Under the aegis of phrases like Big data and IoT, there exists extremely important aspects which form the pillars of such technological development. Machine learning and data mining are such pillars which hold the data science together in its rightful place. Hence, if you have the slightest interest in data science, you need to keep yourself updated about the advancements in these two fields.
Especially, for data scientists, greater accuracy is a must with flexibility in terms of approach. Since the algorithms are constantly getting updated in terms of methods and approaches, the new developments are always worth noting. If you investigate regarding the common topics in machine learning today, you get a clear hint of the relevant areas that you need to research upon.
Machine learning topics
If you are looking into the domain of machine learning, then there are quite a few journals and books on machine learning to look into. However, there are certain topics that are discussed intensely by all contemporary books on this topic. Among them, support vector machine and neural networks are the two most talked-about topics in this regard.
However, certain topics that were on a high recently have surely taken a dip such as genetic algorithms which came through because of the humongous amount of research that was being persuaded in the recent past. Its time consuming nature has somehow pushed it to the backseat and a remedy is being sought for solving its real time optimization problems.
Why support vector machines?
Of course, there must be a factor that works in favour of specific algorithms. Support vector machines, for example, have been readily available to solve quite a few real world problems and it gets enriched by the knowledge produced by solving these problems. So, there is a positive feedback involved in this process. Pattern recognition, one of the most important aspects of today’s technology, is also a method benefitted from the quick processing of this technology.
While support vector machines do contribute to the growth of artificial intelligence, the true breakthrough has been coming through neural networks on which artificial intelligence is heavily dependent upon. In fact, neural network has had a direct impact on all practical problems related to machine learning and data mining, with particular stress on deep learning which has evolved precisely because of neural networks.
Powered by neural networks, deep learning has been investing in various scenarios beyond pattern recognition, although this is precisely the most frequently used tool. However, there are other fields being invaded by these methods, namely the sector of big data, cloud computing and social media which are essentially interrelated. In fact, big data is surely one of the chartbusters when it comes to IT technology which has fuelled the necessity for machine learning to pervade the scene.
Trends and their transience
Of course, these two topics are the hottest favorite among data scientists now. But, like fashion, data science also has its trends and they shift rapidly on the kind of research being pursued. So, it is important to observe the trend and train accordingly if you aspire to be a data scientist.
If you observe the frequency of topics that appear on fields like artificial intelligence, data mining etc. you get to know what kind of necessity does the IT industry face right now and then you can align yourself accordingly. In this age, being a specialist hardly matters because your special knowledge will soon be outdated. Hence, try to adapt to various planes of knowledge and maintain a balance. This is probably the best survival strategy for data scientists.