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Tag: machine learning training

How Machine Learning is a Boon For License Plate Recognition?

Posted on by Imarticus Learning

Machine Learning has weathered some tough days, pulling through to become a powerful technological force capable of leading and creating real-world change.

A prime example of this is the use of Machine Learning in automated surveillance systems on roads in busy metropolises. License plate recognition, for example, has transformed from a pipe dream to current reality, thanks to image processing and recognition capabilities of AI and ML systems.

Out of all the solutions posited towards vehicle movement and management, machine learning solutions are the most accurate because:

  • They derive crucial information from vehicles on the move
  • They are real-time and efficient
  • They are self-taught and don’t need human resource support

What is license plate recognition?

It is the process of detecting and identifying license plates, through Optical Character Recognition, to be run against an existing database. The most basic recognition system consists of three steps:

  • Detecting the actual license plate
  • Segmenting characters into individual images
  • ML algorithms recognize each character

License plate recognition is a boon for many government and private entities, especially when used in tandem with an existing robust database.

Where is license plate recognition thought to be useful?

As the number of vehicles increases across metros and crime also shoots up, many law enforcement bodies find it increasingly difficult to track offensive vehicles and levy fines, book drivers or conduct searches in good time.

Identification of traffic defaulters: ML-based license plate recognition systems allow bodies like the police and traffic control to identify vehicles that break rules like driving over the speed limit, not wearing seatbelts or having broken headlights. Depending on the business of the junction or road, the technology can be used in tandem with human traffic police to ensure accuracy and efficiency.

Recognition of abandoned wanted or stolen vehicles: By integrating ML-based systems on mobile devices, police or other law enforcement bodies can recognize and identify vehicles whether wanted, stolen or abandoned. This reduces a lot of time wasted on identifying vehicles or organizing paperwork. It also allows the enforcers to contact the person in question or make arrests were necessary.

Automating of toll collection: By adding LP systems to toll booths, law enforcement authorities can conveniently collect tolls without the need for actual service personnel manning the station.

A good LPR system is set apart from the rest by the following salient features–

Functions in all environments: Using humans to identify license plates in extreme weather leaves plenty of room for errors. Good LPR systems work regardless of the environment– stormy, cloudy, foggy or dusty– thereby heightening the accuracy and increasing real-time problem solving or escalation. They also function on low-resolution images, when the image is at an angle or where the image is blurry and of a fast-moving vehicle.

Identifies plates on all vehicles: Depending on the vehicle, indeed even the country it’s in, the license plate can differ in color, font, and sequence. It could also be placed on different parts of the vehicle body, which means a good LPR tool needs to be able to identify the right markings in the right spot and identify the sequence correctly.

The final word

Detecting and recognizing license plates is a task that grows increasingly cumbersome as the number of vehicles and registration requirements increase. To get a head start on this emerging field, students, freshers, and industry professionals must engage in a Machine Learning Course and open up new avenues for themselves. Using a license plate recognition system that is dynamic, ML-based and scalable is a positive step towards managing the chaos and getting real-time, positive results.

What Is The Quickest Way to Learn Math For Machine Learning and Deep Learning?

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Synopsis

Math is integral to machine learning and deep learning. It is the foundation on which algorithms are built for artificial intelligence to learn, analyze and thrive. So how do you learn math quickly for AI? 


Machines today have the ability to learn, analyze and understand their environment and solve problems on the basis of the data given to them. This intelligence of the machines is known as artificial intelligence and the ability to learn and thrive is known as machine learning. Algorithms form the crux of everything you do in technology and a Machine Learning Course provides you with an understanding of the same. 

 

Today, individuals who are proficient after completely a Machine Learning Certification is highly sought after and employed. Companies invest a large sum of money to have professionals trained in AI as the applications of AI are vast and cost-effective.  It is a lucrative career to pursue one that involves complex and challenging problems that need to be solved in creative ways. 

