And this, in a nutshell’s nutshell, is “Machine Learning“
Part One
Probably imagining a robot or terminator when asking about machine learning, in reality, machine learning is beyond and involved in almost all application you can imagine in our today world. Think of spam filter in your email, voice, face and fingerprint recognition on your phone, your Siri on iPhone, google assistance on android device etcetera have an iota of machine learning. Remembered whenever you log-in to your Netflix, Amazon, Facebook, Jumia, something related to your likeness will be presented (such as products, movies, services etc), these are work of machine learning which is referred to as Recommended System. Let dive into what machine learning is, and how it works.
What is Machine Learning?
Machine learning is a subfield of artificial intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data usually called train data, build models that explain the world, test the model on a test dataset, and predict things without having explicit pre-programmed rules and models.
Why machine learning?
Let assume that you would like to write a recommended system program without machine learning then you would have to carry out the following steps:
In the beginning, you will take a look at a customer’s profile and compare it with a pool of customers to figure out related features for clustering or segmentation.
Secondly, after clustering, you look for their activities and interaction when using the platform or services. Such as clicks, search words etc.
Thirdly, you’d write an algorithm to detect the patterns that you’ve seen, and then the software would recommend a related product, and services if a certain number of those patterns are detected.
Finally, you’d test the program, and then redo the first three steps again until the results are good enough.
Without machine learning, the program will contain a very long list of rules that are difficult to maintain. But if you developed the same software using ML, you’ll be able to maintain it properly.
From the above, data is supply to a machine learning algorithm for training, and which pattern will be detected by the algorithm to form a model that will automate the process of recommending services and products.
In addition, customer or user can update data (profile, profile images), using of traditional techniques would involve updating code again and again whenever there are changes in data but with ML techniques, changes will automatically be detected and all functionality will be maintained.
When should you use machine learning?
- The problem that requires many long lists of rules to find the solution. In this case, machine-learning techniques can simplify your code and improve performance.
- Very complex problems for which there is no solution with a traditional approach.
- Non- stable environments’: machine-learning software can adapt to new data.
In part two of this writeup, categories of machine learning will be discussed as an introduction level.
