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Tag: machine learning courses

How Much Do AI Researchers Make?

Posted on by Imarticus Learning
Reading Time: 3 minutes

What is Artificial Intelligence?

Also known as Machine Intelligence, Artificial Intelligence deals with the automation of machines so that they can perform human-like activities. Artificial Intelligence is being used in a lot of data-oriented industries such as insurance, healthcare, retail, technology, automotive, etc.

It also finds use in the finance sectors where various credit frauds and identity thefts need to be traced. Artificial Intelligence makes use of various machine learning algorithms to perform a specific set of tasks. Artificial Intelligence training is shaping our new world. It aims at achieving a specific goal by rationalizing the processes involved and then taking actions accordingly.

Who are AI-Researchers?

AI Researchers are those who apply the fundamentals of artificial intelligence to various data sets to draw out conclusions and various business insights. The job of AI Researchers includes language processing, algorithm building, development of data sorting mechanisms, etc. Also, it includes the movement and manipulation of data from various channels so that an efficient and effective transformation of data takes place with the utmost ease and efficiency.

AI researchers usually have a great understanding of various computer programming languages, hence making them proficient in what they do. Their expertise lies in building machines which reduces the human effort to a great extent. Artificial Intelligence researchers are in great demand owning it to the fast-paced technological environment and this ever-growing need for constant change. Thus, making AI Research a lucrative career path. People with less experience are also trying their hands in this area, given the financial attractiveness of this field.

How much do AI Researchers make?

Artificial Intelligence has become one of the most important elements of this data-oriented world. Companies are moving towards automation, making the best use of artificial intelligence and its ability to analyze, compile and assess huge data within a given time frame. The application of artificial intelligence requires expertise, thus demanding a pedestal in the corporate world. And this pedestal converts to a huge salary when converted into financial terms.

Artificial Intelligence is an area with a high demand for skilled personnel and less supply of the same, making it a really expensive affair for the companies looking forward to hiring AI Researchers and experts. The pay is the sweet spot for AI Researchers as the job role comes with huge money. The average salary for an AI Researcher in Silicon Valley is somewhere around $100,000 and $150,000 as it involves a lot of brainstorming and this pay is further increasing with the increase in applications of artificial intelligence.

Also, with increasing applications of Artificial Intelligence, the complexity of the operations is increasing, thus making the job of an AI Researcher a hotspot.

AI training and commands have such huge salaries as it provides a practical approach and puts the theoretical knowledge to some good use. Also, one can teach oneself and be ready for the market but understanding AI takes sufficient time and is not a cakewalk.

It is rapidly growing and the ones who are catching up with this growth are being rewarded in the form of really high salaries which keep them motivated to stay on the path as this growth graph of Artificial Intelligence is constantly going up and will not become flat anytime soon. As per Glassdoor, the average base pay of an AI Researcher is $111,118 per year which is pretty high when compared to other sectors of the economy.

Conclusion

Artificial Intelligence has made the world more dynamic than it was ever before. It is evolving at a very fast pace thus giving rise to a huge demand for AI professionals and the salary associated with the field is making it even more attractive for the generations to come.

We at Imarticus Learning offer Analytics and Artificial Intelligence courses at our centers in Mumbai, Thane, Pune, Bangalore, Chennai, Delhi, Gurgaon, Noida, Ahmedabad, and Jaipur.

Using Machine Learning to Conduct Sales Forecasting

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Reading Time: 2 minutes

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

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.

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

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Reading Time: 3 minutes

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

Posted on by Imarticus Learning
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

Posted on by Imarticus Learning
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.

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.

Ways To Use Artificial Intelligence In Education

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Ways To Use Artificial Intelligence In Education

Do you know that AI is very present in all our lives and has pervaded almost every space? Not just the imaginary humans with chips portrayed by fiction writers and science fiction movie makers but just look around.

Google searches, automatic sensors for reversing your car, automatic lens adjustments and light settings for those perfectly timed selfies, Google maps to take you straight to your destination, MRIs to detect those illnesses you never thought you had, multiple-choice answer sheets scored automatically on online learning sites, paying bills online, that favorite app you just downloaded and everything I between. They all run on the artificial intelligence courses of the self-learning algorithms of machine learning help make machines truly aid to human thinking through deep learning and neural networks.

Though AI has actually taken over most of the human tasks, they are still a long way off from replacing human beings and the one area where they have tremendous application potential is in education. Let’s reiterate that the basic aim of artificial intelligence courses and neural thinking is not to replace humans but to help them with repetitive tasks and data sifting far beyond the limits of the best human brains. So, in the future, AI and humanoid robots will not replace teachers. But they will transform how we learn, and what to learn and go a step further by helping us learn. That includes the teachers too who are constantly learning too!

Why AI matters in education:

Let us explore how AI is going to bring its benefits to the education experience of the future. The class sizes keep increasing with compulsory education and teachers are often facing many challenges in giving attention and help to the large numbers of students. A big challenge like this has been simplified by incorporating computer programs that allow each student to follow his own pace and learning curve. Individualized learning modules can help find knowledge gaps and personalize the learning materials to fill in the gaps.

Teachers can now get truly involved in teaching and rectifying the lacunae in the learning process. Besides, the teachers can also get recommendations on how to rectify the issues, what learning materials to use for personalizing the process and much more to help herd the students towards the right levels of comprehension and skills required. This could also be used for learning processes of differently challenged students.