 

Mathematics forms the foundation of building algorithms as all programming languages use the basics. Binary code is the heart of machines and the language used to teach them things is the programming language. So do you pursue Machine Learning Training, and also learn math quickly at the same time? 

 

Here are a few ways to understand how math is applicable in AI 


Learn the Basics 

Important sections such as  Statistics, Linear Algebra, Statistics, Probability and Differential Calculus are the basics of math that one needs to know in order to pursue learning a programming language. While this may sound complicated, they form the basis for machine learning, so investing in courses that teach the above-mentioned functions will go a long way in programming.  There are plenty of online resources that are useful repositories when it comes to learning math for deep learning. 

 

Invest Sufficient Time

Learning math depends on the ability to absorb and apply the math learned in machine learning. Applications of statistics, linear algebra is important in machine learning and hence investing 2-3 months to brush up on the basics go a long way. Constant applications of the lessons learned also helps when it comes to math for AI. Since the principles are the same but the various derivatives and applications can change with the algorithm constant practice and brushing up will help while learning the code. 


Dismiss The Fear

One of the biggest ways to learn math quickly for machine learning is by dismissing the fear associated with numbers. By starting small and investing efforts, one can move forward in the code. Since there is no shortage of resources when it comes to learning math, taking the initial step and letting go of any fear towards the subject will greatly help. 


Conclusion

Learning a programming language whose principles are based on mathematics can sound daunting and tedious but it is fairly simple once you understand the basics of it. This can be applied while programming for machine learning and artificial intelligence. 

Is Statistics Required for Machine Learning?

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What is Statistics?

Statistics is a branch of mathematics that is used for comparing and analyzing various data points and the numbers associated with them. It also includes the study of numbers and drawing out insights from those numbers. Some of the statistical measures include average, median, mode, variance, standard deviation, correlation, regression, etc. Some of these help in analyzing single sets of data while others are used in comparing two or more sets of data and then making a comparative analysis in the form of trends and patterns. Often these tools are also brought into play when it comes to predicting future numbers.

What is Machine Learning?

Machine Learning is the application of artificial intelligence where the systems are programmed to perform a specific set of tasks. The computers are programmed to function automatically depending on the various scenarios and come up with the required results. It enables the analysis of huge data for drawing out various business insights.

Also, it makes the sorting and analysis of data quick and easy as the automation is brought into play with the help of machine learning. It is a really powerful tool in this data-driven world of today. It collects data from various sources as given by the algorithm, prepares it for analysis and then evaluates this data for bringing out insights and also throws light on various performance indicators in the form of patterns and trends.

Statistics and Machine Learning

Both Statistics and Machine Learning deal with the analysis of data therefore one could guess that the two areas are interrelated. Various statistical methods are used to transform raw data and bring out various results. Many believe that knowing Statistics is a prerequisite for understanding Machine Learning. Statistics is important as the data sets have to be created which can be easily made if one has prior knowledge of Statistics. Also, with the help of statistics, the observations are transformed and put to good use.

Machine Learning has a deep relation with Statistics and the elements of statistics such as the collection of data, classification, and sorting of data, analysis of data, etc. Predictive modeling can be done by someone who at least has a basic understanding of Statistics. Machine learning is also known as “Applied Statistics” as it practically uses various statistical theories and principles to drive growth and various results.

Data analysis is important for machine learning and statistics is an art of handling data. It is the primary skill that drives machine learning algorithms. Statistics plays a very important role when it comes to machine learning. One needs to know about the various parameters on which the data shall be analyzed to bring out desired results.

Methods such as Correlation and Regression are often used to compare various sets of data and these tools are built into algorithms with the help of machine learning so that these numbers of comparison can be automatically calculated and a comparative study can be made based on these numbers. Learning Statistics before getting into machine learning is the best way to go about it. Various Machine Learning training will also give you an idea about statistics and how it is applied to Machine Learning.

