Machine Learning: Some lesser known facts

Machine Learning (ML) has become a buzz word in today’s world. Although we can have its references since the middle of the twentieth century it has gained its popularity during the last few years. Mainly because of its immense capability to explore a large amount of data without the need for any programming and hence the simplicity to use.

Since Machine learning is still a new concept and there are several doubts and misconception about it. In this article, I will try to explore some of these facts that are less known about Machine Learning along with very basic ideas like what is Machine Learning and how it is making our lives better.

Let’s start with a famous conversation of an interview to hire a Machine Learning expert. You must have read this before but I like this so much and it can give a good start to this article. 

So as the interview starts, the interviewer starts asking questions to the candidate:

Interviewer: What is your specialization?

Candidate: Machine Learning

Interviewer: What is 23+34?

Candidate: It’s 10

Interviewer: No, wrong answer, its 57

Candidate: It’s 35

Interviewer: No, wrong answer again, it’s 57

Candidate: It’s 50

Interviewer: No, the answer is still 57

Candidate: It’s 57

Interviewer: You are hired !!!

Although it is a joke, to some extent it reflects how the Machine Learning works. Machine Learning is all about learning from the data it is fed with. Here is a famous quote from Thomas H. Davenport, Analytics thought-leader from the Wall Street Journal which reflects the power of Machine Learning;

“Human can create one or two good models a week; Machine Learning can create thousands of good models a week”

Thomas H. Davenport, Analytics thought-leader from the Wall Street Journal

Importance of Machine Learning in the present context

Today we have a huge amount of data popularly known as big data. This can be a gold mine of knowledge if used and explored properly. Data mining, Baysian analysis all these are getting popular only because they also cater to extract information from a big pile of data. 

As the volume of data increased, so its complexity. The data comes from varieties of sources, consists of numerous fields. We need modelling techniques which can analyze such kind of data quickly with improved accuracy. So here is Machine Learning for you.

So, what is Machine Learning ?

Machine learning in simple term is converting knowledge from information. We have a huge amount of data in our custody, generated throughout a period over more than 50 years. If it is not used to generate knowledge out of it then this huge volume of data is of no use and we are just scrapping a very valuable resource that can help solve many challenges of humanity.

It is as such a very vast field of data science and assimilates many concepts of other associated fields like Artificial Intelligence.

The beauty of Machine Learning is that it does not need programming by human rather as the name suggests it learns from the data it was fed. In this sense, it is similar to a human who also learns from their past experiences.

This learning comes through a rigorous process of observing the data, finding out the pattern in order to minimize the difference between actual and estimation. 

Machine Learning has three main categories, which are

Application of Machine Learning?

Application of Machine Learning
Use of Machine Learning
Photo by Andy Kelly on Unsplash

Recent advances made in Machine Learning enables computer some of the tasks which can only be handled by human until very recent time. In our daily life, we take help or use applications which use this technique and most of the time we don’t even know that it is Machine Learning which is making our lives easier.

In daily life

We can take a simple example of getting personalised Google news. This application which type of news you are interested in by keeping an account of your likes and dislikes as you time to time input in Google’s database. The same technique is used by Facebook to suggest you groups or pages that you may like. Ever wonder how your email service identifies spam emails for you and discriminates from important mails, thanks to ML.

Online video streaming services like Netflix, Amazon Prime, Hotstar etc. or music streaming applications like Spotify all of them have a nice feature which automatically populates your account with contents you prefer. Here the essence is Machine Learning; it analyzes your popular choices and suggests content according to your choice.

Image/speech recognition & medical research

Image recognition uses this technology to answer whether an animal is a cat or dog, identifying persons crossing the road, identifying your handwriting and converting into texts and many more.

In a similar way converting voice into text which is predominantly in use in several platforms like speech to text tool in Google doc and here also ML plays an important role.

In medical research, ML is a fast-growing technology. It helps in analyzing voluminous data and to identify trends and patterns.  Especially with the advent of wearable devices and sensors which keep track of vital parameters of patient’s health. The data generated by these devices are analyzed through ML often in real-time to enable medical practitioners to detect any trend and red flag any symptom for better diagnosis. 

Oil and gas sector

In this sector, ML finds its use to identify natural resources like minerals under the ground, pointing out any risk involved in the performance of the refinery sensors and chance of failure, also preparing an optimized oil distribution plan to make it more cost-effective and efficient.

Thus almost in every sector of our society, the use of Machine Learning is rapidly expanding. In absence of Machine Learning, performing such a resource-intensive and time-consuming process would not be at all feasible in traditional ways.

Futuristic applications

Few applications of Machine Learning which are still in the testing phase, are always been the popular topics of science fiction stories. We are now frequently hearing and reading about self-driving cars of Google or Tesla. This is already a reality now, but go back 10 years, such a concept used to be a subject of science fiction only. The basic concept behind this revolutionary invention is Machine Learning.

