language processing-equipped contact forms, businesses are now able to make the lives of both the users the customer support executives because error-ridden messages aren’t only difficult to interpret but may result in frustration and miscommunication for everyone involved.

Parting Thoughts

At this moment, natural language processing is trying to identify nuances in language meaning occurring due to different reasons – from spelling errors or dialectal differences to lack of context. Despite all these limitations, the discipline is developing at quite a fast pace and we can expect to reach a certain level of advancement in the near future. We can expect to see that with the help of the natural language processing systems, future machines or computers will be able to learn from the information available online and apply that information in the real-world. However, a lot of work is needed to be performed in order to enable the machines to attain that high level of intelligence.

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