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 Machine Learning Application in Daily Life: 1.  Commute Estimation   In general, a single trip takes more than average time to complete, multiple modes of transportation are used for a trip including traffic timing to reach the destination. Reducing commute time is not simple yet, here below you find how machine learning is aiding in reducing commute time,   Google’s Map:  Using the location data from smartphones, Google Maps can inspect the agility of shifting traffic at any time, moreover map can organize user-reported traffic like construction, traffic, and accidents. By accessing relevant data and appropriate fed algorithms, Google Maps can reduce commuting time by indicating the fastest route.   Riding Apps:  From how to fix the price of the ride, and how to minimize the waiting time to how do riding cars fix up one’s trip with other passengers to lessen diversion. Yes, the solution is machine learning.  ML assists the company to estima...

 Application of ML in Military Machine learning is now a critical component in modern warfare systems. Let’s explore 7 key military applications of artificial intelligence today.         Machine learning has become a critical part of modern warfare, and a major point of interest for me, both as an Army veteran and data scientist. Compared with conventional systems, military systems equipped with ML/DL are capable of handling tremendously larger volumes of data more efficiently. Additionally, AI improves self-control, self-regulation, and self-actuation of combat systems due to its inherent computing and decision-making capabilities; a critical aspect to consider due to the nature of combat. AI/ML is being deployed in almost every military application, and increased funding for research and development from military research agencies promises to drive adoption of AI-driven systems in the military sector even further. For instance, the US Department of Defense’s (...

 How are Machines Trained? The process of making machines learn from historical data is known as training. The science of machine learning revolves around teaching the machine by using datasets of different sizes composed of useful or random facts and/or figures and feeding them to the machine. The essence of this activity is to help the machine observe the data, establish meaningful connections between the different pieces of the supplied information, and prepare to make decisions about incoming data by incorporating these pre-established connections, also known as rules. Want to know in detail about the process how any machine is get trained and capable of doing work like humans. Click on the video given below:  

 DeepLearning Frameworks That Techies should learn in 2022.           Deep learning framework s help data scientists and ML developers in various critical tasks. As of today, both predictive analytics and machine learning are deeply integrated into business operations and have proven to be quite crucial. Integrating this advanced branch of ML can enhance efficiency and accuracy for the task-at-hand when it is trained with vast amounts of big data. In this video, we will explore the top deep learning frameworks that techies should learn this year Tensor Flow : The Javascript-based open-source learning platform has a wide range of tools to enable model deployment on different types of devices. While the core tools facilitate model deployment on browsers, the lite version is well-suited for mobiles and embedded devices. Keras : It is an open-source framework that can run on top of Tensorflow, Theano, Microsoft Cognitive Toolkit, and Plaid ML. Keras framework ...

 Case Study         A  2 017 study  out of the Massachusetts General Hospital and MIT showed that an artificial intelligence (AI) system was equal or better than radiologists at reading mammograms for high risk cancer lesions needing surgery. A year earlier, and  reported by  the Journal of the American Medical Association, Google showed that computers are similar to ophthalmologists at examining retinal images of diabetics.  And recently, computer-controlled robots performed intestinal surgery successfully on a pig. While the robot took longer than a human, its sutures were much better more precise and uniform with fewer chances for breakage, leakage, and infection. Tech boosters believe that AI will lead to more evidence-based care, more personalized care, and fewer errors. Of course, improving diagnostic and therapeutic outcomes are laudable goals. But AI is only as good as the humans programming it and the system in which it operates...

Friend or Foe? What is the future of artificial intelligence (AI)? Will it be possible for robots to be autonomous? If so, when will that happen and will it be a good thing? We asked four experts what they think.        I would say that we are quite a long way off developing the computing power or the algorithms for fully autonomous AI, though I do think it will happen within the next thirty or forty years. We will probably remain in control of technology and it will help us solve many of the world’s problems. However, no one really knows what will happen if machines become more intelligent than humans. They may help us, ignore us or destroy us. I tend to believe AI will have a positive influence on our future lives, but whether that is true will be partly up to us.        I have to admit that the potential consequences of creating something that can match or surpass human intelligence frighten me. Even now, scientists are teaching computers how t...

 Applications There are numerious applications of Machine Learning. Some of them are represented in the given below diagram. Given below video also show some real time examples of Application of machine learning.

Supervised Learning In supervised learning, the computer is provided with example inputs that are labeled with their desired outputs. The purpose of this method is for the algorithm to be able to “learn” by comparing its actual output with the “taught” outputs to find errors, and modify the model accordingly. Supervised learning therefore uses patterns to predict label values on additional unlabeled data. For example, with supervised learning, an algorithm may be fed data with images of sharks labeled as  fish  and images of oceans labeled as  water . By being trained on this data, the supervised learning algorithm should be able to later identify unlabeled shark images as  fish  and unlabeled ocean images as   water . A common use case of supervised learning is to use historical data to predict statistically likely future events. It may use historical stock market information to anticipate upcoming fluctuations, or be employed to filter out spam emails. In...

 Introduction Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people. Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs. Any technology user today has benefitted from machine learning. Facial recognition technology allows social media platforms to help users tag and share photos of friends. Optical char...