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Adopt Machine Learning and Personalize Marketing Game Big Time

Adopt Machine Learning and Personalize Marketing Game Big Time

In the last couple of years, Netflix and Spotify have altered our digital expectations. The technology that these fast-growing streaming media companies use to generate fulfilling customized experiences is a particular kind of Artificial Intelligence, known as Machine Learning.

Highly technical though it sounds, Machine Learning is the most valuable, new-age tool that all the marketers need to employ right now. To better explain the nuanced concept, we’ll start with an approach that preceded it.

Human-based Marketing: Limited Scope

Previously, rules and segmentation used to dominate marketing domains; most of the customized experiences in the past were delivered through a set of norms, created manually by a marketer based on some predetermined criteria. Though the approach worked, but its scope was very limited.

The hitch is that the humans wrote the rules, based on what they believed true and right. But, remember, each human being is unique, and so is their perception. Also, their intent varies from time to time. In short, there exists too much data for a normal human being to assess or sort without taking the help of machines, or in this case Machine Learning.

The Rise of Machine Learning

Instead of relying on human intuitions, machine learning algorithms offer an innovative way for marketers to curate incredible experiences for individuals. No longer does the computer follow any rules and commands, rather we’ve programmed it to learn everything about a particular person, so that it can conjure up the experience that appeals to him the most.

For improved machine-learning personalization, marketers should build and feed in own ‘recipes’ to the computers that tell the kind of information to consider, when formulating someone’s digital campaign.

 Sometimes, the algorithms can be pretty simple, such as showing trending topics or they can be very complex, like decision trees or collaborative filtering. It all depends on the marketers to devise a strategy that would ensure the best customized experience for the visitors, of course with Machine Learning using Python.

Decision-making Induced by Machine Learning

When you speak with a person, you know what to say next and when to stop, based on the idea of previous encounters with him/her. Now, if it’s for the first time you’re speaking with him, you behave in a way you are expected to, based on social interactions with others.

Machine learning functions in the same way. Based on recognition and remembering past situations, this type of learning creates a fluid pattern that controls next behaviors.

It uses real data to derive at decisions, just similar to a normal human being who would come to a conclusion after a conversation.

As parting thoughts, humans shouldn’t hand over everything to the machines; machine learning can be all so rosy and perfect, but it’s us who needs to define, examine and refine the algorithms to make them work and fulfill the overall objectives of one-to-one customization and superior brand experience for the clients.

Of course, machine learning has over-the-top advantages against traditional human-based approaches, but it’s us who have developed them. And that matters!

For business analyst training courses in Noida, drop by DexLab Analytics. They are specialists in a number of in-demand skills, including big data hadoop, SAS and R programming, amongst others.

 

The blog has been sourced from – https://www.entrepreneur.com/article/311931

 

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Want to Develop an AI Chatbot? Know How:

Want to Develop an AI Chatbot? Know How:

As businesses are focusing on improving customer engagement and building personalized experiences for them, AI-powered chatbots are rapidly becoming the norm to meet user-centric tasks. Gartner proclaims that by 2020, 85% of interactions between customers and a brand will occur through chatbots. Microsoft’s CEO, Satya Nadella rightfully says, ‘’ Bots are the new apps.”

It is important for a chatbot to have a ‘’human touch’’. The key to that is its intelligent quotient.

So, you want to build a smart AI chatbot? In this blog, we shall discuss some important pointers to get you started.

  • Understand Customers:

The most important thing to keep in mind while building a chatbot is the goal of building it. So, a chatbot needs to understand what users demand from it very well. Hence, the better the designer understands the goals; the superior will be the quality of the bot. A chatbot needs to be familiar with the most commonly asked questions and also needs to provide relevant answers to those. The two common goals of building a chatbot are helping users or collecting information from them. Helper chatbots employ natural language processing (NLP) and have strong understanding capabilities. These bots can be used to carry out a variety of tasks, like buying products or booking hotel rooms. On the other hand, collector bots adhere to a pre-defined set of questions and don’t have the ability to respond when presented with new queries. However, by utilizing intelligent platforms, the performance of collector bots can be enhanced; they learn to respond to unknown queries by intelligently presenting the information they collect.

