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Intricacies of Machine Learning in Data Science

November 29, 2018

Careers Employment

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Machine learning served as APIs

Machine learning is no longer just for geeks. Nowadays, any programmer can call some APIs and include it as part of their work. With Amazon cloud, with Google Cloud Platforms (GCP) and many more such platforms, in the coming days and years we can easily see that machine learning models will now be offered to you in API forms. So, all you have to do is work on your data, clean it and make it in a format that can finally be fed into a machine learning algorithm that is nothing more than an API. So, it becomes plug and play. You plug the data into an API call, the API goes back into the computing machines, it comes back with the predictive results, and then you take an action based on that.

Machine learning – some use cases

Things like face recognition, speech recognition, identifying a file being a virus, or to predict what is going to be the weather today and tomorrow, all of these uses are possible in this mechanism. But obviously, there is somebody who has done a lot of work to make sure these APIs are made available. If we, for instance, take face recognition, there has been a plenty of work in the area of image processing that wherein you take an image, train your model on the image, and then finally being able to come out with a very generalized model which can work on some new sort of data which is going to come in the future and which you have not used for training your model. And that typically is how machine learning models are built.

The case of antivirus software

All your antivirus software, typically the case of identifying a file to be malicious or good, benign or safe files out there and most of the anti viruses have now moved from a static signature based identification of viruses to a dynamic machine learning based detection to identify viruses. So, increasingly when you use antivirus software you know that most of the antivirus software gives you updates and these updates in the earlier days used to be on signature of the viruses. But nowadays these signatures are converted into machine learning models. And when there is an update for a new virus, you need to retrain completely the model which you had already had. You need to retrain your mode to learn that this is a new virus in the market and your machine. How machine learning is able to do that is that every single malware or virus file has certain traits associated with it. For instance, a trojan might come to your machine, the first thing it does is create a hidden folder. The second thing it does is copy some dlls. The moment a malicious program starts to take some action on your machine, it leaves its traces and this helps in getting to them.

Now that you have seen the importance of machine learning in Data Science, you may want to learn more about it and other areas of Data Science, which continues to be the most sought after skill set in the market.

Why Is Data Science Certification Crucial for You?

November 29, 2018

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Data Science happens to be one of the highly sought after skills in today’s job market. This is reinforced by the seemingly unstoppable demand for these professionals. However, prior to you taking the plunge in getting the certification, it is imperative to know all the aspects of the landscape around it.

What are the Data Science Components?

Let us now spend some quality time in trying to uncover the intricacies of a sampling of the terms you usually hear related to Data Science. Some of the general terms you may have come around are Visualization, Statistics, Deep Learning and, Machine Learning. These terms happen to form the pillars of its components. These are also the major areas when we consider the various parts of Data Science. The individuals who form part of teams of Data Science are really expected to be experts in Statistics. Statistics forms one of the chief skill sets. Visualization also forms a big part of the required skill set. Machine Learning is not where everyone works on a Data Science team. This area is specially occupied by individuals who have a background in computer science and to top it off, they have the ability to break problems down into crisper forms.

Machine learning as it relates to Data Science

As far as Machine Learning goes, the crucial part of reaching a final solution is to ensure that the problem is made as precise as feasible. Once you can achieve that, the final solution to the given problem is very much doable or can be achieved using various methodologies. Given that there are a lot of tools centered approaches available nowadays, programming languages of the nature of R/Python along with many other exclusive tools like SAAS, Data Scientists are able to shape models of Machine Learning models very rapidly. In most of the cases, individuals typically lack the understanding of the methodologies. What these people lack is an understanding of the algorithms before using the tool. That is also an important factor in coming out with a solution successfully.

Another burning thing that is being spoken about for quite a while now in the industry is the topic of Deep Learning. Deep learning in effect is a part of Machine Learning. The really powerful thing that Deep Learning gives us is due to its very highly accurate models that it can build and that combined with its capability to work with data of higher dimensions that was not feasible with the earlier models of machine learning. Even though you are enabled to solve a problem in data science with high dimensions using machine learning, the very accuracy was really not at acceptable levels. Deep learning has been changing this very problem for us.

What are the Components of Data Science?

  • Statistics is the about presentation of numbers
  • Visualization is about visuals that help in communication.
  • Machine learning is about to study, exploration and construction of algorithms.
  • Deep Learning is an upcoming area.

This article has taken you through various aspects and components of Data Science. This will hopefully help you make the right decision about which part you want to focus on.