The newer methods of experiential learning at educational institutions use advanced techniques of AI, machine learning and deep learning in instructing and teaching like chatbots and learning bots. A differentiated AI style of learning deals with the most effective style to help the student learn. Adaptive artificial intelligence courses based learning curates the learning exercises matching them to the student’s needs and knowledge gaps. Competency-based AI tests aid the students to gauge their learning levels and progress from thereon. Using all these three types of learning AI can test how well the students can adapt their learning to applications of it and thus promote the progress of students based on individual interests.

The benefits:

Some of the benefits of artificial intelligence courses that can be harnessed are: 

1. Grading, scoring, and such repetitive tasks can easily be handled by AI.

2. Personalization of educational software can be need-based and adapted to individual learning curves.

3. Lacunae and learning gaps can be predicted and rectified with suggestions for learning materials and courses needed to improve.

4. Tutoring through subject-specific learning bots, online self-paced courses etc can support students.

5. The feedback route is almost instantaneous and can be gainfully harnessed by both educators and learners.

6. AI has changed the way we search for and interact with data. Just Google for information on anything and everything is what 95% of the people do to find information.

7. AI will make teachers more effective and ever-learning educators.

8. AI will develop human skills and make trial-testing-and-error learning the norm.

9. Data harnessing and empowerment will change the learning experience using AI to find, support and teach students.

10. AI can offer both offline and online resources which will alter where we learn, how and who teaches them and help apply to learn to basic implicational skills.

Conclusions: 

What do you think would be the results of AI in education and the learning process? Yes, the education field is going to be very different from what we now see it as. Skills in learning applications will count for more. Jobs will be linked to skills and not degrees. Certification will emerge as a measurable tool of skills. And, if you want to explore more, why not do artificial intelligence courses at the reputed Imarticus Learning institute?

What Machine Learning Has To Do With Your Personal Finances?

Posted on by Imarticus Learning
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What Machine Learning Has To Do With Your Personal Finances?

Machine learning is a subset of AI technology that develops complex algorithms based on mathematical models and data training to make predictions whenever new data is supplied to it for comparison. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence through machine learning courses without the use of any human intervention or explicit programming.

Though these two concepts that always go together have been around for ages, the past two decades have seen a phenomenal rise and exploitation of benefits of ML applications.

Let us explore some applications in real-life in the financial services area where they have made huge differences in customer service, fraud and risk management, and last but not least personal finance.

Examples in Customer Service:
Chatbots are the latest feature of financial services being deployed to aid and automate and reply when asked frequently asked questions, common customer service answers and requests, help in bill payments, provide information on services and products and more.

Since they work with NLP-natural language processing they understand the query and answer appropriately. But there are instances when the scenario does not fit the scripted questions and the conversation is beyond their comprehension.

ML is important to teach the chatbots in customer service to assimilate data from interactions where the AI can self-learn how to respond in the future based on the experience they gather. Obviously more the interactions, the better they get.

They are also capable of recognizing emotions like frustration, anger and so on where they can diffuse the tensions by transferring to a live customer service agent for further help or resolution. Often they up-sell products, introduce the newer services and help in transactions like making automated payments.

During the course of such interactions, they can also pick up customer behavior trends like the possibility of defaults due to cash-flows. Imagine how satisfied a customer would be when it is the due date for payment, the account is bereft of money and the chatbot work efficiently offers a different due date, a short-term loan or a customized payment plan.

That’s just a small example of the chatbot and its machine learning courses enriching the customer or user experience.

Examples in Personal Finance:
ML comes to the aid of financial institutions by specializing in the service of customers needing applications for budget management, offering guidance and highly targeted financial advice. Such apps are made for mobile devices and allow their clients to track their daily spending.

Using their innate ability to spot trends they can help with budgeting, saving and investment decisions and plans by watching and learning from the client’s spending and purchase patterns.

Ina real-life example a leading bank spotted the trend of people from a certain segment facing problems with their cash flow and using their credit cards for late-night transactions and withdrawals. By flagging such abnormal behaviour it was found that the segment faced unduly low-interest rates in their savings accounts. Based on such foresight the bank not only improved its savings rates but it also offered the segment increased credit limits to restrict defaults on payments.

ML intelligence worked very well since the bank retained its customers with such an offer and also saw an increase in its savings accounts deposits.

Examples in Fraud and Risk Management:
In the fields of risk and fraud management the daily number of transactions to be scanned, are very large and involve huge sums of money. In modern times online payments have emerged as an ideal spot for fraud perpetration. Paypal the market leaders, have employed machine learning courses specializing in risk management and fraud detection and using Big Data, complex neural networks, and deep learning capabilities. Any abnormal behavior is flagged and forms a sandboxed risk queue within milliseconds.

The cybersecurity challenges are confrontable by smart ML algorithms. The detection of phishing attacks is dependent on the algorithm being able to easily compare the original and fake sites for logos, visual images, and site components. T

hey can also detect unusual behavior once they are trained on recognizing normal patterns on a profile or account. A red flag is immediately raised and the user is asked to verify the transaction.ML is also used in risk scoring, assessing defaults in payments, automating credit scores and compliance issues, assessing loan applications and every transaction in between.

In conclusion:
Machine learning is not restricted to any one field. However, the applications can get very complex and extend far beyond these few examples. ML helps in better security, increasing operational efficiency and delivering better customer service or user experience.

If you would like to learn more, then do the machine learning courses at the Imarticus Learning Institute where technologies of tomorrow are taught and skilled for today.