  • Designing Conversational Flow:

Creating a conversation flow chart is a crucial phase of building a smart chatbot. Here are the steps that you need to follow:

  1. Write down a standard conversation
  2. Jot down the possible ways in which a user can go off track
  3. Learn to deal with such off track queries. Here, interacting with existing online bots proves extremely useful. Ask questions in order to break their flow and note down the responses you get. Apply these to your flow. David Low, chief technology evangelist for Amazon Alexa, has stressed on the importance of creating a conversation script and testing it back-and-forth.
  4. It is advisable to present your bot as a non-human character. For example, to make it clear that your platform is a bot, greet users with a welcome message and state all the tasks your text platform can perform.
  • NLP and Machine Learning:

Natural language processing (NLP) platforms, like WIT, API and LUIS are the driving force behind intelligent chatbots. They analyze and resolve sentences into intent, agents, actions and contexts. NPL platforms help identifying links between words and determining parts of speech like nouns, verbs and adjectives. When it comes to leveraging machine learning or NPL for your bot, consider open and closed sources, generative and retrieval-based models before settling for the ideal model.

Want to Develop an AI Chatbot? Know How:

Conversations happening in social media platforms include a variety of topics and fall under open domain category. However, if you wish to regulate input and output for a bot then you must opt for a closed domain. Retrieval-based models work with predefined responses whereas; generative models have the ability to come up with new responses. A complex feature like sentiment analysis can also be incorporated in chatbots through NPL. This is useful in situations where a chatbot is unable to satisfy a customer. In such cases it transfers the problem to a human customer representative.

In future, companies will be increasing dependent on chatbots to boost their sales. Hence, professionals with expertise in this upcoming tech are likely to be highly valued. So, if you want to be part of that elite group then you must enroll for machine learning training in Delhi at Dexlab Analytics– our seasoned consultants offer the best machine learning courses in Delhi.

 

References:

https://moz.com/blog/chat-bot

https://intellipaat.com/blog/how-to-build-an-artificial-intelligence-chatbot/

https://www.marutitech.com/make-intelligent-chatbot/

 

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Top 5 AI-based Applications for Crime Prevention and Detection

Top 5 AI-based Applications for Crime Prevention and Detection

Companies and cities across the globe are attempting to employ AI in a plethora of ways to address crime. Day by day, city’s infrastructure is becoming smarter and tech-efficient. Crime detection is no more a catch-22. With easy availability of real time information, it’s now easier to detect crimes.

Here, we are going to dig into a few present AI applications in crime detection and prevention:

Gunfire Detection – ShotSpotter

ShotSpotter utilizes smart city infrastructure to pinpoint the area from where the gunshot came through. The company representatives claim that their system has the ability to alert authorities in real time with the data about what kind of gunfire it was and the exact location as accurate as 10 feet. Thanks to multiple sensors and their machine learning algorithm. They work by picking up the sound of the gunshot.

At present, they are being used in over 90 cities across the world, including Chicago, New York and San Diego.

AI Security Cameras – Hikvision

China’s top notch security camera producer, Hikvision made an announcement last year: they are going to use chips from Movidius (an Intel company) to develop cameras that would run intricate, deep neural networks right away.

They claim this new camera would better scan the license plates on cars, perform facial recognition for potential criminals and automatically identify suspicious anomalies. Currently, their advanced visual analytics systems can achieve 99% accuracy and with 21.4% of market share for CCTV and Video Surveillance Equipment worldwide, Hikvision has clearly secured a respectable position in the video surveillance space.

Predict crime locales – Predpol

Predicting future crime spots is no mean feat! But Predpol is proud to venture into that nuanced area with their powerful big data and machine learning capabilities that can predict the time and location new crimes are most likely to happen. And that can be done through data analysis of past crimes. Historical data plays an integral part in building such algorithms.

Los Angeles is one of the American cities that have adopted their system, among others.

Who commits the crime – Cloud Walk

Cloud Walk, the Chinese facial recognition enterprise is foraying into a new scope of technology where it would be possible to predict if a person decides to commit a crime, even before he attempts to. As a result, they have built a system to detect suspicions changes in the manner or behavior of an individual. For example, if a person buys a hammer, that’s completely fine. But of course, if he buys a knife and a rope, he comes under the radar of suspicion.