Conclusion
Machine Learning and Statistics are two parts of the same coin. Machine Learning makes use of statistics for sanitizing data and on the other hand, Statistics is given a practical shape and is made applicable with the help of machine learning. Therefore, it becomes easy to conclude that one must have at least a basic understanding of statistics to understand the aspects of Machine Learning.

What Are The Major Fields Of Robotics?

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Before we jump into the major fields of robotics, we should have a clear idea about the term robotics. It is a diverse field related to construction, engineering, and operation of robots in many commercial industries and consumer fields. Robotics involves the study of a physically constructed technology system and its performance or role in any interface or new technology.

The fields of robotics

There are five major fields of robotics namely: 

    • Operator interface – This refers to the Human-Robot Interface that explains how the human and the robot communicate. The robot works based on the commands conducted by the human. The best example of this is a child playing a video game. The joystick here acts as the interface between the human and the machine.
    • Locomotion based robots – In this case, robots perform tasks based on locomotion. Here, you will at times find human-like robots using legs for moving. Some flying robots and drones use the propellers for movement. Some may even use wheels depending on the environment they are i.e. air, water or land.
    • Component-based robots – In this case, it is the component within the robot that makes it do the specified job. Based on the situation, these human-like robots may use mechanized arms and fingers, claws or pushers to conduct the job. This is especially required in industries that are into heavy lifting and moving of things.
    • Ways of delivering a message to the robot – Commands need to be given to the robots with the help of different means. Today there are over one thousand programming languages and each robot interprets the given instructions in their own way. Some robots are even modified so that they are able to adapt to their changing environment.
    • Based on how robots to sense and to perceive – This denotes how the machines identify things in and around their environment and react. For example, when a robot comes in contact with an obstacle, what direction should it take. This component is fed into the robot that helps them make the right decision.

Role of machine learning in robotics

Robot learning is a field of research that connects both machine learning and robotics. Machine learning training helps robots deal with dynamic interaction and adapt them to situations where they can avoid obstacles and maintain productivity. Machine learning is often used by computer vision algorithms when it comes to robotics and these applications are normally not referred to as “robot learning”.

The future of robotics

This rapid advancement in the robotics sector in India will ultimately affect the job market and become a major cause of unemployment. Due to the huge possibility of robotics in warehousing, the Indian warehouse automation market is anticipated to grow at a CAGR of 10-12% in the period 2015-2020.

The future of the professionals working in fields like automation is also uncertain, with the new technological development. It will also lead to layoffs and downsizing of the employee strength. Some areas will not be affected immediately, but in the near future, they will be impacted in some way or the other. Robotics is also expected to play a key role in the “Make in India” initiative and attract global manufacturers to invest in the country. Hence, we can see that the future of robotics has both positive and negative impacts.

Conclusion

In order to build a fully functional robot, all these major fields of robotics should be carefully incorporated to ensure maximum effectiveness. Proper incorporation guarantees that the robot would work at an optimum level without any glitches.

Using Machine Learning to Conduct Sales Forecasting

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Using Machine Learning to Conduct Sales Forecasting

As the future of machine learning slowly transforms the present, models and algorithms are increasingly becoming more powerful, flexible and scalable. They’re also perfectly capable of being adapted into any industry, regardless of how they’re used and to what end.

An example of that is the use of machine learning in sales forecasting. When product catalogs expand in volume and variables become more complex, machine learning algorithms make the process of sales prediction easier and more real-time in the following ways:

Analyzing Sales Variables

Machine learning algorithms can be used to sift through data dumps of prices and stocks as well as to conduct analyses of traffic to certain products and pages or identifying trending products. Thanks to such analyses, retail and e-retail firms can identify which products are likely to perform well and what measures to take to ensure the success of other offerings now and in the future. Some of the variables that directly affect sales are:

  • Price of products
  • Supply of products
  • Market trends
  • Demand for products
  • Marketing tactics

