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What is Machine Learning? Why is machine learning becoming more and more important?

What is Machine Learning? Why is machine learning becoming more and more important?

 



Machine learning, also known as Machine Learning, is a sub-branch of artificial intelligence (AI) and computer science. It focuses on data processing and algorithms to mimic the way humans learn. Thereby improving the accuracy of the predictions it makes.

Machine learning is an incredibly important component in the evolution of data science. Through the use of statistical methods and algorithms, they are trained to make classifications, predict and discover new knowledge in data mining projects. This knowledge will influence decisions in applications and businesses. 

As the internet is becoming more and more popular, everyone of us still regularly goes online to update news, entertainment, shopping, etc. It is these activities that have produced a large amount of data. As big data continues to expand and grow, so does the market demand for data scientists. It also requires the scientist to assist in identifying the business questions and making the most appropriate decisions.


Figure 1: Areas related to machine learning

According to UC Berkeley (University of California-Berkeley) they divide machine learning systems into 3 main parts:

A Decision Process : Generally machine learning algorithms are used to make a prediction or classification. Based on the input data it can be labeled or unlabelled, the algorithm will make an estimate of a pattern in the data.

An Error Function : An error calculation function that calculates whether the model has a high or low prediction rate, based on known data.

* An Model Optimization Process : After building the basic model, people will optimize the model by adjusting the weights so that the model achieves a suitable exact ratio. certain.

For example, if you're building a movie recommendation system, your prediction algorithm might consider how similar a movie is to movies you've seen. From there, build optimal weights for different features of the system. 

During the training, the algorithm will look at the movies you have watched and the attribute weights of those movies. Questions will be asked like is it a horror movie, is it a comedy, etc. Then the algorithm checks to see if it recommends movies that you (or others) people like you) watched or not. If it predicts correctly, the weight will be kept the same, and if it is wrong, it will reduce the weight so that the next time it does not encounter the same error again.

Figure 2: Netflix has successfully applied a machine learning-based movie recommendation system

Figure 2: Netflix has successfully applied a machine learning-based movie recommendation system

Machine learning algorithms will be automatically updated to improve accuracy after iterations. The nature of these repetitions is unique and valuable because they take place without human intervention. It automatically discovers insights that are not specifically programmed to do so.

Today's virtually limitless data availability, low cost storage capacity, and powerful development of processing have fueled the field of machine learning. Many industries are developing more powerful machine learning models, capable of analyzing larger and more complex data. It also provides faster, more accurate results at scale. Machine learning tools allow organizations to more quickly identify potential opportunities and risks.

Real-world applications of machine learning driving business results can significantly affect a company's bottom line. New techniques in the field are evolving rapidly and extending the application of machine learning to almost limitless possibilities. Many industries have seen machine learning as the best way to model, strategize, and plan. Because they depend on huge amounts of data — and need a system to analyze that data efficiently and accurately.

Figure 3: Applying machine learning to business operations

Figure 3: Applying machine learning to business operations

Health care: 

Sensors can monitor everything from heart rate and steps to oxygen levels and blood sugar. Even sleep generates a significant amount of data that allows doctors to assess the health of their patients in real time. Machine learning can also detect breast cancer through analysis of x-ray images, diagnose diabetic retinopathy through retinal images, etc.

Marketing and Sales: 

Machine learning is revolutionizing marketing. Many businesses have successfully implemented artificial intelligence (AI) and machine learning to increase and enhance customer satisfaction. 

According to Forbes , “57% of executives believe that the biggest benefit of AI and machine learning developments is improved customer experience.” 

E-commerce sites and social networks already use machine learning to analyze the products that you previously purchased and searched for. Then come up with other suggested items you might buy. 

Many experts believe that the future of retail will be dominated by AI and machine learning. Because deep learning business applications are becoming more and more adept at capturing, analyzing, and using data to personalize each individual's shopping experience. Also develop customized targeted marketing campaigns.

Financial services: 

The insights provided by machine learning in this area allow investors to identify new opportunities or know when to trade. 

Machine learning can help with portfolio calibration or risk assessment for loans and underwriting. 

Old-fashioned security questions like usernames and passwords will be replaced by voice recognition, facial recognition, and biometrics.

For the government: 

Machine learning systems make it easier for governments to predict potential future scenarios, adapting to rapidly changing situations. Machine learning can also help in improving cybersecurity, cyber intelligence, supporting counter-terrorism efforts, optimizing availability, and more.