Machine Learning: Solving 8 Major Problems Easily

Machine Learning: Solving 8 Major Problems Easily
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Machine Learning and AI have burst in popularity over the last few years and every new startup strives to use these techniques to disrupt traditional markets. Adding the word AI (Artificial Intelligence) to a startup pitch can greatly enhance the odds of getting funding.

No doubt, Machine learning is going to transform every business. Artificial intelligence’s growth has led to a new era in which Machine learning (ML) developers can employ principles of AI and ML to solve a sort of problem. Every industry across the globe has challenges that can be solved effectively by applying ML.

In this article, we explain how Machine learning can solve big problems easily. Let’s take a glimpse at some of the major business problems solved by Machine learning.

8 Major Problems Solved by Machine Learning

  • Manual Data Entry –

Error and duplication of data are major business problems for an industry wanting to automate its processes. Machine learning algorithms and imminent modelling algorithms can significantly enhance the situation. ML programs use the generated data to improve the process as more calculations are made. Thus, machines can learn to perform time-intensive documentation and data entry duties.

  • Financial Analysis –

Due to a huge volume of data, quantitative nature, and detailed historical data, machine learning can be employed in financial analysis. Modern use cases of Machine learning in finance cover algorithmic trading, portfolio management, fraud detection, and loan underwriting.

Furthermore, future applications of Machine learning in finance involve chatbots and conversational interfaces for customer service, security, and opinion analysis.

  • Detecting Spam –

Spam detection is the most advanced problem solved by ML. Thanks to “Neural networks” in its spam filters, Google now owns a 0.1% spam rate. Brain-like “neural networks” in their spam filters can determine to identify junk mail and phishing messages by analyzing rules across a large collection of computers. In addition to spam detection, social media websites are adopting ML as a way to recognize and filter abuse.

  • Image Recognition –

Computer vision provides statistical or symbolic data from images and high-dimensional data. It includes machine learning, data mining, database knowledge discovery, and model recognition. Potential business uses of image recognition technology are mostly found in healthcare, automobiles such as driverless cars, marketing campaigns, etc.

  • Product Recommendation –

Machine learning empowers a product based recommendation system. The ML algorithm recognizes hidden patterns among items and focuses on grouping similar products into groups. A model of this decision process would let a program to obtain recommendations to a customer and motivate product purchases. E-Commerce businesses such as Amazon have this facility.

  • Customer Lifetime Value Predictions –

Businesses have a large amount of marketing-related data from several sources such as email campaigns, website guests, and lead data. Employing data mining and machine learning, an exact prediction for individual marketing offers and incentives can be performed. With the use of Machine learning, savvy marketers can reduce the guesswork involved in data-driven marketing.

  • Medical Diagnosis –

Machine learning in the medical domain will improve a patient’s health with the least costs. Use cases of Machine learning are making near-perfect analyses, suggest the best medicines, predict readmissions, and recognize high-risk patients. Adoption of ML is falling at a rapid speed despite many hurdles, which can be overcome by practitioners and experts who know the legal, technical, and medical barriers.

  • Predictive Maintenance –

The manufacturing industry can use AI and ML to identify meaningful patterns in factory data. Curative and preventive maintenance practices are expensive and inefficient. Whereas predictive maintenance reduces the risk of unexpected failures and the amount of unnecessary preventive maintenance activities.

Also Read: Why Every Business Should Use Machine Learning?

4 Benefits of Machine Learning 

  • Continuous Improvement –

Machine Learning algorithms are proficient in learning from the data we provide. As new data is provided, the model’s precision and efficiency to make decisions improve with consequent training. Giants like Amazon, Walmart, etc collect a large volume of new data every day. The exactness of finding associated products or suggestion engine upgrades with this huge amount of training data available.

  • Trends and Patterns Identification –

This benefit is a no brainer. We all associated with Machine Learning technology are very much aware of how the diverse Supervised, Unsupervised, and Reinforced learning algorithms can be utilized for a few grouping and relapse issues. We identify many trends and patterns with an immense amount of data using this technology.

  • Automation of Everything –

A very powerful benefit of Machine Learning is its ability to automate several decision-making tasks. This saves up a lot of time for developers to use their time for more prolific use. Machine Learning is transforming the world with its automation for almost everything we can imagine of.

  • Wide Range of Applications –

Machine Learning is employed in every business these days, for example from security to education. Companies make profits, cut expenses, automate, predict the future, analyze trends and patterns from the prior data, and many more. Machine Learning is a part of Artificial Intelligence, the latest trends, and applications can be seen in Artificial Intelligence Trends in 2020.

Reasons Why Machine Learning Is So Popular 

  • The modern challenges are high-dimensional in nature.
  • With rich data sources, it is essential to build models that solve problems in high-dimensional space. Through this, the models can be integrated into working software.
  • It supports the types of products that are being demanded by the industry.

5 Ways Machine Learning will Impact your Everyday Life

  • Innovations in Banking –

Using location data and purchase patterns, AI can support banks and credit issuers identify fraudulent behavior while it is befalling. The machine learning-based exception detection models monitor transaction requests. They can detect patterns in your transactions and alert users to suspicious activity.

  • Personalized Digital Media –

Machine learning has immense potential in the entertainment industry, and the technology has already found a home in streaming services such as Netflix, Amazon Prime, Spotify, and Google Play. Some algorithms are already being used to reduce buffering and low-quality playback, getting you the most high-grade quality from your internet service provider.

Moreover, ML algorithms are also making use of the almost unlimited stream of data about customers’ viewing habits, helping streaming services offer more helpful recommendations.

  • Home Security & Smart Homes –

For the best technology in home security, many householders look toward AI-integrated cameras and alarm systems. These advanced systems use facial recognition software and machine learning to build a record of your home’s frequent visitors, allowing these systems to recognize uninvited guests in an instant.

Another benefit of smart homes would be a reduction of household trash and automated recycling, setting the household in better stability with the ecosystem. Freeing humans from the house job could deliver major benefits in terms of enhancing sustainability, saving time, and reducing stress.

  • Enhanced Health Care –

Hospitals that use machine learning to aid in treating patients see fewer accidents and fewer cases of hospital-related diseases, like sepsis. Today, high-performance computing GPUs have become essential tools for machine learning and AI platforms.

Machine learning models quickly render real-time insights and help healthcare professionals diagnose patients faster and more certainly, develop innovative new drugs and treatments, predict adverse results, and reduce the costs of healthcare for providers and patients.

  • Streamlined Logistics and Distribution –

Currently, the shipping costs are quite expensive. Developing efficiency through AI integration and automation will mean big losses in shipping costs and improvements in delivery speed. Optimization opportunities in supply chain management and vehicle maintenance will also make shipping quicker, simpler, and more environment friendly.

Final Thoughts 

Above were some of the major problems that we think can be solved by Machine Learning. Though, there is no limit to how many problems can be solved with Machine learning. We still believe that there is a lot more to go into the domain of Machine learning and a lot of problems can be solved.

Since the field has evolved both in terms of identity, methods, and tools it has various benefits and thus the horizon of jobs has increased. Aside from that, the thing which makes it popular is that there is an excess of data to learn from. Adding to that, there is an abundance of calculation to run the methods. All this makes it a brilliant field and a field to look up to!

So, what are you waiting for? It is an opportunity to explore everything about Machine Learning. Here is Eduonix’s best approach for you –

1.) A Complete Guide on Machine Learning Probability
2.) 6 Machine Learning Algorithms You Should Learn as a Newbie


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