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Find suspects most likely to commit another crime – Hart

If you know, the individuals charged of a crime are soon released until they stand trials. Now, deciding who should be released pre-trial is like being in deep water. For that, Durham, UK has employed AI technology to enhance their current system of deciding which suspect to release. The program is called Harm Assessment Risk Tool (Hart), and is fed with 5 years’ worth of criminal data for smoother prediction of a person’s vulnerability towards crime.

A whole body of data is used to predict whether an individual falls under the purview of low, medium or high risk. Comparing the prediction with the real world results, we found out that most of the predictions of HART were close to being accurate.

The robust growth of AI and machine learning is the best thing since sliced bread. Their superior technology for crime detection is already in place, and is growing to expand further in the future.

Keeping that in mind, we at DexLab Analytics offer a bunch of Machine Learning Using Python courses to shape your future for good. Our Machine Learning Courses are of top quality and fits the budget of all.

The article has been sourced from – https://www.techemergence.com/ai-crime-prevention-5-current-applications/

 

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Top 4 Applications of Cognitive Robotic Process Automation

Top 4 Applications of Cognitive Robotic Process Automation

With the dawn of automation, industries all over the world are depending on robots to carry out tasks, such as product designing and manufacturing. It optimizes repetitive processes and improves cost efficiency. Incorporation of cognitive capabilities, like natural language processing and speech recognition into robotic process automation has resulted in the birth of Cognitive Robotic Process Automation (CRPA). Let’s delve into the current applications of this revolutionary technology.

Finance and banking sector:

Customers are demanding expedient methods to transfer money and make investments.  Also, the volumes of customer data are increasing rapidly. Hence, banks need to improve the speed of information processing. To achieve this, they have turned to process automation. Many banks are adopting AI-powered technology to automate regular processes.

According to a survey conducted by BIS Research, Banking and Finance sector is likely to become the largest revenue generator in the world for CRPA industry. For example, Bank SEB in Sweden bought cognitive robotic process automation software from IPsoft, a foremost company of CRPA industry. This technology is actually a software robot named Amelia that has knowledge of 20 different languages and is aware of semantics, including English and Swedish. In case Amelia fails to solve the problem at hand, it transfers the same to a human operator, and studies the interaction to hone its skills and apply it to similar cases in future.

U.K.’s KPMG has collaborated with Automation Anywhere to provide digital staff for clients.

Insurance:

Task like manual inputs, data gathering and retrieval, legacy applications and system updating is very time consuming. Hence, the insurance industry is welcoming automation in its processes. This help with the following tasks:

  • Automates fraud detection, policy renewal and premium calculation
  • Improves customer service
  • Enhances employee engagement
  • Upgrades business productivity as software robots can work for hours at a stretch
  • Frees employees for important tasks that need manual handling

Developed economies, including U.S. and the European nations are extensively employing RPA/CRPA bots. AXA Group, one of the chief French insurance companies using smart automation services to improve its bankroll, reported that France has the fifth highest insurance premiums in the world.

Leading IT service provider of Australia, DXC Technology, has partnered with Blue Prism, one of the best companies providing RPA solutions, to improve the RPA capabilities for key insurance clients, like Australia and New Zealand Banking Group (ANZ). Fukoku Mutual Life Insurance, top insurance firm of Japan, has replaced 30 human workers with IBM’s latest AI tech, Watson Explorer. The tech’s deployment has boosted company savings and enhanced productivity by 30%.

2

Telecom and IT Industry:

Business process outsourcing (BPO) services are facing problems like increased operational costs and low profit margins. RPA/CRPA software bots can be one of the ways to tackle this problem. Hexaware Technologies, a topnotch company in this field, has partnered with Workfusion to evolve IT infrastructure, combat the aforementioned problems and boost overall productivity.

Healthcare:

Some of the challenges of the healthcare industry are:

  • Maintaining paper records of patients’ medical documents.
  • Transferring these records to digital databases
  • Manually updating databases
  • Maintain an inventory database for medicinal supplies
  • Systematic management of unstructured data
  • Innovation in healthcare encounter regulatory and reporting challenges when launching new drugs.

These tasks are repetitive and increase chances of errors when done manually. Automation helps tackle these problems and also provide safe and good quality drugs to the market. Blue Prism is one of the principal providers of RPA for healthcare.

