Machine Learning is one of the most popular sub-fields of Artificial Intelligence. Machine learning concepts are used almost everywhere, such as Healthcare, Finance, Infrastructure, Marketing, Self-driving cars, recommendation systems, chatbots, social sites, gaming, cyber security, and many more.
Lets see some of the common mistakes people make as beginners while learning ML..
1) Not Understanding the Basics of ML:
Beginners tend too early to jump into the codes and libraries instead of learning the theory models ,Mathematics and statistics behind it. It is really essential to have basic theoretical knowledge at least.
2) Starting with Bad Data:
Data is the crucial part of ML. Learning algorithms and concepts of ML is just not enough. The data must be good and refined. It is better to concentrate a little more on data and its features, because the quality of your final project will completely depend on the data than your model algorithms.
3) Not Too Much of Theory:
Yes, its good to have basic understanding of theory before jumping deep into ML. But a lot of students spend too much time on theory, and not enough time practicing that theory. So learn concepts and keep practicing them practically.
4) No Proper Learning Plan:
Improper learning path is again a major problem. When you're learning anything new, you need a proper structure/plan of how you're going to learn. Without a proper learning plan you wont remember half of things that you learned and feel disgusted.
5) Giving Up Too Soon:
Machine learning and Data science have a continuous learning path and not everyone is able to stick to it. Even the people who are "experts" now faced problems when they started it. So don't quit learning and exploring as there is always a beautiful success awaiting for you at the end.
That's it for todays blog. I hope you have liked it. Please give an upvote and Happy exploring..