Almost every industry who deal with a large amount of data has realized the importance of Machine Learning. Be it banking and finance sector, automobile, research or health care sector ML enables them to work more efficiently and have an edge over their competitors with the help of data insights often in real-time. 

So, what is Artificial Intelligence (AI) then?

If you have read up to this, then this question is most probably rising in your mind and it is bound to. Although most of the times we use the terms AI and Machine Learning interchangeably they are not the same. AI makes machines to emulate human intelligence whereas ML helps machines to learn from data.

Read this article for a brief about Artificial Intelligence (AI)

Artificial Neural Network (ANN) as its name suggests it mimics the neural network of our brain hence it is artificial. The human brain has a highly complicated network of nerve cells to carry the sensation to its designated section of the brain. The nerve cell or neurons form a network and transfer the sensation one to another. Similarly in ANN also a number of inputs pass through several layers similar to neurons and ultimately produce an estimation.

Schematic diagram of Artificial Neural Network
Schematic diagram of Artificial Neural Network

Machine Learning is a way to implement Artificial Intelligence. Machine Learning has been in application since decades but in recent days as Artificial Intelligence came into action Machine Learning, to be more specific Deep Learning has become more popular.

ANN: a deep learning process

ANN is a deep learning process, the burning topic of data science. Deep learning is basically a subfield of Machine Learning. You may be familiar to the machine learning process and if not you can refer to this article for a quick working knowledge on it. Talking about deep learning, it is in recent times find its application in almost all ambitious projects. Starting from basic pattern recognition, voice recognition to face recognition, self-driving car, high-end projects in robotics and artificial intelligence deep learning is revolutionizing the modern applied science.

Read about supervised machine learning here

ANN is a very efficient and popular process of pattern recognition. But the process involves complex computations and several iterations. The advent of high-end computing devices and machine learning technologies have made our task much easier than ever. Users and researchers can now focus only on their research problem without taking the pain of implementing a complex ANN algorithm.

The concept of Artificial Intelligence although not very new, it was first used in 1950 and was supposed to use a computer to perform such activities which can only be done by human beings only.

So in that sense, AI is a much broader concept and ML can be considered as a subset of it. AI is as a whole mimics the concept of human intelligence and to achieve it ML plays a very important role by extracting information from data without the need for programming.

Machine Learning Vs Deep Learning Vs Data Mining

Often these three concepts are little confusing and the main reason is all these techniques have the same goal, which is to get an insight, relationship or trend of the data in hand. But they differ in their execution and abilities. 

Machine Learning

As we discussed, Machine Learning functions more like statistical models, where there is a mathematically proven strong theory about the distribution of the data and it is assumed that the data fulfil some assumptions too. The advantage of Machine Learning is that even if we do not have any theoretical idea about the distribution of the data it can learn from the data through several iterations until the best pattern is found. Hence, the process of ML can be easily automated too.

Data Mining

It is a much broader concept with the same objective as ML and encompassed a variety of concepts to achieve that. Like deep learning uses traditional statistical theories, text analytics, time series algorithm, data manipulation techniques and even Machine Learning too in order to identify an underlying pattern in the data. 

Deep Learning

It is a more advance concept compare to the above two. Deep learning involves the state of the art technologies combining modern high-end computing and neural networks to identify complex patterns in a large amount of data. Advance technologies like image recognition, recognizing words from the sound which are still in the testing stage are all subject of deep learning.

Some facts on Machine Learning

At the very beginning I have mentioned that being a new concept, some ideas about Machine Learning are also popular but not completely true. Here I will try to discuss all those lesser-known facts about ML.

Fact 1: It is not complete automated process and human intervention is required

There is a misconception that ML is a 100% automated process, which is not completely true and human intervention is necessary to create and improve algorithms. The system needs context and parameters to operate which again provided by human operators.

Fact 2: Having advance knowledge in Mathematics is not a prerequisite for simple application of Machine Learning

You can start the application of ML to analyze your data with some practice and guidance. There are lots of content available on the internet some of them are free whereas few are premium courses.

To start practising with ML you can choose any of the free courses. The main factor is you have to practice a lot. I can suggest you a free crash course on ML by Google Developers, developed by Google, so no question about the quality.

The MOOC’s course on ML in Coursera is also very good to start your learning session.

Fact 3: Machine Learning and Artificial Intelligence are not the same

Some people have this notion that these two are same, even I used to have the same idea until I came across this article published in Forbes. It was a very good comprehensive discussion about the differences between these two, read it you will get your many doubts about ML and AI cleared.

Fact 4: Even without a very sound knowledge of programming language you can learn the application of ML

Oh… it certainly helps, having good knowledge in a few programming languages can help you jump start your carrier in ML, but it is not at all an essential one. Its just you have to give some little more time when you are first time writing your code for Machine Learning. Be it R or Python or any other language, you learn it by making errors, this is the most effective way of learning any language.

So, in nutshell, if you are interested in learning ML, just start it now, take a small dataset, write a small piece of code. There will be errors in the beginning, don’t let it hold you back. Soon you will start enjoying its beauty and it will get more and more interesting.


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