Future Scope:

Competition in the global capital markets is increasing. New contestants are bringing in ‘’disruptive technologies’’ that are pressurizing existing institutes to increase their efficiency and cut down costs. Hence, the need to embrace cognitive automated technology.

Australia and Japan are among the top countries adopting process automation. Leading countries embracing RPA for financial services include India, China and Singapore. It is expected that Fintechs will mainly disrupt three areas of financial sector-consumer banking, investment handling, fund and payment transfer.

It is about time that all businesses and organizations integrate machine learning and artificial intelligence in their processes for competitive advantage.

How can you take advantage of this tech-driven era? Enroll for machine learning training in Delhi at DexLab Analytics. Many top companies look for expertise in this budding technology while recruiting employees. DexLab’s Machine learning course in Delhi offers superior guidance that will help you develop crucial knowledge needed to stay ahead of competition.

 

Reference link: https://www.techemergence.com/cognitive-robotic-process-automation-current-applications-and-future-possibilities

 

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How Some Astonishing Techs of 2018 are Influencing Development in Africa

How Some Astonishing Techs of 2018 are Influencing Development in Africa

The technological evolution is conquering things which we previously considered to be strictly human. Addressing the scope of current tech space, we can say that the possibilities are endless, quite literally. AI is accomplishing tasks we never thought were feasible, such as composing music and creating videos. With the help of analytics, doctors can predict the effectiveness of cancer treatments in days; whereas earlier precious months were wasted to determine the same. In Africa, AI-powered technology will soon be employed to tackle burning social problems.

So, let’s take a look at some current tech trends that are inspiring development in Africa.

New data sources:

The availability of new sources of data plays a pivotal role in current tech advancements. For example, consider the financial inclusion of a nation’s population. Traditionally, financial institutes analyze financial history of an individual to determine if the person is eligible for a loan or not; which includes credit scores, IDs and other relevant documents. However, this excluded a large portion of the population who never had the chance to build a credit history due to the absence of any form of documentation. FinTech companies in East Africa have adopted a different approach-they are analyzing available mobile data about a person, like the frequency of getting a recharge, and making lending decisions on the base of this data. Thus, more people are now being able to access financial services and accomplish their goals, like educating children or starting businesses.

New features in SAS platform have the ability to analyze images. This is benefiting rangers working for wildlife conservation as previously they would have to manually sort the pictures of animals into species and sexes. Now, SAS’s new AI-driven technology can do the classification and rangers can focus on more important tasks.

Improved predictions:

Africa is seeing the emergence of new machine learning algorithms, like the extreme gradient boosting model, which are allowing data scientists to make more precise predictions. In Nigeria, this is boosting the development of models that prevent customer churn in the telecom industry. These models assess customer information, like billing data, purchase history, demographics and service usage, and create loyalty profiles that enable better marketing campaigns.

Bring into play the unstructured data pool:

Generally, companies crunch data from structured data sources, like transactional data. However, tapping into unstructured data sources, like customer complaints, reviews and text information, can be highly advantageous for businesses. These data sources help predicting customer churn more accurately.

Plunging into Deep Learning:

Deep learning falls under the category of machine learning, which is creating waves of excitement all over the world. It has the ability to model complex concepts in data through the use of high-level structures, algorithms and multiple processing steps. Deep learning teaches computers to recognize patterns through the numerous processing steps and perform tasks that are conventionally carried out by humans, such as image identification and speech recognition.

These models are improving traditional techniques used in credit risk modeling and fraud detection. SAS has collaborated with Equifax to implement deep learning models for improved risk management.

Nigeria has turned its focus on upskilling its people in data science, so that they can take advantage of this AI-era and become an outsourcing hub for deep learning projects.

Emotionally intelligent bots:

An exciting application of AI and language processing is chatbots. They are programmed to enable conversations between machines and humans. This helps save a lot of time and money that was previously wasted on performing repetitive and mundane tasks, such as responding to customer queries related to their bank accounts.

Recently, United Bank of Africa launched its chatbot, named Leo, which is in fact a Facebook bot that allows bankers to carry out real-time transactions and other banking activities, like opening accounts and paying bills.

Thus, we are entering an era where machines can think and learn utilizing the power of AI. AlphaGo, a programme created by Google in 2016, has been able to defeat the best human players of this ancient Chinese game. And AlphaGo Zero, the next version, learned by playing against itself and after a period of time defeated AlphaGO.

