4 MIN READ Machine Learning can drive the efficiency of Predictive Maintenance for Industries Machine Learning is a method that helps train machines to learn from data sets, without being programmed explicitly. Using training data, a Machine Learning algorithm is taught to build a learning model. The machine can also enhance performance by improving the experience based on the previous data set. Data sets for training and validation are usually defined separately. An advanced learning algorithm acts on complex sets of data to make a prediction based on the underlying model. A newly developed algorithm is deployed based on the accuracy of the predictions it can make. However, if it lacks accuracy, the algorithm is trained to work on a new model with an amplified training data set. The intelligent capability of Machine Learning lies in improving with the number of past experiences. Robotics and Machine Learning The Machine Learning discipline also involves the use of complex algorithms that can educate the robots to carry out meaningful and organized tasks. For example, in a typical scenario of filtering emails, algorithms help in classifying emails and sorting them based on previously identified use-cases. Another suitable example is of computer game that is easy for the human at the first attempt, but gets tougher with each game, as the machine has learned over time. Machine Learning models are the driving force behind advanced robotics. It relies on a sequence of algorithms that use learning data to help perform tasks and improve with experience. AIBridgeML is specialized in cutting-edge RPA solutions for onboarding, reception and helpdesk, payroll processing, among others. Machines will be trained to handle tasks with advanced cognitive abilities Robot orchestration with ability to handle speech inputs and dynamic actions Advanced logic gates and neural networks shall support the RPA of the future Data-mining to determine the number of web-clicks for gauging User Experience Improved productivity and optimum resource utilization in manufacturing units Diagnosis of diseases and the prediction of weather with new learning models Machine Learning models will potentially drive new-age location-based services Supervised Learning vs. Unsupervised Learning Supervised learning is based on a model that gets its training from data sets having the input as well as output respectively. We build a supervised learning model with different Machine Learning algorithms that define the training as well as the validation datasets. Based on the experience from past learning, the supervised model can improve performance in the next iteration by relying on previous data sets as the base. An unsupervised model learns without the aid of training data. It has no prior experience or training based on any form of datasets. Such a model sorts information from similarities or patterns, without the use of any training data. Once data scientists or engineers integrate the raw data with unsupervised learning models, it is possible to form different data clusters based on specific patterns. Machine Learning can help firms and businesses across a wide range of verticals and business domains: BFSI: The application of Machine Learning in BFSI and Insurance mobilizes the industry and customers with advanced learning from key data on new policies and insurance coverage. Banks can leverage Machine Learning in BFSI to streamline transaction data processing. Machine Learning in Insurance is set to accelerate processes towards superior customer experience for policyholders. E-commerce: Machine Learning in E-commerce relies on intelligent learning models. It can effectively transform order processing, stock augmentation and many more functions - based on previous or real-time data. Online shopping has grown manifold with intelligent capabilities driven by Machine Learning in E-commerce. Retail: Machine Learning plays a significant role in capturing buyer data and purchasing trends. Furthermore, Machine Learning in Retail can identify prospects based on class, gender, and literacy. With new and more complex evolution of Machine Learning in Retail, the models can be trained on real-time data on customer visits and monthly or seasonal footfalls to the store as well as logistics and supply chain across the retail channel partners. Manufacturing: The advent of Industry 4.0 has also helped Machine Learning in Manufacturing by scaling up production at minimal costs. An increase in intelligent collaboration with machines is now possible with the real-time collation of data from past production schedules. As such, Machine Learning in Manufacturing is set to revolutionize the industry with efficiency across production and assembly lines. Healthcare: With vast amounts of data, Machine Learning in Healthcare is poised to incorporate cost-effective and intelligent ways of analyzing patient data to accelerate the diagnosis of diseases, as well as use of intelligent agents in surgeries. Our expertise in developing solutions for Machine Learning in Healthcare falls in line with continuous R & D in the respective domain. 