Offshore Ruby on Rails Development and Bacancy Technology – A Match Made in Heaven
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The document discusses a conversation between two friends, Mike and Danny, about machine learning (ML) and its real-world applications. It outlines various sectors where ML is utilized, including healthcare, banking, finance, marketing, e-commerce, cybersecurity, and transportation, highlighting specific use cases such as fraud detection, personalized marketing, and smart email categorization. The dialogue emphasizes the practical benefits and significance of machine learning, countering the notion that it is merely a societal hype.
Offshore Ruby on Rails Development and Bacancy Technology – A Match Made in Heaven
- 1. HTTPS://WWW.BACANCYTECHNOLOGY.COM/ MACHINE LEARNING FOR EVERYONE: FROM HYPE TO HUMANIZATION
- 2. Danny and Mike are two friends, attending the course on Machine Learning organized by their school. Just after the first class, they both indulge in a long conversation. Mike: Hey Dan, what a fantastic session it was! I didn’t know that machines have such great potential with learning. Danny: I didn’t get you, Mike? Learning is an explicit quality, and humans have indeed taught the machines this art of learning on their own, which we commonly known as machine learning. Mike: So, you mean that we humans do not need to imply our knowledge of our day-to-day tasks, because machines can do it for us, right? I thought that the actual machine learning is just a hype created in society! Danny: Ohh, it seems that you still don’t know where and how machine learning has established its footprints in varied applications industry-wide.
- 3. Mike: Uhm, yeah, like I used to think that scientists feed programs into machines and then machines can work for humans. That’s it. Danny: Mikey, my friend, I think you haven’t yet come across real-time use-cases of machine learning. I must tell you so much. Mike: Certainly, please do educate me about the scope and reach of machine learning. Danny: Mike, let me brief you about what is machine learning? First of all, Machine learning is a subfield of Artificial Intelligence, where ML developers feed data in computer systems and program them to use that data to learn on their own without human intervention. To explain it better, I am going to educate you about the varied applications of machine learning across fields such as- healthcare, banking & finance, data analytics, marketing & sales, eCommerce, cybersvecurity, commuting, and email communications.
- 5. Machine learning can monitor mother and fetus health, and enable diagnosis of any complications. This life-saving power of ML lowers the risks of miscarriages and pregnancy-related diseases. Machine learning incorporates several tools and techniques used for prognostic and diagnostic issues of everyday lives in the healthcare domain. Some of the common use-cases of machine learning in the healthcare sector are, ML for Detection and Diagnosis of Disease- Machine vision is at an early adoption for detecting symptoms of the patients so that doctors can address patients’ health beforehand. Also, doctors are attempting medicine recommendations, overall diagnosis, and readmissions for social advantage. Treatment Recommendation- Depending on the patient’s previous health record, machine learning can aptly recommend which treatment is going to suit-well to the patient. Discovery of Drugs- Healthcare and drug research are a crucial industry impacting the overall development of a nation. Machine learning also helps in providing approaches that improve decision-making while drug-selection with abundant specified- questions over a vast data pool. Machine Learning Approach for Prediction of Pregnancy- Machine Learning in Healthcare
- 6. Most of the banking and finance matters deal with vast amounts of data, and machine learning works well with large volumes of data to detect anomalies and nuances. Some of the implications of machine learning in the banking and finance industry are, Fraud Detection- Programmers train machines to catch abnormal finance behaviors, and this way, finance companies, and banks are saving on their operational costs by limiting exposure to fraudulent activities. Credit Scoring- Banks use AI intended credit lending applications to find out low-performing loans and credit scores of their customers. Insurance Underwriting- AI analyzes a variety of factors in the credit decision-making process like the behavior of traditionally underserved borrowers and avails acute underwriting. Money laundering prevention- machines detect suspicious activities, and hence finding fraud is easy to prevent money laundering. Robo Advisory- Customers use AI chatbot applications and mobile assistance to reach their financial targets. These AI/ML apps assist them with their spending rates, managing savings, and more. Machine Learning in Banking & Finance
- 7. Machine Learning to Boost your Predictive Analytics Machines Learning works wonders for handling large data sets by giving predictive results in a short time. It reduces the work of computer-coders as machines learn on their own by getting real-time data. Some use-cases of machine learning in data analytics, Analytics platform- Employers provide quick analysis tools to their employees so that they can work efficiently with unified data and tools. Analytics services- The E2E solution providers satisfy companies’ custom analytics needs and turnkey solutions. Real-time analytics- ML is used to explore data, though unstructured so that users can get time-sensitive decisions without any disruptions. Image recognition and visual analytics- AI algorithms analyze data piles of images and videos and derive meaningful insights.
