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Tag: Artificial Intelligence

How Statistics Relate to Machine Learning?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Introduction

Machine learning and statistics have always been closely related to each other. This led to an argument about whether it was different from machine learning or formed a part of machine learning. Several Machine learning courses specify statistics as one of the perquisites for machine learning.

Hence, we need to develop an understanding of the fact if statistics relate to machine learning and if it does, how?

Individuals working in the field of machine learning concentrate on the task of model building and the result interpretation from the model that was constructed while the statisticians perform the same task but under the cover of a mathematician concentrating more on the mathematical theory involved in the machine learning task concentrating more on the explanation of the predictions made by the machine learning model. So, we can say that in spite of the differences between statistics and machine learning, we need to learn statistics in machine learning.

Statistics and machine learning

Both statistics and machine learning are related to data. Although they work with the data in their way, some requirements are needed by both and hence they form a close relationship with each other. Given below is a step by step analysis as to how statistics relate to machine learning.

Data preprocessing requires statistics

To proceed with the machine learning task, cleaning of data is a mandatory step. This process involves tasks such as identifying missing values, normalization of the values, identifying the outliers, etc. These operations call for statistical concepts such as distributions, mean, median, mode etc.

Model construction and statistics

After the data has been cleaned, the next step is to build a model with that data. A hypothesis test might be needed for model construction which calls for good statistical concepts.

Statistics in evaluation

Model evaluation requires tasks such as validation techniques to be performed so that the accuracy and model performance increases. These validation techniques are easily understood by the statisticians but a bit difficult for the machine learners to interpret as it involves mathematical concepts.

Presenting the model

After the successful construction and evaluation of the model, the model is presented to the general public. The interpretation of results requires a good understanding of concepts such as confidence interval, quantification, an average of the predicted results based on outputs produced and so on.

Other than the above-mentioned steps some additional concepts must be adhered to while working with machine learning. Some of these concepts are listed below:

  • Gaussian distribution – It is often represented by a bell-shaped curve. The bell-shaped curve plays a very important role while normalising the data as a normalised data is supposed to lie at the point where the bell-shaped curve is divided into two equal parts.
  • Correlation– It can be either positive, negative or neutral. A positive correlation indicates that the values change in the same manner(positive causes positive and negative leads to negative). A negative correlation indicates values change oppositely while neural suggests no relationship. This concept is of great importance to the analysts while identifying the tendencies in the data.
  • Hypothesis- An assumption might be done for the elementary predictive analysis in machine learning that requires a good understanding of the hypothesis.
  • Probability – Probability plays an important role in predicting the possible class values in classification tasks and hence forms an important part in machine learning.

Conclusion

Statistics is of huge importance to machine learning, especially in the analysis field. It is one of the key concepts for data visualization and pattern recognition. It is widely used in regression and classification and helps in establishing a relationship between data points. Hence, statistics and machine learning go hand in hand.

How Machine Learning is a Boon For License Plate Recognition?

Posted on by Imarticus Learning
Reading Time: 2 minutes

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?

Posted on by Imarticus Learning
Reading Time: 2 minutes

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?

Posted on by Imarticus Learning
Reading Time: 2 minutes

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?

Posted on by Imarticus Learning
Reading Time: 2 minutes

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.

AI Helping The E-commerce Stores To Dramatically Increase Conversions

Posted on by Imarticus Learning
Reading Time: 3 minutes

 

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.

A Look at the 3 Most Common Machine Learning Obstacles

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Reading Time: 4 minutes

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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Reading Time: 4 minutes

 

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.

5 ways AI is Utilized in Advancing Cancer Research

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Reading Time: 2 minutes

When it comes to the health of a person, life and death become a matter of problem. Health care centers and medical professionals all over the world are now leveraging the power of AI, to research a plethora of ailments. One such case is cancer research. Cancer is a disease which results in the uncontrollable division of cells and hence the destruction of body tissues. This problem can be solved with the help of artificial intelligence as it is nowadays providing favorable outcomes in every field. It can help in early detection of cancer and the treatment can prove to be very successful.
 Here’s a list of 5 ways Ai is being utilized in advancing cancer research: 

  1. Machines fed with adequate data and programmed with advanced algorithms can make use of past medical records during surgery of a patient. This is possible only with the help of artificial training. Researchers have found that there are approximately 5 times fewer complications in a robotic procedure of surgery in comparison to surgeons operating alone.
  1. Artificial intelligence can be used to interact with patients by directing them the most effective care, answering the questions, monitoring them and providing quick solutions to their problems. Most applications of virtual nursing include fewer visits to hospitals and 24 hours of care to the patients.
  1. Healthcare providers also make use of artificial intelligence to diagnose patients. Early diagnosis of cancer has now become a necessity, as any delay can cause a difference between life and death.

