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Machine Learning simplified

Recently, I have developed an interest in Big Data and more specifically “Machine Learning.” Honestly speaking, I have benefited a lot personally and professionally from implementing some basic forms of the methods I am going to discuss. Hence, I wanted to share some of these concepts as I progress in this amazing field. I have decided to release series of posts to discuss machine learning in the simplest form possible avoiding the complexity of the mathematical models when possible. The aim is to make these posts appealing to a wider audience. Those who develop interest can surely find more advance material online or in bookstores (you can also contact me for more in-depth resources related to this field).

Machine learning is about using machines (computers, supercomputer, robots, you name it) to predict or infer outcomes using complex datasets and statistical models. Machine learning is usually classified into two branches which are: 1- Supervised 2- Unsupervised learning. Usually differentiated by the “labeled” dataset that we’re dealing with or the level of human interference or “supervision” associated with studying the relationship between the variables.

Clearly, the models or algorithms associated with machine learning are not something new! There is an evidence that these models were produced centuries ago and many academic research papers talked about these statistical models. However, what makes Big Data or Machine Learning a Hot Topic these days is that while earlier papers focused of very specialized fields such as astronomy; nowadays machine learning applications have seen great demand and use in wider fields such as hospitality, marketing, medicine, retail and even video games. Opss, did I forget to mention TESLA and their autonomous cars?

Peter Norvig, Google’s director of research; once said: “We don’t have better algorithms. We just have more data.” and I add to that also the availability and accessibility of advanced computational technologies that made these complex computations possible.

Final Note:

I believe this is enough of an introduction to the subject for this time. Kindly note that I am by no means declaring myself an expert in this filed. It is only part of sharing the knowledge and thus I have two requests from the readers:

1- If you are an expert in this field and find no new information produced by this post, please assist me in correcting or confirming if what I have said and written is right.

2- If you are new to this field and benefited from my article, please like the post and share it with others whom you think would benefit from it as well.

Until next time…