- 8. Machine Learning For Marketing & Sales Marketing & Sales is all about getting close to the end-customer, and this can pertain when companies very well know about their customers’ preferences. Machine learning is the ideal choice when assisting companies in their sales and marketing goals. Let us see a few ML applications, Marketing Analytics- Artificial Intelligence systems analyze, learn, and measure the marketing efforts of the PR team. These systems give out expert insights that drive engagement, traffic, and revenue. Personalized Marketing- Internet users get ads of visited online products even on other webpages, which is one of the techniques used by eCommerce companies for customer-specific marketing.
- 9. Context aware marketing- Marketing executives use Machine Vision and Natural Language Processing for creating context-aware ads for specific users based on their interests. Sales forecasting for pre-sales- Based on previous sales outcomes and customer contacts, brands use AI automated forecasts for attaining sales accuracy. Sales content personalization- AI analyses the needs of high-priority leads by watching their browsing patterns and matches the right content to meet their requirements.
- 10. Machine Learning in E-Commerce The most vital and trending industry today is the eCommerce industry. Machine Learning is empowering many online businesses in many ways like, Recommender systems- One of the most potent marketing styles currently is on-site recommendations. ML reveals customized user recommendations that benefit eCommerce companies, and they have noticed a 30% hike in sales because of such ML advice. Content Personalization- The AI bots study user patterns, interests, and browsing methods and decide the type of content to display for each user. Such personal-touch boosts sales amongst the customers. Chatbots- AI has taken personalization to a very high-level. Customers feel personal preference and respond much better with the businesses, which ultimately benefits the online companies. Dynamic Pricing- Studying customers’ browsing history, shopping patterns, times, and more, ML automates customized sales and discounts for users. Such an active style of pricing works out favorably for e-businesses.
- 11. Machine Learning for Cybersecurity Artificial Intelligence is used to provide security and authentication in sophisticated systems. Some of them are, Password protection & Authentication- Machine learning adapts user profiles by checking their keystroke styles, typing speeds, and else to provide multi-factor authentication. It suggests stronger passwords whenever necessary. Cyber threat detection- AI detects anomalies early and help the brands discover vulnerabilities beforehand. AI finds out even the minutest changes in human interaction patterns to identify red-flags in the process. Phishing Detection and prevention control- AI combats the phishing attacks very well by analyzing internal communication systems and detecting any unusual event. Vulnerability management- Machine learning can track hackers’ activities and analyze their attack patterns. Then, companies can use these ML reports to enlighten their employees about such attacks so that they stay protected.
- 12. Predicting Travel Mode of Individuals by ML AI and ML have been the most useful in reducing the tiring commute time of workers. Some of the ways how AI is helping to tackle the complexity of transportation, Predictions- Google maps help in sharing users’ locations, which, in turn, helps to detect the traffic and speed of its movement. This way, ML can suggest the commuters to find the fastest routes to get to their destinations. Ridesharing apps- ML helps the ride-sharing apps to find out the price of user rides, waiting times, and detour rides availability. Apps like Lfyt and Uber are a boon today. Auto-pilot- the earliest use of AI is in airplanes. The cockpit requires minimal human pilot effort. Rest, since 1914, planes have been using AI auto- pilot mode for flying.
- 13. Machine Learning to Improve the Email Experience Email is an essential part of one’s life, and you cannot guess how magically AI and ML are making your work routine easy. Let’s see, Spam filters- AI uses simple filters to keep unwanted emails, marketing emails, and promotional ones from unknown ids away from your inbox, i.e., in your spam mails. Smart Email Categorization- Gmail uses an efficient AI technique to keep your emails categorized into primary, promotional, and social mails in groups. Gmail is continually learning as every time you mark an email important, it remembers.
- 14. Danny (continuing): So, Mike, I hope these real- time examples of Machine learning have brought insights to you. Mike: I am excited, Danny! These examples of machine learning and implementations of Artificial Intelligence have sparked me up. Now I know that all the hype I heard about ML is nothing but a proven reality. Thank you so much, my friend, Thank you!
- 15. Wrapping Up Many of you might have cleared the perceptions of new and upcoming ML and AI after reading this post. Now that you know the true potential of machine learning; get the best AI and ML development services from Bacancy to yield high-performing AI/ML apps.
- 16. Thank You