Why Machine Learning is Important?
A large volume of data is generated every day by billions of users using the internet. For a human being it's not possible to process data and gets insight from data. This data is very important and profitable for business to guide customer behavior and activity. Machine learning is a collection of advanced algorithms which process data. If short, In machine learning, we perform three main tasks:
Train the model
Test the model
Predict the model
But perform these three tasks we'll have to do lots of pre-processing tasks. The two main challenges of machine learning to data preparation and accurate data collection. then divide data into labels/Fields or properties.
The second task is to select the best model suited for data. To learn and understand machine learning needs statistics skills to predict and evaluate data mining results and predictions.
The Industries using Machine Learning?
Finance Industry like Banks and others institution use machine learning technology to identify important insights in data, and prevent fraud or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.
Machine learning can assess a patient's health in real-time and medical experts can analyze data to identify trends or red flags that may lead to improved diagnoses and treatment. From playing a critical role in patient care, billing, and medical records. Identifying Diseases and Diagnosis,Medical Imaging Diagnosis
Government us machine learning for public safety since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.
Analysis of customer buying patterns with invalid/fraud requests, Analyzing customer interactions with virtual assistants and chatbots, Performing retail analytics at scale to understand year on year growth,Customer sentiment analysis
Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations
Oil and gas
Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. From oil and gas giants to renewable green tech, the energy industry produces a lot of data. AI is perfect for processing large data sets and producing actionable insights
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