Machine learning has been making a difference to businesses all over the world. According to a recent research the machine learning market is expected to grow by $30.6 Billion in 2024. The world is increasingly driven by the applications of Artificial Intelligence and Machine Learning.

What is Machine Learning?

Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. It uses data and algorithms to imitate the way that humans learn, gradually improving its accuracy. 

Top Machine Learning Use Cases And Applications

Machine learning applications are already infiltrating a broad spectrum of different business processes. Machine learning can be used in various ways by a business in any industry to improve workflows and processes. Here are a few of them:

Healthcare

1. Personalized Treatment/Medication: Machine learning could play a vital role in determining the kind of genetic markers and genes that respond to a particular treatment or medication.
2. Cancer Prognosis and Prediction: These methodologies can be used to model the evolution and therapy of malignant illnesses and further used to cancer research to develop prediction models, resulting in effective and accurate decision-making.

Finance

1. Fraud Detection for Secure Transactions: The machine learning techniques are applicable in enhancing the security of the transactions by detecting the possibilities of fraud in advance like Credit card fraud detection.
2. Loan Eligibility Prediction: Banks can implement any classification algorithm to classify their customers before deciding who should receive a loan and who shouldn’t.

Cybersecurity

1. Email Monitoring: Machine learning techniques can be instrumental in the detection of cybersecurity attacks by conducting real-time email monitoring. This can prevent malware or other cybersecurity attacks. Phishing emails, for instance, extract sensitive information by tricking the end-users. 
2. Detecting Drive-By Download Attack: Event De-noising CNN, EDCNN is used to in controlling vulnerabilities that are helpful for the attackers to redirect the users to a malicious website and download the malware.

Marketing 

1. Product Recommendations: Using machine learning and AI, websites track your behavior based on your previous purchases, searching patterns, and cart history, and then make product recommendations.
2. Customer Experience: Machine learning is used to drive customization, understand customers, streamline processes and create a memorable experience. 

Travel 

1. Dynamic Pricing: Refers to a revenue management pricing strategy in which travel agencies can set flexible prices for products or services based on current market demands.
2. Sentiment Analysis: Applications of sentiment analysis include campaign monitoring, brand monitoring, stock market analysis, compliance monitoring, etc.

Conclusion

Popular machine learning applications and use cases are evolving at a rapid pace. You know that today modern-day applications cannot exist without machine learning algorithms. From simple chatbots to self-driving cars, Machine Learning is everywhere. ML is improving almost every aspect of our lives.

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