To read more blogs on current technologies, follow Dexlab Analytics– we provide the best Machine Learning training in Delhi. Take a look at our machine learning courses in Noida.

 

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Transforming Society with Blockchain and Its Potential Applications Worldwide

Transforming Society with Blockchain and Its Potential Applications Worldwide

According to Google Search, ‘blockchain’ is defined as “a digital ledger in which transactions made in bitcoin or in other cryptocurrency is recorded chronologically and publicly.”

Speaking in a way of cryptocurrency, a block is a record of new transactions that could mean the actual location of cryptocurrency. Once each block has completed its transaction, it’s added to the chain, creating a chain of blocks known as blockchain.

Suppose a Google spreadsheet is shared by each and every computer which is connected to the internet in this world. When a transaction happens, it will be recorded in a row of this spreadsheet. Just like a spreadsheet has rows, Blockchain consists of Blocks for each transaction.

Whoever has access to a computer or mobile can connect to the internet and can have access to the spreadsheet and add a transaction, but the spreadsheet doesn’t permit anyone to edit the information which is already available. No third party can interfere into its transactions, therefore saves time and conflict.

1

Types of Blockchains:

  • Open and permission-less: Public and permissionless blockchains look like bitcoin, the first blockchain. All exchanges in these blockchains are open and no authorizations are required to join these circulated elements.
  • Private and permission: These blockchains are constrained to assigned individuals, exchanges are private, and authorization from a proprietor or supervisor substance is required to join this system. These are frequently utilized by private consortia to oversee industry esteem chain openings.
  • Hybrid blockchains: An extra region is a developing idea of sidechain, which takes into consideration distinctive blockchains (open or private) to speak with each other, empowering exchanges between members crosswise over blockchain systems.

Various Applications Of Blockchain Are As Follows:

a) Smart Contracts:

Smart Contracts eases the way we exchange money, property, shares and avoids third person/party conflicts. Smart keys access can only be permitted to the authorized party. Basically, computers are given the command to control the contracts and to release or hold the funds by giving the keys to the permitted persons.

For example, if I want to rent an office space from you, we can do this in blockchain using cryptocurrency.  You will get a receipt which is saved in the virtual contract and I will get the digital entry key which will reach me by a specified date. If you send the key before the specified date, the function holds it and releases both receipt and the key when the date arrives.

If I receive the key I surely should pay you. And this contract will be canceled when the time gets complete, and it cannot interfere as all the participants will be alerted. The Smart contracts can be used for insurance premiums, financial derivatives, financial services, legal processes etc.

b) Digital Identity:

The future of blockchain will be blooming in the coming years. Blockchain technologies make both managing and tracking digital identities reliable and systematic, resulting in easy registering and minimizing fraud.

Be it national security, citizenship documentation, banking, online retailing or healthcare, identity authentication and authorization is a process entangled in between commerce and culture, worldwide.  Introducing blockchain into identity-based mechanisms can really bring captivating solutions to the security problems we have online.

Blockchain technology is known to offer a solution to many digital identity issues, where identity can be uniquely validated in an undeniable, unchangeable, and secured manner.

Present-day methods involve problematic password-based systems of known secrets which are exchanged and stored on insecure computer systems. Blockchain-based certified systems are actually built on undeniable identity verification for using digital signatures based on the public key related cryptography.

In blockchain identity confirmation, the only check that is performed is to know if the transaction was signed by the authorized private key. It is implied to whoever has access to the private key is the owner and the exact identity of the owner is deemed unrelated.

c) Insurance:

Claims dealing can be disappointing and unrewarding. Insurance agents need to go through deceitful cases and deserted approaches, or divided information sources for clients to express a few – and process these documents manually. Space for mistake is enormous. The blockchain gives an ultimate framework for hazard-free administration and clarity. Its encryption properties enable insurers to represent the ownership to be protected.

“This will be the toughest on the portions of the industry that are least differentiated, where consumers often decide based on price: auto, life, and homeowner’s insurance.” — Harvard Business Review

d) Supply-Chain Communications and Proof-of-Provenance:

The majority of the things we purchase aren’t made by a single organization, yet by a chain of providers who offer their ingredients (e.g., graphite for pencils) to an organization that gathers and markets the final commodity. On the off chance that any of those commodities flops, in any case, the brand takes the brunt of the backfire — it holds most of the duty regarding its supply chain network.