Revising Compensation

Sales compensation is a powerful driving force in motivating sales employees to achieve targets. However, what’s often seen in companies is that sales targets can be unachievable or based on incorrect metrics that can hamper top employees’ performances, even cause them to leave. Machine learning systems can help to identify Key Performance Indicators (KPIs) based not only on past performance but also on the overall performance of the company and external influential factors. Here are some ways in which machine learning training can set better sales goals:

  • Setting achievable targets
  • Adapting the right frequency for revision of metrics
  • Identifying the ideal incentive
  • Implementing compensation and revisions

Identifying and Maintaining Benchmarks

Benchmarks are ideal scenarios that firms use as a target to meet or emulate. Machine learning algorithms can be leveraged to identify these benchmarks using the aforementioned sales indicators as well as past data dumps of employee performance and business targets. Benchmarks are just that– they needn’t be used if it’s business as usual. But to stay ahead of competitors and identify winning strategies, it’s essential that a firm has a goal to work towards and an ideal situation to use as a comparison. Upon failing to meet benchmarks, companies can turn the lens inwards to identify loopholes in the sales cycle, demotivation in employees or products or services that have fallen out of favor.

Maximizing Sales

A data dump is the most important asset to a machine learning algorithm, an arsenal of sorts. This arsenal can be used to predict prime prices that are attractive to customers yet profitable for the company. They can also be used to upsell, cross-sell and recommend stock-ups to avoid going out of stock and losing out on potential sales. Future sales can also be predicted; this, in turn, can be used to drive investments into departments or services and advise marketing strategies to maximize the bang for their marketing buck.

Carrying out A/B Testing

A/B testing is crucial for firms who do not know what marketing strategy will work or what products will do well in the market despite initial research, however thorough. Machine learning can conduct such tests without running too much risk to the business or demanding human resources and attention.

Conclusion

Machine learning has permeated every industry today, so much so that every good machine learning course explores the benefits of technology across fields. By using machine learning algorithms and persevering through trials, businesses can transition into higher performances, better sales, and more impressive profit margins.

Machine Learning and Information Security: Impacts and Trends

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Machine Learning and Information Security: Impacts and Trends

Gone are the days when we needed to patiently sit and teach computers how to perform complex tasks that were backed by human intelligence. Today, the machine teaches itself– far from ‘magic’, it’s a tool that has revolutionized industries across the board today.

For context, machine learning is as significant a change for the world as the introduction of the Internet was. The future of machine learning encompasses more than just tech and related industries. Cybersecurity– more specifically, information security– has been heavily impacted by the introduction of machine learning in a mostly positive manner, but some grey areas exist.

What is Information Security?

InfoSec is the network of processes and systems designed for and deployed to safeguard confidential information, largely business-related, from destruction or modification in any way. InfoSec is not the same as Cybersecurity, albeit it is the part of it that is dedicated exclusively to data protection.

The types of Information Security span cloud security, cryptography, infrastructure protection and detection and management of vulnerabilities. Most Machine Learning training courses brief students about these facets, not least because they’re universal in their use across industries.

Machine Learning in InfoSec: Impacts and Trends

Automate repetitive tasks

Setting up ML algorithms to take care of everyday threats can help ensure a regular check on the underlying security. This also allows security analysts, supported by more complex algorithms, to focus their strength on bigger tactical fights an set up bulletproof systems. This frees up a lot of time on the team’s hands and cuts costs on holding onto employees for repetitive tasks alone.

Endpoint security control in mobile devices

With mobile passwords being the quickest and easiest springboard to accessing information worth selling, cybercriminals are increasingly preferring to target mobile devices. To counter this, machine learning techniques include ‘zero trusts’ no sign-on approaches that eliminate passwords and cloud-based authentication systems.

Predict and preempt strikes on systems

Predictive analytics is fast becoming a core facet of InfoSec systems today because continuous analysis and correlation mean better chances of recognizing patterns and threats ahead of the actual strike. Using AI and machine learning techniques to capture, analyze and classify data in real-time is a benefit that no other system has offered so far, least of all human systems. By identifying potential threats, businesses can prepare in advance by strengthening security, putting extra authentication processes in place and running audits.

