Machine Learning is Fascinating but rides on Data Quality
Very often they are referred interchangeably but Machine learning (ML) is actually is a type of artificial intelligence (AI) and it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.
Do you know?
- 90% of the world’s data was generated in the last 2 years along with apps and devices that have helped to make sense of data.
- One of 2 very hot 2018 buzzwords will be Artificial Intelligence (AI) and Machine Learning (ML).
Very often they are referred interchangeably but Machine learning (ML) is actually is a type of artificial intelligence (AI) and it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.
Artificial Intelligence in general is machines replicating tasks that humans are good at but computers are not.
Machine Learning is a core subset of Artificial Intelligence and is an idea of collaborating algorithms and statistics and learning from data. It’s a fascinating technology filled with limitless potential. ML relies on algorithms that learn from past examples and help humans perform complex tasks faster and better than ever.
Though Machine learning and Artificial intelligence (AI) are not new concepts but they are exerting a huge influence on all businesses nowadays. Their abrupt innovations are reshaping the technology world.
An example of machine learning is - if you want your website to suggest products to customer based on their buying or browsing pattern on your website, you cannot make recommendations manually. So, you use machine learning to recommend visitors in real-time as they come to the site. Off course, all these recommendations will be based on site visit/conversion data.
Machine learning tries to encode the human decision-making process into algorithms. It learns from the data available at disposal (called data set or training set) and creates inferences using different algorithm to deliver results/predictions.
However, it isn’t that easy to produce the desired results out of this technology. It is very imperative that the data-set fed to ML is complete, reliable, consistent & predictive otherwise it will just be garbage in and garbage out.
As a result of data propagation, many companies are sitting on huge un-tapped data reserves and they are often scattered and in incompatible formats. There is a need to invest in getting your data-set ready for ML implementation on larger chunk.