However, consider the possibility that an organization could proactively give carefully perpetual, auditable records that show stakeholders the condition of the item at each esteem included process.

This is not a little task: The worldwide supply chain network is evaluated to be worth $40 trillion; and from a business-process point of view, it’s a fabulously incapable chaos. As a related issue, blockchain can be utilized to track diamonds, creative skill, real estate, and practically any other resources.

e) Music Industry:

While music lovers have hailed digitization as the popular government of the music business, 15.7 billion dollar music industry is confusingly continuing as before. Music piracy through unlawfully downloaded, duplicated and shared content eats into the artist’s sovereignties and music labels’ income. Added to this, is the absence of a vigorous rights administration framework, which prompts loss of income to the artist.

Also, the income, when it really achieves the artist, can take up to two years! Another region of concern is unpaid sovereignties, which are frequently suspended in different stages because of missing data or rights possession. There is additionally an absence of access to continuous advanced sales information, which if accessible can be utilized to strategize advertising efforts more successfully.

These very zones are the place Blockchain can have stunning effects. As a publically accessible and decentralized database that is distributed over the web, Blockchain keeps up lasting and undeletable records in cryptographic format. Exchanges happen over a peer to peer system and are figured, confirmed and recorded utilizing a computerized agreement strategy, disposing of the requirement for an intermediator or outsider to oversee or control data.

The very engineering of Blockchain being unchanging, dispersed and distributed conveys enormous potential to manage the present troubles influencing the music business.

An essential region in which Blockchain can bring out positive change is in the formation of a digital rights database. Digital rights articulation is one of the basic issues distressing the present music industry. Recognizing copyright of a melody and characterizing how sovereignties ought to be part of musicians, entertainers, distributors, and makers are troublesome in digital space. Regularly artists miss out on sovereignties because of complicated copyright condition.

Blockchain’s changeless distributed ledger framework, which guarantees that no single organization can assert proprietorship, ensures an ideal arrangement. Secure documents with all applicable data, for example, structure, versus, straight notes, cover craftsmanship, permitting, and so on, can be encoded onto the Blockchain making a changeless and inerasable record.

f) Government and Public records:

The administration of public services is yet another region, where blockchain can help diminish paper-based procedures, limit fraud, and increment responsibility amongst specialists and those they serve.

Some US states are volunteering to understand the advantages of blockchain: the Delaware Blockchain Initiative propelled in 2016, expects to make a proper legitimate foundation for distributed ledger shares to increase productivity and speed of consolidation administrations.

Illinois, Vermont, and different states have since reported comparative activities. Startup companies are sponsoring in the effort also: in Eastern Europe, the BitFury Group is presently working with the Georgian government to secure and track government records.

Conclusion:

This article focused on the blockchain and its applications in various industries explains challenges and potentials and how people can secure their information digitally without any issues and increasing their ability. As these applications are still under development and yet to be untangled in the future, blockchain could become a powerful tool conducting fair trade, improving business and supporting the society.

To never miss a beat of technology related news and feeds – follow DexLab Analytics. We are a team of experts offering state of the art business analyst training courses in Gurgaon. Not only that, we provide a plethora of machine learning and Hadoop courses too for all the data-hungry candidates. So, drop by and quench your thirst for data from us!

About the Author:

K.Maneesha is an SEO Developer At Mindmajix.com. She holds a masters degree in Marketing from Alliance University, Bangalore. Maneesha is a dog-lover and enjoys traveling with friends on trips. You can reach her at manisha.m4353@gmail.com. Her LinkedIn profile Maneesha Kakulapati.

 

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

In recent years, recommendation systems have become very popular. Internet giants, like Google, Facebook and Amazon, use algorithms to tailor search results to customer preferences. Any system that has a search bar collects data on a customer’s past behavior and likings, which enable these platforms to provide relevant search results.

All businesses need to analyze data to give personalized recommendations. Hence, developers and data scientists are investing all their energies and mental faculties to come up with perfect recommendation systems. Many of them are of the opinion that Python Machine Learning is the best way to achieve this. Often, building a good recommendation system is considered as a ‘rite of passage’ for becoming a good data scientist!

