5 Simple But Important Things to Remember About Machine Learning

September 21, 2018
Machine Learning

On the landscape of technological advancements, Machine Learning is taking giant strides. Every sector is getting infused with artificial intelligence be it social networks, retail stores, automobiles, home appliances etc. It is no more the ‘Next’ big thing, and it is the ‘Thing’ today.

‘What is machine learning?’ is something most of us have read about, but the famous question is, Why it has grabbed so many eyeballs over the years?  Primarily because it can predict events and spot patterns, that most humans are not efficient at. A developer cannot write code for every possible scenario. He or she can work around a specific data set, but can’t make generalised conclusions, which Machine learning can achieve.

Also Read: What is The Easiest Way To Learn Machine Learning?

Machine learning is a complex subject, and its education can never be complete. There are so many exciting aspects to understand about ML. You can go for a Machine learning course in India to realise its nitty-gritty. We have discussed some of the issues below:

Machine Learning comprises of three stages, namely, Representation, Evaluation, and Optimization

  • Representation: In this stage, a classifier is converted to a language that a machine can understand. Moreover, a set of classifiers, also known as hypothesis space, is dedicated for a learner.
  • Evaluation: Once the classifiers are chosen, it’s important to segregate good classifiers from the bad ones. The internal function used for evaluating the classifier is different from the external feature of the algorithm. The classifier itself optimises the outer function.
  • Optimization: Finally, the predictions made by the Model and the actual outcome are compared. Based on this comparison the parameters are optimised so that that perfect outcome can be obtained.

Generalization is the soul of Machine Learning

The essence of Machine Learning lies in generalising so that it can go beyond the scope of specific data-sets, and predict never-seen-before events. An efficient ML model is the one which can quickly adapt to new or unseen data. It’s like how humans learn to drive. They don’t learn to drive on specific roads, but they learn the skill of driving to traverse all kinds of routes and paths.

The ML Model will generalise better if the data is reliable and contains a broad spectrum of observations. It will be easier for the Model to discover the underlying mapping if data are more representative than others. You can understand this concept further by opting for the best Machine learning course in India.

Feature engineering is critical for Machine Learning

Feature engineering enhances Machine Learning Algorithms by utilising core domain knowledge of the data. It develops features using the raw data to improve the predictive ability of algorithms. Such features make the process of Machine Learning a lot easier, as they seamlessly correlate with the class. 

Machine Learning Models also give ‘Too good to be true’ results

To predict an outcome, the Machine learning Model receives the training-data first, and the testing-data afterwards. If the accuracy of the consequences is satisfactory, then the complexity of data is increased to improve the prediction-ability of algorithms. At times, this approach backfires, as the Model becomes too complicated and starts giving poor results. In other words, too much of data stops the learning of algorithms, and instead, they start memorising. Such a model produces a graph where the prediction-line covers the noise-points as well. It produces results that are too good to be true. The best way to deal with Overfitting is to generalise the Model.

Machine learning is not insulated from human errors.

Machine learning won’t take over Humanity, as most of us believe. It is, in fact, vulnerable to human errors. Whenever there is a glitch in the Machine Learning models, the algorithms are rarely responsible for that. Mostly, a human error leads to inappropriate training data, which in turn leads to other systematic errors.

So we may know the answer to ‘What is Machine Learning?’ But by believing that it will surpass us, we are evading our responsibility. We will always be in the driver seat, and it’s our discipline which will decide its future course.

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