Cloud-based security systems

In place of saving millions of customer data on chunky servers prone to breach, businesses are increasingly moving to cloud-based security systems. These systems allow all information to be kept in one place with hefty security barriers in place, with the help of machine learning, to prevent breaches. These systems keep the reins of authority in the hands of a few, making it easier to trace leaks, if any, and allow for timely intervention.

The future role of machine learning

In setting up a top-of-its-class, dynamic security landscape, machine learning plays several roles, both routine and complex. Machine learning training is the talk of the town today because companies want their employees to be more than capable of using machine learning data for the betterment of InfoSec at any organization.

AI Helping The E-commerce Stores To Dramatically Increase Conversions

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In today’s era of continuously developing science and technology, artificial intelligence has touched almost every possible domain in our lives. It has revolutionized technology to mimic the human brain. From self-checkout cash counters at malls to advance security check systems at airports, AI has left its footprints everywhere. It has set a considerable benchmark in the field of e-commerce by providing a wide range of personalized experiences and creating new standards. According to a recent analysis, more than 80% of the human interactions in the future will be held by artificial intelligence. The key features that create a dramatic increase in conversions are listed below.

Chatbots

Chatbots are intelligent conversational agents that use Natural Language Processing as a base to provide the machine with the ability to understand and process human interactions. They are designed to cater to the user needs by providing product recommendations, customer support, and fulfillment of customer purchases. A large number of sales chatbots are being used to provide a personalized user experience. They interact with the customer in a simple question-answer format and provide a suggestion based on the previous purchase history or other buying trends, thereby creating an increment in the sales.

Recommendation Systems

Most of the websites dedicated to online shopping make use of recommendation systems. These systems collect data related to each customer purchase and make suggestions using artificial intelligence algorithms. The main concept used in the recommendation system is to create the sale boost either by suggesting a product based on the purchase history (personalized recommendation) or by promoting the popular products (non-personalized recommendation). They provide an appropriate product suggestions thereby optimizing the website for a boost in sales and increased conversion.

Visual Search Engines

With the help of AI, desired products can be found with just a single click. All a customer needs to do is to take an image of the desired product and provide it as an input to the search engine. The search engine recognizes the desired item based on its specifications and provides appropriate results. Providing accurate results saves the customer time and effort in going through multiple products and the relevance of the system results in increased sales.

Speech Recognition

As AI is expanding every day, it is providing great support for e-commerce by implementing user efficient techniques like voice assistants. By analyzing the sentences used by the customer for the desired product, accurate suggestions are provided thereby raising the bar of user convenience in online shopping. Thus, voice assistance has been proved as one of the most successful tools in providing a smooth, personal and more humane touch to the user experience.

Conclusion

The application of AI in the e-commerce sector has greatly succeeded in providing a better user experience and significant sales increment. It has directed the trend from the traditional shopping approach of visiting the shops to a more advanced yet convenient method of online shopping. It has created a platform for increased conversions with the help of technologies like speech recognition, visual search engines, recommendation systems, better user experience and much more. In simple words, it has made the process of buying and selling swift and efficient for the users.

Artificial intelligence, through its attempts of increasing conversions, is creating rapid advancements in the field of science, technology, and e-commerce. This can be considered a key reason for the recent rise in the number of individuals veering towards artificial intelligence training and opting for machine learning courses.

E-commerce firms are continuously working in the direction of improving the artificial intelligence tools to meet the modern-day market trends. SEO (Search Engine Optimization) integrated with proper marketing strategy and artificial intelligence features like personalized recommendations and sales intelligent chatbots is continuously creating wonders in the e-commerce sector.

What Are The Important Ways That AI Is Helping E-Commerce Stores?

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What Are The Important Ways That AI Is Helping E-Commerce Stores?

