Machine Learning

What is Machine Learning :- Machine Learning is a sub-area of artificial intelligence, whereby the term refers to the ability of IT systems to freely find solutions to problems by recognizing patterns in databases. An exciting branch of Artificial Intelligence, Machine Learning is all around us in this modern world. In other words: Machine Learning enables IT systems to recognize patterns on the basis of existing algorithms and data sets and to develop adequate solution concepts. That is why, in Machine Learning, artificial knowledge is generated on the basis of experience. Like Facebook suggesting the stories in your feed, Machine Learning brings out the power of data in a new way. Working on the development of computer programs that can access data and perform tasks automatically through predictions and detections, Machine Learning enables computer systems to learn and improve from experience continuously. While the concept of Machine Learning has been around for a long time, the ability to automate the application of complex mathematical calculations to Big Data has been gaining momentum over the last several years.


At a high level, Machine Learning is the ability to adapt to new data independently and through iterations.  Basically, applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results. As you feed the machine with more data, thus enabling the algorithms that cause it to “learn,” you improve on the delivered results. When you ask Alexa to play your favorite music station on the Amazon Echo, she will go to the one you have played the most; the station is made better by telling Alexa to skip a song, increase volume, and other various inputs. All of this occurred because of Machine Learning and the rapid advance of Artificial intelligence.


How Machine Learning Works :- There is no doubt that machine Learning is one of the most exciting subsets of Artificial Intelligence. It completes the learning task from data with specific inputs to the machine. It’s very important to understand that what makes Machine Learning work and also how it can be used in the future. The Machine Learning process starts with inputting training data into the selected algorithm. Training data being known or unknown data to develop the final Machine Learning algorithm. To test whether this algorithm works or not , new input data is fed into the Machine Learning algorithm. Then The prediction and results are supposed to be checked. If the prediction is not as per the expectations then the algorithm is re-trained multiple numbers of times until the output is not as per the expectation . Self-driving Google car; cyber fraud detection; and, online recommendation engines from Facebook, Netflix, and Amazon. Machines can enable all of these things by filtering useful pieces of information and piecing them together based on patterns to get accurate results.


Benefits of Machine Learning :- Amidst all the hype around Big Data  we keep hearing the term “Machine Learning”. Not only does it offer a remunerative career, it promises to solve problems and also benefit companies by making predictions and helping them make better decisions. In this blog, we will learn the Advantages of Machine Learning. As we will try to understand where to use Machine learning.

  • Identifies trends and patterns :- Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, for an e-commerce website like Amazon, it serves to understand the browsing behaviors and purchase histories of its users to help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal relevant advertisements to them.
  • Accurate Medical Predictions and Diagnoses :- In healthcare industry, ML helps in easy identification of high-risk patients, make near perfect diagnoses, recommend best possible medicines, and predict readmissions. These are predominantly based on the available datasets of anonymous patient records as well as the symptoms exhibited by them. Near accurate diagnoses and better medicine recommendations will facilitate faster patient recovery without the need for extraneous medications. In this way, ML makes it possible to improve patient health at minimal costs in the medical sector.
  • Automation :- A very powerful utility of Machine Learning is its ability to automate various decision making tasks. With Machine Learning, you don’t need to babysit your project every step of the way. Since it means giving machines the ability to learn, it lets them make predictions and also improve the algorithms on their own. A common example of this is anti-virus software, they learn to filter new threats as they are recognized. Machine Learning is also good at recognizing spam. And the best part is they keep improving in accuracy and efficiency. This lets them make better decisions. Say you need to make a weather forecast model. As the amount of data you have keeps growing, your algorithms learn to make more accurate predictions faster.
  • Product Marketing and Assists in Accurate Sales Forecasts :- ML helps enterprises in multiple ways to promote their products better and make accurate sales forecasts. ML offers huge advantages to sales and marketing sector. ML will let you analyze the data related to past behaviors or outcomes and interpret them. Therefore, based on the new and different data you will be able make better predictions of customer behaviors.
  • Benefits of Financial Rules and Models :- Some of the common machine learning benefits in Finance include portfolio management, algorithmic trading, loan underwriting and most importantly fraud detection. In addition, according to a report on ‘The Future of Underwriting’ published by Ernst and Young, ML facilitates continual data assessments for detecting and analyzing anomalies and nuances. This helps in improving the precision of financial models and rules. Machine Learning has a significant impact on the finance sector. 

Useful Reference:-

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