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.

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