What Are the Common Misconceptions About Machine Learning?
There is a hype train going on about ML (Machine Learning) and most of the beginners are getting victims of this hype 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 at machine learning or your peers are telling you how to get better GPU and IDE (Integrated Development Environment) will help you get better results. As you start to learning from the online course you realize that you need a bigger dataset and expertise in Python. After learning the required skills when you apply for a job you realize that you need more than a few courses or certifications to make it. In the end, after getting the job, you realize that it is demanding work and sometimes these jobs don’t pay well at the initial stages.
This article will help you get through those 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)
