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The term Machine Learning was coined by Arthur Samuel, a colonist in the field of artificial intelligence. According to him, machine learning is defined as – “Field of study that gives computers the capability to learn without being explicitly programmed”.
In other words: Machine Learning enables IT systems to identify patterns on the basis of existing data sets and algorithms. The choice of algorithms which we want to use completely depends on what type of data we have and what kind of task we are trying to perform. Machine Learning Course is considered one of the best in the software arena. This has been the reason why things and also the bewildered aspirants are so much in awe of this particular course.

Let us try to understand this by taking an example of students being trained during their exams. When students prepare for exams, they try to learn the subject with complete understanding. During this process, they feed their brain(machine) with a good amount of questions and answers from different books or notes made by them (good quality data). They are actually training their brain with input and output which means what kind of logic do they apply to solve different kinds of questions. Each time they solve questions and find the score(performance) by comparing the answers given. The performance keeps on increasing with the adopted approach. This is the same way in which models are built, machine is trained with data (both inputs and outputs are given to model) and when the time comes test on data (with input only) and achieves our model scores by comparing its answer with the actual output which has not been fed while training. Bangalore and Chennai are considered as one of the frontrunners as far as the software domain has been categorized. Students, here are now coming as flocks in order to study this course and do a lot good in their respective careers. Machine Learning Course in Chennai has picked up its own speed and in the forthcoming years will be one of the sought after courses.

 Difference in Machine Learning and Traditional Programmes

  How Machine learning works

·         Gather past data in any suitable form for processing. The better the quality of data, the more suitable it will be for modeling.

·         Data Processing – Sometimes, the data collected is in the raw form and it needs to be pre-processed.
Example: Some tuples may have missing values for certain attributes, an, in this case, it has to be filled with suitable values in order to perform machine learning or any form of data mining.

·         Divide the input data into training, cross-validation, and test sets. The ratio between the respective sets must be 6:2:2

·         Building models with suitable algorithms and techniques on the training set.

·         Testing our conceptualized model with data which was not fed to the model at the time of training and evaluating its performance using metrics such as F1 score, precision and recall.

 

Machine Learning – Applications

Machine learning is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect. We probably use a learning algorithm dozens of times without even knowing it. Applications of Machine Learning include:

·         Web Search Engine: One of the reasons why search engines like google, bing, etc work so well is because the system has learned how to rank pages through a complex learning algorithm.

·         Photo tagging Applications: Be it facebook or any other photo tagging application, the ability to tag friends makes it even more happening. It is all possible because of a face recognition algorithm that runs behind the application.

·         Spam Detector: Our mail agent like Gmail or Hotmail does a lot of hard work for us in classifying the mails and moving the spam emails to a spam folder. This is again achieved by a spam classifier running at the back end of a mail application.

Today, companies are using Machine Learning to improve business decisions, increase productivity, detect disease, forecast weather, and do many more things. With the exponential growth of technology, we not only need better tools to understand the data we currently have, but we also need to prepare ourselves for the data we will have. To achieve this goal, we need to build intelligent machines. We can write a program to do simple things. But for most of the time Hardwiring Intelligence in it is difficult. The best way to do it is to have some way for machines to learn things themselves. A mechanism for learning – if a machine can learn from input then it does the hard work for us. 

Thus, this has been the reason that why Machine Learning Algorithms are now regarded so highly by the software clad individuals.