How do you balance Machine Learning theory and practice?

Machine Learning Certification

Machine learning is no longer a technology from the future. The technology giants like Google, Facebook, Netflix, etc. have been using machine learning to improve their user experience for a very long time. Now, the applications of machine learning are growing across the industries and this technology is driving businesses worth billions of dollars. Along with the applications,  the demand for professionals with expertise in ML has grown immensely in the past few years.
So, it is indeed a good time to learn machine learning for better career prospects. A machine learning course is the best practical way to start your learning process. However, often people get too much stuck to the theory and fall behind in the practical experience. Well, it is not the best way to learn anything. This article will help you balance learning machine learning theory and practice. Read on to find out more.
Theory vs Practice 
For practitioners of ML, the theory and practice are complementary aspects of their career. To become successful in this field, you will have to strike the balance between what you read and the problems in real life. So many people avoid building things because it is hard. Building involves constant tracing of bugs, endlessly traversing stack overflow, attempts to bring so many parts together and so many more work. Theory on the other hands is comparatively easy.
You can find all the concepts settled in place and we can just consume everything as to how we wish things will work. But if it doesn't feel hard, you are not learning anything properly. It will be a lot easier for us to rip through journals and understand the concepts, but reading about the achievements of others will not make you any better in this field. You have to build what you read and fail so many times to get an understanding that cannot be achieved by reading alone.
Build what you read
It is the one simple thing you can do to strike a balance between theory and practice. Build a neural network. It may perform poorly, but you will learn how different it is from the journals. Attend a Kaggle competition and let your ranking stare at you even if it is low. Hack together a javascript application to run your ML algorithms in the back end only just to see it fail for unknown reasons.
Always do projects. Your machine learning certification program might have projects as part of their curriculum, but don't be limited to those. Just remember that everything you make during the learning process does not have to work. Even the failures are great teachers in this process. They will provide you with the practical experience you will need to excel in the industry.
Practicing everything you read may make it harder for you, but once you learn to volley theory and practice back and forth, you will certainly get the results better than you were looking for. Only such a balanced approach towards ML will help you make an effect on the real world problems.

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