Contact for Admission

Arya College Cousellor Arun Arya

Prof. (Dr.) Arun Arya

Contact for Admission

Arun College Cousellor Arya

Prof. (Dr.) Arun Arya

Common Applications of Machine Learning for Engineers

Applications of Machine Learning

Machine Learning is a sub-branch of Artificial Intelligence that has established itself as the new go-to technology for businesses worldwide. Whether it is e-commerce or healthcare, almost all the industries are using Machine Learning extensively to make futuristic solutions and products for the students of BTech Colleges in India. Applications of Machine Learning mainly depends on programs and algorithms that help machines self-learn without having to be instructed explicitly. Machine Learning is pretty much dictating our daily lives. Some of the applications of Machine Learning to understand how it is shaping the digital economy includes the following:

Dynamic Pricing

Pricing strategy is one of the oldest puzzles of the modern economy. Whether it is the entertainment industry or the consumables industry, efficient product pricing is important for-profit margins and affordability. Depending on the objective, there are different pricing strategies that businesses can choose for sales and marketing. However, choosing the right pricing strategy is easier for the students of Engineering Colleges in India. Several decisive factors like cost of production, demand curve, market control, consumer demographics, value and more need to be adequately aligned for any product to be priced properly. Due to this, Artificial Intelligence has effectively resolved this issue in recent times. AI-powered pricing solutions have helped businesses to understand consumer purchasing behavior and set their product pricing accordingly.

Transportation and Commuting

All the taxi-booking, vacation planning apps that students of best engineering colleges in Jaipur use run on machine learning. Whether it is customer experience or demand-supply gap, machine learning systems use data to manage and further optimize the booking process. While using a ride-booking app, they must have come across recommended destinations. Machine learning algorithms use historical data to understand the traveled routes and provide suggestions accordingly. Apps like Uber and Ola use extensive data analysis to predict both time and areas of demand. Once the app calculates the demand, drivers are defined so that they can offer rides for that particular area. This is how ride-hailing companies manage the demand-supply gap. Also, Machine learning algorithms reduce ETA by recommending the fastest routes in real-time. For peak hours, these demand-supply predictions work by suggesting higher prices to make these services profitable.

Vacation planning apps use the same system to recommend the hotel bookings, cheapest flight fares, and more.

Fraud Detection

While the vast amount of data available on the internet makes for a great case of data studies and analysis. Also, it increases the chances of fraudulent activities. Machine learning is emerging as an effective technology to secure our cyberspace. Supervised and unsupervised ML models are being used by the students of best BTech colleges in Jaipur to detect different kinds of online frauds, ranging from spotting anomalous behavior to preventing money laundering. Even the entertainment and media industry are facing undeniable problems with online frauds.

Virtual Personal Assistant

Virtual personal assistants have surfaced as one of the most important finds of the 21st century. Machine learning algorithms have done phenomenal work in the field of speech recognition, text to speech, natural language processing, and speech to text conversion. Once students of best engineering colleges ask them a question, they scan through the internet to find they relevant answers. In addition, they keep track of their schedule, goals, and preferences to recommend relevant information. These virtual personal assistants feed on all their queries and inputs (asking about the weather or the traffic) to continually improve and self-learn. ML algorithms collect and refine information based on the user’s past behavior. This process helps in customizing results as per the user profile.

Social Media

Today, with more than 2.5 billion active users every month, social media platforms like Facebook and more are some of the biggest communities. Social media has become an inseparable part of our lives. Targeted ads, friend suggestions, and personalized news feed are a few of the ways in which machine learning algorithms are improving their experience. Machine learning algorithms go through their profile to understand the friend requests they send, friends they connect with, groups they join, their interests, and based on that provide suggestions on who they can become friends with. In other words, ML algorithms recommend similar pins based on the objects (pins) students of engineering colleges have pinned in the past for Pinterest. Computer vision is a subset of machine learning that scans through images to identify objects and patterns and uses this data to create recommendations.

Computer vision is mainly used for the face recognition feature in Facebook and Google. Every time Facebook asks them to tag themselves in a photo, it is because computer vision has scanned through their facial features to recognize the features unique to them. Once the ML systems have collected sufficient data on their facial features, it can accurately suggest the tag.

Instant Translation

Google Translate and other such apps are making language barriers less important. Apps like Google Translate and iTranslate use machine learning algorithms to make translation accurate and semantic as possible. The ML programs have evolved from rudimentary levels to include broader contexts and complex sentence structures.

Google Neural Machine Translation uses Natural Language Processing to self-learn from different languages and exhaustive dictionaries to translate languages correctly. Also, it uses techniques like NER (Named Entity Recognition), Chunking, POS tagging and more to understand language intonation and deliver the most relevant translation.

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