Delving into recommendation systems:

The first step in the process of building a recommendation system is choosing its type. They are classified into the following types:

  • Recommendation based on popularity:

This is a simplistic approach, which involves recommending items that are liked by the maximum number of users. The drawback of this approach is its complete exclusion of any personalization techniques. This approach is extensively used in online news portals. But in general, it isn’t a popular choice for websites because it bases popularity on entire user pool, and this popular item is shown to everyone, irrespective of personal choice and interest.

  • Recommendation based on algorithms:

This process uses special algorithms that are tailor-made to suit every customer. They are of two types:

  • Content based algorithms:

These algorithms are based on the idea that if a person likes a product then he/she will also like a similar product.  It works efficiently when it is possible to determine the properties of each product. It is used in movie and music recommendations.

  • Collaborative filtering algorithms:

These algorithms are dependent on past behavior and not on properties of an item. For example, if a person X likes items a, b, c and another person Y likes items b, c, d, then it is concluded that they have similar interests and X should like item d and Y should like item a. Because they are not dependent on additional information, collaborative filtering algorithms are very popular. E-commerce giants, like Amazon and Flipkart, recommend products based on these algorithms.

After choosing the type of recommendation system to build, developers need to locate relevant datasets to apply to it. The next step is determining the platform where you’ll build your recommendation system. Python machine learning is the preferred platform.

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Advantages of using Python Machine Learning:

  • Code: Python makes the process of writing code extremely easy and working with algorithms becomes quite convenient. The flexible nature of this language and its efficiency in merging different types of data sets make it a popular choice for application in new operating systems.
  • Libraries: Python encompasses a wide range of libraries in multiple subjects, such as machine learning and scientific computing. The availability of a large number of functions and methods enables users to carry out several actions without having to write their own codes.
  • Community: Python includes a large community of young, bright, ambitious and helpful programmers. They are more than willing to provide their valuable inputs on different projects.
  • Open source: The best part about Python is that it is completely open source and has sufficient material available online that will help a person develop skills and learn essential tips and tricks.

Proficiency in Python is highly advantageous for anyone who wants to build a career in the field of data science. Not only does it come handy in building complicated recommendation systems, it can also be applied to many other projects. Owing to its simplicity, Python Machine Learning is a good first step for anyone who is interested in gaining knowledge of AI.

In the current data-driven world, knowing Python is a very valuable skill. If one’s aim is to collect and manipulate data in a simple and efficient manner, without having to deal with complicated codes, then Python is the standard.

For Machine Learning training in Gurgaon, join DexLab Analytics– it is the best institute to learn Machine Learning Using Python.

 

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How is AI Shaping the Indian Job Market?

How is AI Shaping the Indian Job Market?

Currently, startups focusing on Artificial Intelligence, Machine Learning and Deep Learning are on the rise in India. According to a recent report by AI Task Force, there are 750 startups in India that are actively working to build a robust AI ecosystem in India. Initiatives to promote AI by Indian government include establishment of NITI Aayog, the policy think tank of India, and Digital India, which is a campaign to improve technological infrastructure of the country.

65% of participants of a PwC survey believed that AI will have a grave impact on the employment scenario of India. Interestingly, the majority of participants of this survey were of the opinion that AI will allow employees to do more value-added tasks as it will take up all the daily mundane tasks.

Deep Learning and AI using Python

Job market outlook:

‘’We expect a 60 per cent increase in demand for AI and machine learning specialists in 2018’’, said BN Thammaiah, Managing Director, Kelly Services India. Belong, a Bengaluru-based outbound hiring firm startup, shares the same view, stating that the demand for AI professionals has risen by leaps and bounds due to the widespread adoption of AI and automation technologies across companies. Consulting industry leader, Accenture, expects AI to add $957 to India’s GDP by 2035.

Jump in demand:

Only 4 percent of AI professionals have work experience in core domains, like deep learning and neural networks.

For every 1000 jobs in the field of Deep learning, there are approximately 530 professionals available. Similarly, for every 1000 jobs in the field of Neuro-linguistic Programming (NLP), there are only 710 professionals available.

The lack of core data science disciplines in engineering institutes across the country is responsible for the disparity between demand and supply of AI professionals. Only a few selected institutes, like IITs and IISc, have ML programs in their curriculum. The active AI researchers in India are a meager 386 in number.