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4 MIN READ Machine Learning can drive the efficiency of Predictive Maintenance for Industries Machine Learning is a method that helps train machines to learn from data sets, without being programmed explicitly. Using training data, a Machine Learning algorithm is taught to build a learning model. The machine can also enhance performance by improving the experience based on the previous data set. Data sets for training and validation are usually defined separately. An advanced learning algorithm acts on complex sets of data to make a prediction based on the underlying model. A newly developed algorithm is deployed based on the accuracy of the predictions it can make. However, if it lacks accuracy, the algorithm is trained to work on a new model with an amplified training data set. The intelligent capability of Machine Learning lies in improving with the number of past experiences. Robotics and Machine Learning The Machine Learning discipline also involves the use of complex algorithms that can educate the robots to carry out meaningful and organized tasks. For example, in a typical scenario of filtering emails, algorithms help in classifying emails and sorting them based on previously identified use-cases. Another suitable example is of computer game that is easy for the human at the first attempt, but gets tougher with each game, as the machine has learned over time. Machine Learning models are the driving force behind advanced robotics. It relies on a sequence of algorithms that use learning data to help perform tasks and improve with experience. AIBridgeML is specialized in cutting-edge RPA solutions for onboarding, reception and helpdesk, payroll processing, among others. Machines will be trained to handle tasks with advanced cognitive abilities Robot orchestration with ability to handle speech inputs and dynamic actions Advanced logic gates and neural networks shall support the RPA of the future Data-mining to determine the number of web-clicks for gauging User Experience Improved productivity and optimum resource utilization in manufacturing units Diagnosis of diseases and the prediction of weather with new learning models Machine Learning models will potentially drive new-age location-based services Supervised Learning vs. Unsupervised Learning Supervised learning is based on a model that gets its training from data sets having the input as well as output respectively. We build a supervised learning model with different Machine Learning algorithms that define the training as well as the validation datasets. Based on the experience from past learning, the supervised model can improve performance in the next iteration by relying on previous data sets as the base. An unsupervised model learns without the aid of training data. It has no prior experience or training based on any form of datasets. Such a model sorts information from similarities or patterns, without the use of any training data. Once data scientists or engineers integrate the raw data with unsupervised learning models, it is possible to form different data clusters based on specific patterns. Machine Learning can help firms and businesses across a wide range of verticals and business domains: BFSI: The application of Machine Learning in BFSI and Insurance mobilizes the industry and customers with advanced learning from key data on new policies and insurance coverage. Banks can leverage Machine Learning in BFSI to streamline transaction data processing. Machine Learning in Insurance is set to accelerate processes towards superior customer experience for policyholders. E-commerce: Machine Learning in E-commerce relies on intelligent learning models. It can effectively transform order processing, stock augmentation and many more functions - based on previous or real-time data. Online shopping has grown manifold with intelligent capabilities driven by Machine Learning in E-commerce. Retail: Machine Learning plays a significant role in capturing buyer data and purchasing trends. Furthermore, Machine Learning in Retail can identify prospects based on class, gender, and literacy. With new and more complex evolution of Machine Learning in Retail, the models can be trained on real-time data on customer visits and monthly or seasonal footfalls to the store as well as logistics and supply chain across the retail channel partners. Manufacturing: The advent of Industry 4.0 has also helped Machine Learning in Manufacturing by scaling up production at minimal costs. An increase in intelligent collaboration with machines is now possible with the real-time collation of data from past production schedules. As such, Machine Learning in Manufacturing is set to revolutionize the industry with efficiency across production and assembly lines. Healthcare: With vast amounts of data, Machine Learning in Healthcare is poised to incorporate cost-effective and intelligent ways of analyzing patient data to accelerate the diagnosis of diseases, as well as use of intelligent agents in surgeries. Our expertise in developing solutions for Machine Learning in Healthcare falls in line with continuous R & D in the respective domain.