The e-commerce industry has proved to be a boon for all the shopaholics who are too lethargic for a regular brick and motor engagement. Growing in double digits the expansion in the e-commerce industry is unmatched by any other and with the potential to grow multiple folds in the coming years it has set new highs.

In a broad sense of things, the concept behind the e-commerce world is simple, creating on an online marketplace with multiple stores available to shop anytime using the means of smartphones and other computerized devices that support web surfing.

The virtual market is not bounded by geography, having its customer base all across the world. What’s different about this shopping escapade is that it makes the entire store available for you to facilitate your shopping spree, all with a few clicks. I wonder how many times it happens that I am not sure about what exactly I need to purchase unless acquainted with the varieties available.

Now if we have to walk by several stores to find out what could be bought it will be tiresome, to say the least. Let’s assume that we somehow managed to step into each of them, how will we compare all the available products in real-time? That’s where the e-commerce industry adds value and steals the show with convenience.

The e-commerce stores not only help to bring everything together but also helps to search select and choose by providing valuable suggestions and insightful product descriptions. It also lets you read into the feedback provided by the users of the products that might help you buy better.

In the tangible world, we have a shop for every need, we have shopping complexes for multiple segments. This evolution went a little further in the era of the internet with e-commerce where we have all the product segments from all the known brands under a few keystrokes.

AI applications in the e-commerce industry

While shopping at stores with a physical address on the map, what attracts the most apart from quality goodies is the presentation and organization of the products.

Similarly when buying goods online what helps increase engagement and purchase? The answer is better to search for tools and classified product segments. This is where AI fits into the e-commerce must-have tools.

The high-tech tech AI-enabled solutions can also help in searching product descriptions and other relevant details to form a variety of keywords that might match with the user’s search and help discover the product better. This doesn’t stop here, the AI-powered solutions also help with product selection by asking some intelligent questions and narrowing down the list for us.

At times it so happens that we know what we are looking for but the name is unknown to us and thus we feed in a variety of keywords to complete our search. The predictive search mechanism provided by AI technology uses our past search and purchase history helping us identify what we might be looking for with relative ease saving a lot of time and keystroke efforts.

Arrangement of products and tidiness are some of the key drivers of customers in the traditional brick motors store, how do you implicate this approach online? Well, the answer doesn’t require a brainstorming session, it is through the website design.

Making the website aesthetic needs a well-planned web design that not only looks good but also goes along with the objective of the website. From optimized website design testing to improving decisions with auto traffic analysis & better sales funnel structuring, Artificial Intelligence Training delivers on all aspects of customer conversions and engagement.

In the present-day scenario conversational chatbots are mainstream for better customer servicing, it could also be seen as a norm, whatever site you visit for your purchase you are bound to be greeted by a bot. This evolution has been propelled further by a new wave of intelligent sales chatbots. This new AI by-product is hyper-personal in its functioning, providing customized recommendations and suggestions for better conversion.

Conclusion

AI has improved the e-commerce industry to a great extent by providing better search options for product searches to suggesting an optimized website layout for better conversions. Apart from the mainstream chatbots for customer servicing this new AI wave has welcomed the trendy sales chatbot that uses customer preferences data for good by providing a customized and hyper-personal shopping experience.

A Look at the 3 Most Common Machine Learning Obstacles

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A Look at the 3 Most Common Machine Learning Obstacles

When we talk about artificial intelligence (AI), the research and its findings have surpassed our little expectations. Some experts also believe that this is the golden age of AI and machine learning (ML) projects where the human mind is still surprised at all the possibilities that they bring to the table. However, it is only when you start working on a project involving these advanced technologies that you realize that there are a few obstacles that you need to address before you can start throwing a party.

Predictive assembly line maintenance, image recognition, and automatic disease detection are some of the biggest applications of ML-driven automation. But what are the hurdles that data scientists need to cross if they want to practically execute these applications and gain the desired outcome?

This article will give you an overview of the three common obstacles involved in machine learning models.