AI hotspots in India:

AI-work hubs in India are Bengaluru, New Delhi and Mumbai. IBM, Microsoft, Flipkart and Amazon are carrying out good research work in AI. Companies like Adobe, Accenture, Amazon, JP Morgan, SAP, L&T Infotech, Nvidia, Intel and Wipro are actively hiring AI professionals. The main sectors fostering AI employment are e-commerce, banking and finance. Kamal Karanath, Co-founder of Xpheno, a recruitment company, said that there would be a huge demand for AI engineers in these sectors in the next 5 years. AI-powered technology boosts efficiency and security of Indian banking and financial sector.

India Inc is endeavoring to upskill workers in subjects like machine learning, cloud computing and big data. In efforts to nurture talent and obtain solutions from vertical focused AI startups, which are developing innovative technologies, enterprises have set up many accelerator programs. Flipkart is developing AI products that will boost their business growth.

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A peek into the future of AI:

The Indian government intends to establish research institutes and Centres of Excellence that foster training and skilling in fields like AI, robotics, big data analysis and internet of things. Top engineering schools, like IITs, IIITs and IISc are collaborating with industries to bridge the gap in AI talent, provide targeted solutions and steer growth of the AI industry. Government of India is framing numerous policies to promote industry-academic partnerships.

Get an edge in this AI-era by enrolling yourself for the Machine Learning training course at DexLab Analytics– a leading data analyst training institute in Delhi.

 

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Here’s Why Automation Is Gaining Accolades All over the World?

Here’s Why Automation Is Gaining Accolades All over the World?

Over the last few decades and more, if there’s anything that has continuously evolved, surprising us with something new at each turn, it’s TECHNOLOGY. Technological advancements have come to a point where people and businesses have started relying on Automation.

Automation is the technology by which a process is performed entirely by means of various control systems and equipments without human assistance. The purpose is to enhance productivity, deliver faster results and save costs, depending on the industry and the extent to which automation is being applied.

Automation, which is mostly about streamlining the work process, can take place either by means of implementing intelligence into the existing systems or by replacing the same. Some of these systems operate without human interference while others support humans and pave ways for better productivity.

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Leveraging Automation for Good

How to introduce automation into the workflow and the procedure of doing that has been a matter of concern for most entrepreneurs. Putting machines to work requires certain considerations. Following are a few smart tips for introducing automation to your business and leveraging the most out of it.

  • Putting the machines to work for you necessitates you to implement the automation tools gradually. This should always be followed by a close evaluation of the existing process so that it is applied to where streamlining is needed the most.
  • Automation is an instant success when you start things manually, but sometimes things can take a downturn if you lack enough knowledge of the domain. The switch to automation should be planned ahead with care.
  • Automation is a change that most people will be reluctant to adopt at least initially. So, it is always better to ensure that there is a solid change management strategy in place to make the technology actually make lives easier and not otherwise.

Automation isn’t something NEW ON THE BLOCK

The very notion of automation has been in the minds of entrepreneurs as well as traditionalists for around centuries. For an example, an elevator that’s been in existence for so many years is an automation tool for mobility. Recently, massive breakthroughs have been achieved in this budding field, and cloud computing is one of them. It has driven the cost of storage solutions really down, while pushing machine learning skills to a new high.

In short, while automation has always been more of a IT tool, it’s now finding its footing in other domains within an organization. “Whether it’s a catalog or an app, it now allows me as a consumer to control things when they’re happening,” says Marc Wilkinson, CTO for Workplace and Mobility at DXC.

Automation and employment crunch

As it’s said, automation isn’t about eliminating jobs instead it’s more about creating efficiencies. If the work done in twice the speed, it would be more efficient and employer will benefit from productivity gains and the employees in the event will hit off high-impact projects.

So, in a sense, automation won’t entirely result in lower employee headcount, though it may affect a certain kind of employment. High-touch occupations will witness limited impact. For an instance, hospitals have to very much human-to-human; they can’t go totally automized.

In case, you are thinking of having a career in automation, get enrol in a good Machine Learning Certification course. DexLab Analytics offers some of the best machine learning training in Delhi, NCR region: go check out the course itinerary.

 

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