Last updated on January 29, 2020
Machine Learning model – The what
Demystifying the hullabaloo around machine learning that has been making all the noise over the last few years is one simple fact – all machine learning models operate on the same basic principle. If we take enough data, feed it into the machine, the machine can analyze it and predict accurate patterns.
These patterns are how everything from recommendations on Spotify and Netflix, “suggested for you” on Amazon, online chess, or even self driving cars make decisions. The opportunities are exciting and the possibilities endless. The link between big data and machine learning is clear – it goes without saying that the more data it gets, the more accurately it can predict patterns.
The brain of this machine learning is the model – which is essentially a function that takes in inputs, performs a certain operations as directed by the algorithm, and decides/predicts/classifies the result.
Machine learning modelling – The How
A model is the most likely representation of a dataset, whose data is not completely available – meaning, situations where there is a probability, ML tries and makes predictions and decisions with insights that people are most likely to make.
Simplifying it further, if an algorithm is the technique or the rule, if the algorithm is “taught” with lots of data, what we have is a model.
While building a machine learning model, it is very important to understand that real world data is not perfect. It is okay and part of the process to be tweaking the model, altering approaches and tools, and the path to a successful, efficient model is filled with trial and error. Teaching a machine to analyse data independently is challenging and before determining the right model, there could be significant experiments. All that said, this should not be confused with a lack of direction, procedure or laxity.
Machine learning modelling – The process
An effective ML model would essentially be modelled on the following steps:
Machine learning – The Why
It is by now undoubted that, then why should even be a question. If a company has got data, there can be multiple ways to use it to be a driver for its business. It can be something small as a marketing insight, or significant like using it to drive behavioral economics depending on the business objectives, timeframe, and budget.
Machine learning models – The Where next:
The accuracy of a machine learning model is as reliable as its data. If there is not enough data, and decisions are being made on small subsets of data, it could possibly mean a misinterpretation of a trend or a pattern analysis in the wrong direction. Big data is vital in training machine learning models, and enterprises can apply machine learning to as much as their imagination and innovation allows.
With machine learning algorithms being easily available through open-source communities there are huge resources, frameworks, and libraries that have made development easier.
Also, an organisation will not be using machine language in isolation. Used in combination with deep learning, neural networks, AI, IoT and several other techniques, when the model is online, it continuously garners data, and constantly produces results. Leveraging it to create more reliable results is however the key to a model’s success.
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