According to a recent study, artificial learning methods can help to classify the patients into high or low-risk groups. The study further added that AI has a great impact in the area of cancer imaging as artificial intelligence can analyze more than 10000 skin images with higher sensitivity.

  1. Complicated tests and analysis, such as CT scan and internal imaging have turned out to be hassle-free with the help of AI-enabled systems. It reduces the chances of any manual error and helps the doctors to diagnose the condition before it becomes critical.

According to a study AI has proved to be 99% accurate and more than 25 times faster in detecting breast cancer. Artificial intelligence can also be used to find out vertebral fractures if any.

  1. AI has the potential of developing lifesaving drugs and saving billions. Engineers have developed algorithms that can analyze the potency and effectiveness of the medicines developed for treatment. It also helps them to make better decisions related to healthcare.

Most of the people even use wearable technology based on artificial intelligence to check out their sleep patterns and heart rate. Applying artificial intelligence to detect cancer can inform healthcare providers about specific chronic conditions and manage the disease in a better way.
So there are various cases where artificial intelligence can find its application. Artificial intelligence training can help the individual to enhance their skills and knowledge in the field of artificial intelligence.
Imarticus Learning is one of the leading institutes that provide numerous courses in data science, machine learning, blockchain, etc. The institute takes pride in helping students make a career in artificial intelligence. AI has improved its application in the past few years and is expected to revolutionize the world in many ways in the coming years. Thus, having good artificial intelligence training will prove to be useful in all fields. You can have such good knowledge with the help of experts and qualified staff at the institute which can help you to shape your career in a better way.

Which Are The Powerful Applications of Machine Learning in Retail?

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Reading Time: 3 minutes

Introduction

Machine Learning is one of the top technological trends in the retail world. It is having a great impact on the retail industries, especially in e-commerce companies like Flipkart, Amazon, eBay, Alibaba, etc. These companies completely rely on online sales, where it is common to use Machine Learning or Artificial Intelligence nowadays.

Companies like Flipkart, eBay, Amazon or Alibaba are the successful companies who have integrated AI technologies across the entire sales cycle. Not limited to these big companies, there are also numerous small companies, some of which are already using this technology and inclined towards using this technology for the growth and development of their businesses.

How can Machine Learning Change Retail

We can think of three key scenarios when Machine Learning comes into play:

  • Finding the Right Product; Enabling your users or your customers to find the right product at the right time. We can move people away from the regular textual searches and help them find the products more visually.
  • Recommending the next product; The other aspect is how do we help them recommend the right product at the right time. One of the things that we increasingly deal with  is a choice. We can help customers by giving them the right product at the right time on the basis of their prior user behaviour.
  • Understanding the feedback; Once the product is released into the wild, we are interested in knowing how the product fares, people’s opinion about it, their suggestions in relation to the particular product. We can get a better feeling of sentiment, understanding what they do with the product, and get a better answer that can drive your life cycle from a product’s development perspective, marketing perspective and multiple downstream activities.

Applications of Machine Learning in Retail

As discussed above, those three are the key scenarios when Machine Learning is used in Retail. Some other applications of Machine Learning in retail are discussed below:

  • Market Basket Analysis: This is the traditional tools of data analysis in retail. The retailers have been making huge profits out of it for years. This is totally dependent on the amount of organization’s data that is collected by customer’s transactions. This analysis is done using Association Rule Mining algorithm.
  • Price Optimization: The formation of price not only depends on the cost to produce an item but also on the different types of customers and their budgets as well as other competitor’s offers. The data received from various sources define the flexibility of prices at different locations, different customers’ buying attitude, seasoning, and the competitor’s pricing. The retailers attract customers, retain the attention and realize personal pricing schemes with the help of real-time optimization.
  • Inventory Management: Inventory is nothing but stocking goods for their future use. This means retailers stock goods in order to use them in times of crisis. Their aim is to provide a proper product at the right time at the right place and in proper condition. A powerful machine learning algorithms and data analysis platforms help in finding not just the patterns and correlation but also the optimal stock and inventory strategies.
  • Customer Sentiment analysis: Sentiment analysis is performed on the basis of Natural Language Processing, and text analysis to extract positive, neutral or negative sentiments. The algorithms run through all the meaningful layers of speech. Here, the output is the sentiment ratings.
  • Fraud Detection: Fraud detection is one of the challenging activities for retailers. The reason for fraud detection is an immense financial loss. The algorithms developed for fraud detection not only recognize the fraud and flag it to be banned, but it also predict future fraudulent activities.

Conclusion

In the article, we briefly discussed the applications of Machine Learning in retail.