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  We’d like to welcome you to a new series of posts on our blog. We’ll break down the fundamental concepts of Machine Learning (ML) and Artificial intelligence (AI) in non-technical terms, and into bite-sized content. You’ll grasp key concepts faster and get a feel of the underlying mechanics. And with as little math as possible.. This is going to be a fun ride. Machine Learning  is really the verb in this process. How do we expect our AIs to learn? From data, of course, just like we do, absorbing the stimuli and from the environments around us. Somehow we must make machines gain the ability to think and perform actions based on their past experiences (i.e. the data fed in).  So, how do machines learn from data?? If you are a programmer, you might already be trying to decipher the difference between traditional programming (TP) and machine learning. Both of them are designed by humans to solve problems using computing power. In TP, we provide the system wit...

  “Our algorithms find similar patterns of words and phrases across the content submitted by different students and inform us if such matches are high. Pattern Detection, Sir! The program found out the assignments submitted by these four students are very similar. We looked at it and confirmed what the machine found.” Well, this brings us to the next category of  Machine Learning  algorithms –  “Unsupervised Learning-based Algorithms” . Unlike supervised algorithms, in this case, “labelled” data is not required to train the algorithm i.e. a supervisor is not needed. For example, the text from the assignments was the only data given to the plagiarism detector. No labels. The unsupervised algorithm in play over here employs a clustering technique. Yes, yes. It does exactly what you are thinking - groups together objects which are similar to each other (and less similar to other data). So, the algorithm used by the school automatically puts all four assignments under on...