The Common Misconceptions About Machine Learning

The Common Misconceptions About Machine Learning

There is a hype train going on about ML (Machine Learning), and many beginners are getting to be the victims of this hype train as they are getting in for the wrong reasons. Your professor will explain how getting a Ph.D. is necessary if you want to get better or your peers are telling you how to get a better GPU and IDE (Integrated Development Environment). When you started to learn from the online courses, you realized you needed a bigger dataset and proficiency in Python. After learning the required skills when you applied for a job, you realized that you need more than a few courses or certifications to make it. In the end, after getting the job, you realized that it is demanding work, and sometimes these jobs don’t pay well at the initial stages.

This article will help you get through these disappointments and prepare you to face these problems. We will be learning a lot about the real-life problem faced by a beginner getting into the machine learning field.

There is clear empirical evidence that you don’t need lots of math, you don’t need lots of data, and you don’t need lots of expensive computers. — Jeremy Howard (Practical Deep Learning for Coders)

 

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