The Importance of Machine Learning for Data Scientists

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Simon Tavasoli

Published on December 24, 2016


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What is Machine Learning?

Simply put, Machine Learning is the core subarea of artificial intelligence. It makes computers get into a self-learning mode without explicit programming. When fed new data, these computers learn, grow, change, and develop by themselves.

The concept of machine learning has been around for a while now. However, the ability to automatically and quickly apply mathematical calculations to big data is now gaining a bit of momentum.

Machine learning has been used in a number of places like the self-driving Google car, the online recommendation engines – friend recommendations on Facebook, offer recommendations from Amazon, and in cyber fraud detection.

Why Machine Learning?

The machine learning field is constantly evolving. And along with evolution comes a rise in the demand and importance. There is one crucial reason why data scientists need machine learning, and that is: ‘High-value predictions that can guide better decisions and smart actions in real time without human intervention’.

Machine learning as a technology helps analyse large chunks of data, easing the tasks of data scientists in an automated process and is gaining a lot of prominence and recognition. Machine learning has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced the traditional statistical techniques.

So, how drastically is machine learning revolutionizing the data analysis avenue?

Data analysis has traditionally been characterized by the trial and error approach – one that become impossible to use when there are large and heterogeneous data sets in question. It is for this very reason that big data was criticized for being overhyped. Availability of more data is directly proportional to the difficulty of bringing in new predictive models that work accurately. Traditional statistical solutions are more focused on static analysis that is limited to the analysis of samples that are frozen in time. Obviously enough, this could result in unreliable and inaccurate conclusions.

Coming as a solution to all this chaos is Machine Learning proposing clever alternatives to analysing huge volumes of data. It a leap forward from the computer science, the statistics, and other emerging applications in the industry. Machine learning is able to produce accurate results and analysis by developing efficient and fast algorithms and data-driven models for real-time processing of this data.

How will data science evolve with the rising popularity of machine learning in industry?

Machine learning and data science can work hand in hand. Take into consideration the definition of machine learning – the ability of a machine to generalize knowledge from data. Without data there is very little that machines can learn. If anything, the increase in usage of machine learning in many industries will act as a catalyst to push data science to increasing relevance. Machine learning is only as good as the data it is given and the ability of algorithms to consume it. Going forward, basic levels of machine learning will become a standard requirement for data scientists.

This being said, one of the most relevant data science skills is the ability to evaluate machine learning. In data science, there is no shortage of the cool stuff to do r the shiny new algorithms to throw at data. However, what it does lack is why things work and how to solve non-standard problems. Which is where machine learning will come into play.

Simplilearn’s certification training

With Machine Learning being such a craze, it is important for data scientists to learn it. Which is why Simplilearn has introduced a revolutionary Machine Learning certification program that provides an advanced-level training on the applications and algorithms it uses.

This training will give you a hands-on experience in multiple, highly sought-after machine learning skills in both supervised and unsupervised learning. Our unique case study approach ensures that you are working with data as you learn.

With 28 hours of instructor-led training and 2 industry projects in virtual labs, this training program is everything you need to become a machine learning expert. So get out there. It’s you time to get certified and take on the world. 

About the Author

Simon Tavasoli is a Business Analytics Lead with more than 12 years of hands-on and leadership experience in various industries. He has led the development of many analytic projects that drive product and marketing initiatives. He has more than 10 years of experience teaching Data Science, Data Visualization, Predictive Analytics, and Statistics.


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