What is Machine Learning?

What is Machine Learning and How is it Shaping our Future?


Doesn’t the overwhelming response as well as the inclusion of voice assistants, algorithm-based apps and websites that are taking automation to the next level amaze you?


While these applications often steal the thunder, it is machine learning that has become a go-to technology for firms willing to enhance consumer experience and bring in efficiency.


Past few years quite evidently show the interdisciplinary scope of machine learning with it being applied in almost every vertical of commerce and the non-commercial sectors.


Before knowing further about how it is going to shape our future let us understand the basics of it.


What is Machine Learning?


Machine Learning Definition


In simple words, machine learning is a concept where the machines learn from the examples as well as the experience instead of needing to be coded explicitly for a task.


This happens with the help of data that is fed into the machine algorithm and ML enables them to build logic based on the previously available information.


Being a sub-division of artificial intelligence, machine learning is looked upon for using the existing data and making relevant predictions based on it.


For e.g. when you shop online its machine learning that analyses your past behaviour on site and recommends you similar products with an intention to make your shopping experience better or to make the firm richer, how would you know! 🙂


How it Evolved?


Machine Learning Evolution


Machine learning is involved in making our lives easier much earlier than we heard of it.


It all started with a test being developed by Alan Turing in 1950 where a computer has to project as a human in a conversation and fool humans into believing this.


  • The year 1950 to 1990 witnessed a number of developments in machine learning like self-learning games and computers that replicate human thought process.


  • 1990’s were the years when an actual shift in our approach happened, we shifted from a knowledge-driven approach towards machine learning to a data-driven approach. Here the computers were programmed to analyse large chunks of data and learn from it.


  • In 1997, IBM’s machine learning software Deep Blue beats a world chess champion in the game.


These were few early years of machine learning, as more and more tech giants started seeing scope in it, they were on the toes to integrate it into their systems and that’s how now we find its application everywhere.


Companies like Pinterest, Facebook and Twitter use machine learning to enable better curation of the content for users.


Video streaming website Netflix uses Machine learning to understand the kind of content a particular user is consuming and then recommend them similar content to watch.


Uber (a Taxi App) uses machine learning to analyse from millions of travel records and give you an ETA as accurate as possible.


It is no doubt that the firms are leaving no stone unturned when it’s about making user-experience better with machine learning.


As the scope of machine learning is no longer limited to tech giants alone, institutions like banks and stockbroking firms are using it for achieving accuracy in risk analysis.


Something as crucial as detecting lung cancer symptoms from X-ray imagery is being done with the help of machine learning algorithms.


How it’s Different from Statistical Modelling?


machine learning vs statistical modelling


When discussing machine learning, it is natural to think about how it is different from other statistical computer programmes that are coded to function in a particular manner.


Here we should understand that though the objective of both of these programmes might be similar but the difference is in the fundamentals.


The statistical modelling works on the pre-notified information about the relationship between the different variables in a given dataset.


Whereas, in machine learning, we don’t need any prior assumption or rule-based programming to find patterns in the given dataset.


For e.g., while predicting the possibility of an earthquake on a particular landmass, a statistical method will start with a hypothesis and prove it true or false with the help of previous statistics.


On the other hand, machine learning does the job finding patterns rather than simply holding on to a hypothesis, this will not only help in predictions of future earthquakes but would uncover various other patterns that cause certain changes in the land mass.


Types of Machine Learning


Types of Machine Learning


Supervised and Unsupervised are the two basic types of machine learning processes, here’s how are they different:


Supervised Learning

As the name goes, this is when the machine is trained with the pre-classified training data and with its help, a function is developed to identify the new examples.


Let’s simplify this with an example, if we want a computer to classify a basket of fruits correctly, we will first train/feed info about how each of them looks like and their physical characteristics.


With this data, the computer can further identify the new sets of apples and bananas that we give it. Therefore, classification is one of the major uses of supervised learning.


Unsupervised Learning

It is an approach which tries to find the hidden patterns and structure in the unlabelled data.


For e.g., this time we don’t tell the computer characteristic of each fruit rather the computer analyses every fruit finds some similar characteristics in them and classifies them accordingly.


Clustering is one of the major uses of this type of learning.


What’s the Deal with Deep Learning and Artifical Intelligence?


Deep Learning and Artificial Intelligence


Deep learning is a subset of machine learning which is nothing but various machine learning algorithms working on multiple layers.


Deep learning programmes have contributed towards solving many complex problems and have bagged the record for accurate results.


The deep learning programmes are majorly being used for functions like image recognition, sound recognition and recommender systems.


Therefore Deep Learning algorithms are applied for a bigger chunk of data with more variables as compared to machine learning which is crafted for a controlled amount of data and lesser variables.


Artificial Intelligence being any intelligence applied by machines to find viable solutions to a given problem, encapsulates in itself machine learning as well as deep learning.


It uses them in various ways to function successfully and get accurate results.


For e.g., Deep learning could be used in a program that automatically analyses the picture and generates captions for it, whereas, Artificial intelligence can further search an image on the basis of its contents.


The Scope


Machine Learning Professional Salary


With more and more firms employing machine learning to get their job done, the scope of machine learning only keeps on increasing with the coming days.


It is already shaping futures and careers of millions, it’s about the time when it would become a norm for IT professionals and engineers.

Even professionals from non-technical backgrounds could get acquainted with machine learning skills and explore new possibilities of applying them in their respective domains.


A recent report says machine learning would pave the way for about 2.3 million opportunities by the year 2020.


There wouldn’t be any safer bet than adding machine learning programming skills to your portfolio if you are seeking a career in the industry and pursuing a certified machine learning course can help in taking the precise step in the ML world.

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