Common Machine Learning Obstacles

On theory, machine learning evangelists tend to liken the technology to magic. People scrolling through Facebook watch videos that use buzzwords in their captions and believe that AI can do wonders. Of course, it can, but when you think practically it is not as easy as it sounds. Commercial use of machine learning still has a long way to go because the reference dataset that is essential for any such model to function needs to be tidied up and organized at such a minute level it becomes tedious.

Ask any data scientist who has worked in a deep learning project and she will tell you all about it: the time, the resources, and the particular skills needed to create the database, sometimes known as a training set. But these are challenges found in any project. When you deal with machine learning, there are a few peculiar ones too.

Let’s dig deeper into these three common obstacles and find out why they are so integral to the larger machine learning problem.

The Problem of Black Box

Imagine a machine learning training program that is developed to predict if a given object is a red apple or not. During the early days of machine learning research, this meant writing a simple program with elements that involved the color red and the shape of an apple. Essentially, such a program was created through a thorough understanding of those who developed it. The problem with modern programs is that although humans developed it, they have no idea how the program actually detects the object, and that too with unprecedented accuracy. This is also one of the issues hampering the wide application of data classification models.

Experts have studied this problem and tried to crack it, but the solution still seems elusive because there is absolutely no way to get into the process while it is running. Although the program gives out fabulous results – results that are much needed to detect if a given fruit is a red apple or not from a wide range of fruits that also include non-apples – but the lack of knowledge as to how it works makes the whole science behind it feel like alchemy.

If you have been following world news related to AI-enabled products, this is probably the biggest cause of ‘accidents.’ That self-driving car hit a divider when there was no reason for it to hit it? That’s the black box problem right there.

What Classification Model to Choose?

This is another common obstacle that comes in the way of data scientists and successful AI tools. As you might know, each classification model has its own set of pros and cons. There is also the issue of the unique set of data that has been fed to it and the unique outcome that is desired.

For example, a program wanting to detect a fruit as red apple is totally different from another program that requires the observation to be classified into two different possibilities. This puts the scientists behind the program in a difficult situation.

Although there are ways to simplify this to an extent, it often ends up as a process of trial-and-error. What needs to be accomplished, what is the type and volume of data, and what characteristics are involved are some of the common questions that need to be asked. Answers to these will help a team of engineers and data scientists selects an appropriate model. Some of these popular models are a k-nearest neighbor (kNN), decision trees, and logistic regression.

Data Overfitting

Understanding this will be easier because it can be described using an example. Take, for instance, a robot who has been fed the floor plan of a Walmart store. It is the only thing that has been fed to it, and the expected outcome is that the robot can successfully follow a set of directions and reach any given point in the store. What will happen if the robot instead is brought to a Costco store that is built entirely differently? Assumption tells us that it won’t be able to go beyond the initial steps as the floor plan in its memory and the floor plan of this new store do not match.

A variation of this fallacy is what is known as data overfitting in machine learning. A model is unable to generalize well to a set of new data. There are easy solutions to this, but experts suggest prevention rather than cure. Data regularization is one of those prevention mechanisms where a model is fed data sufficient for the requests that it will handle.

The above-mentioned three obstacles are the most common, but there are many more like talent deficit, unavailability of free data, and insufficiency of research and development in the field. In that vein, it is not fair of us to demand a lot more of the technology when it is relatively new compared to the technologies that took years and decades to evolve and are part of our routine use (internet protocol, hard disks, and GPS are some examples).

If you are an aspiring data scientist, the one thing that you can do is contribute to the research and development of machine learning and engage in more discussion both online and offline.

Difference Between Data Classification and Prediction in AI

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In machine learning, it is important to understand the difference between data classification and data prediction (or regression) and apply the right concept when a task arises. This is because classification involves a premature decision (as in a combination of prediction and past decisions) and the decision-maker may end up making a decision based on incorrect elements. Not a good situation when error-free action is the main aim of using such a model.

