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Showing posts from January, 2022

What’s the Difference Between Artificial Intelligence, Machine Learning and Deep Learning?

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  Often Artificial intelligence, Machine learning, and deep learning are often overlapping terms that get candidates confused. Artificial Intelligence means getting a computer to imitate human behavior.  Machine learning  on other hand is a subset of Artificial intelligence, and it consists of strategies that enables systems to figure things out from data and deliver applications. Meanwhile, Deep learning is a subset of  machine learning  that allows computers to solve highly complex problems. While these descriptions are accurate, they are little concise. So let us explore each of these segments and provide you with a little more background. Difference between AI, Machine Learning and Deep Learning What Is AI? Artificial Intelligence is an academic discipline that was established in 1956. The aim was to get processors to perform tasks regarded as uniquely human, aspects that required intelligence. In the beginning, researchers worked on issues like play...

Machine Learning a Great Career Pathway

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  While many advanced machine learning tools are hard to use and need a good deal of knowledge and sophistication in statistics, mathematics, and software engineering. As far as beginners are concerned one can opt for a machine learning coding bootcamp to gain wide knowledge and get accessibility to career opportunities. A variety of supervised and unsupervised learning models could be made use of using R and Python, which are freely available and could be straight away set up on the system and even simpler models like linear or logistic regression could be used for performing interesting and crucial machine learning tasks. If you are a fresher to machine learning, then having math and data analysis skills is essential. One needs to have skills to crunch data for deriving useful patterns and insights that are the foundation of machine learning. Some essential steps in data analysis are from loading large data sets, cleaning it for finding missing data, and slicing and dicing data ...