Machine Learning is a present utilization of AI based around the possibility that we should simply have the capacity to give machines access to information and let them learn for themselves. Machine Learning is a field of software engineering that utilizations factual procedures to enable PC frameworks to "learn" (i.e., continuously enhance execution on a particular errand) with information, without being expressly modified.
The two terms regularly show up when the discourse swings to Big Data — informational indexes that are so voluminous and complex that conventional information preparing application programming is insufficient to manage them.
Each of the three terms mirror the quickly changing world we live in.
Machine Learning has received much attention of late, and for good reason. With the rise in Big Data, machine learning has become a key technique for solving problems in areas such as:
Computational finance, for credit scoring and algorithmic trading
Image processing and computer vision, for face recognition, motion detection & object detection
Computational biology, for tumor detection, drug discovery and DNA sequencing
Energy production, for price and load forecasting
Automotive, aerospace and manufacturing, for predictive maintenance
Natural language processing, for voice recognition applications
Businesses and organizations working with large amounts of data recognize the benefits of Machine Learning technology. By gaining insights from Big Data (often in real time), industries work more efficiently and can gain advantages over their competition. Some of the organizations focusing on Machine Learning include:
FINANCIAL SERVICES:
Banks and other businesses in the financial industry use machine learning technology to identify important insights in data, and prevent fraud.
GOVERNMENT:
Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights.
HEALTH CARE:
Machine Learning has made possible wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.
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