Machine Learning Courses


INTRODUCTION
Machine Learning- a branch of artificial intelligence and as the name would suggest, the process of teaching machines how to learn. You heard that! It is the mechanism by which we make computers and systems capable of taking decisions on their own using data as their basis of input. Computers use raw data, data science course syllabus process them using advanced algorithms, and accordingly learn, understand, and tune their performance to the desired levels of accuracy, so that a similar kind of decision can be taken in the future without any human intervention. Using the concepts and basics of big data and data analytics, machine learning has enabled automation of a lot of the core tasks in a wide variety of industries.          
With an estimated 5 billion gigabytes of data (~5000 petabytes ~5 exabytes) generated by the world on a daily basis has made it impossible to process and filter such colossal amounts of data. At least the thought of humans doing it is completely out of the scope. But with computers and systems having evolved into machines with monstrous processing capabilities, machine learning proves to be a life savior in conducting data analytics and also facilitate in making better, enhanced and informed decisions. The best part about machine learning is that it holds true for the saying- “The more the merrier”. The fact that more is the data supplied to the model better is the efficiency and precision of the output. As such, the vast amount of data that is generated on a daily basis is put to quality use instead of just getting wasted and discarded.  
  

CHALLENGES
With a countless number of advantages to the study of machine learning, let us take a look at the other side of the coin.

        DATA SIZE: Even after so many advancements in the computing power of machines, there are limitations to them also. Even the biggest of companies and businesses having large infrastructure and supercomputers at their disposal can only process close to 1-2% of the data that is generated every day; that is almost 50 petabytes of data can be processed in a single go. Even after being a ridiculous amount of data that is getting processed, the amount that is wasted is exponentially more. Even after using concepts of parallel and distributed programming can this figure be taken to a maximum of 5-6% only.

        VARIETY: One of the many aspects of big data is its variety which is a major cause of the generation of heterogeneous aspects of output which make absolutely no sense when compared with the problem and its expected solution. Therefore the challenge of segregating and filtering the unnecessary aspects of data before it can be sent for processing is a major headache.

        DATA VERACITY: The reliability of the data source and the accuracy of the data science courses in hyderabad play a pivotal part in generating outputs that are neutral and confer to the target audience in a neutral fashion. Discrepancies, abnormalities, redundancies, duplications, biases etc. are some of the aspects that should be kept in mind and eradicated completely in order to enhance the overall accuracy of the data and consequently the overall precision and also stick to the associativity and interrelations.

RESOURCE BOX
Being one of the hottest and the most in-demand industry of the present day world in terms of requirement of skilled people, the fields of data science and machine learning are one to ponder your future over. Join datascience courses in by 360DigiTMG: one of the best places to get certified in Bangalore.


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