As artificial intelligence and associated technologies evolve, for a program to make the right decision actively depends on how a particular task compares with a set of reference data. This set of data, in most cases, is crucial in the development of that system.

So, let’s take a closer look at these two separate concepts and then go through some of their core differences in the context of decision-making in AI and other related fields.

What is Data Classification?

In simple words, classification is a technique where a system determines what class or category a given observation falls in so that the future course of action can then be defined. Machine learning training uses a three-pronged approach to do this:

  • Application of a classification algorithm that identifies shared characteristics of certain classes
  • Comparison of this set of characteristics with that of the given observation
  • The conclusion to classify the given observation

Let’s take the example of a real-life event to better understand this.

If a bank wants to use its AI tool to predict if a person will default on his loan repayments or not, it will need to first classify the person. For a bank, the two classes in this context will be defaulters and non-defaulters. It does not care about any more details.

The tool will execute the above-mentioned three-step process to conclude if the person will fall in the first or the second category.

While this process may seem unbiased, there is one major drawback that data classification has. This is called the black box problem. It involves a lack of identification of the specific characteristic that must have influenced the decision to classify a given observation under one class. It can be one or more characteristics but pinpointing which ones will be impossible.

What this means is that the bank cannot use it as an example to deter other applications. It has to run the tool every time to assess the person. And for all persons, this defining characteristic may differ.

How Does Data Prediction Compare?

If data classification deals with determination based on characteristics, data prediction focuses on coming up with a more polished output (e.g.: a numeric value). Such type of regression analysis is often used for numerical prediction.

In the above example of the bank loan defaulter, a data prediction model will come up with the probability of how likely a person is to default on loan repayments rather than mere classification.

What is the Difference?

As one can observe, there is a stark difference between data classification and data prediction. Although both of them are widely used in data analysis and artificial intelligence tools, they often serve separate purposes.

According to Frank Harrell, a professor of biostatistics at Vanderbilt University, classification is a forced choice. He takes the example of the decision that a marketer has to take when she has to plan how many of the total target audience should she focus on when building a marketing plan on, say, Facebook. Here, “a lift curve is used, whereby potential customers are sorted in decreasing order of estimated probability of purchasing a product.” To gain maximum ROI, the marketer targets those that are likely to buy the product. Here, classification is not required.

When to make a forced-choice and when not to totally depends on the observation being made. This is why most algorithmic models working on data analysis cannot be used for all types of results. Classification and prediction both depend on what the required output is.

Now, let’s take a look at some pointers that will further clarify the differences between these two models:

  • In classification, a data group is divided into categories based on characteristics
    • In prediction, the reference dataset is used to predict a missing element
  • Classification can be used when the type of desired output is already known
    • Prediction, on the other hand, is used when the type is unknown
  • Classifiers are dependent on previous data sets. They require abundant information to provide better prediction
    • Numerical regression, on the other hand, can provide usable data and act as a starting point for future activities

What these differences highlight is the need to apply them cautiously. Choosing one method over another might feel like a free option, but it is much more than that. While preparing the data will involve challenges in relevance analysis and data cleaning (to chuck out as much noise as possible), one has to also consider factors such as accuracy, speed, robustness, scalability, and interpretability.

As one can assume, these two models have differing values as far as the above factors are considered. The computational cost, for example, is not an important topic at the moment in the artificial intelligence field. But once these models become a part of the everyday analysis, a discussion will surely pop up. And that’s when a more learned decision must be taken.

Finally, it is important to understand that both classification and regression (prediction of a numerical value) are types of predictive analysis. The difference is mainly in how they interact with observation as well as how the reference data set is used.

Choosing which one to go with should perfectly fit the case otherwise one will end up with the wrong choice. Building such predictive models should, therefore, be a joint project involving data scientists and business users. In the example of the bank, if loan agents can directly work with data scientists during the development of these models, it aids in removing at least the known errors out of the equation.