How to Learn Machine Learning ?
Machine Learning is an ever-changing and fast-evolving field. It takes an incredible amount of work and study to get with the grips of it and master it.
There are certain basic and fundamental prerequisites of machine learning that learners need to have an in-depth or at least a decent amount of knowledge of, from the offset of the learning process. Concepts that require prerequisite knowledge of are Linear Algebra and Calculus. Then, learners can move on to learning Statistics. They should start off by a basic level and then dive deeper and further into it, finishing eventually at the advanced level.
Taking up every single online course that involves data science which further includes basic Machine Learning, linear algebra, statistics, visualization, deep learning, etc is a significant step in the process. Constant and compulsive reading of blogs, classic and model texts, journal articles are also extremely helpful and instrumental. Accurate and almost having an eidetic memory of derivations as well as sincerely and actively researching any foreign concept that the learner might come across is also important.
The surge and pursuit of the individual and the level to which he/she is intrigued are what determines the shaping up of the whole process itself. Learning and relearning of even the simplest of the concepts and understanding them with different perspectives is also a vital part of the process. To be a quick and effective programmer is very crucial for Machine Learning.
Staying updated with new tools, packages and developments, in general, is also important. One should participate routinely and regularly in Kaggle competitions as they can serve as a kind of self-introspection mechanism which can also help them in being aware of their weaknesses and irregularities.
Lastly, learners’ relentlessness and sincerity meddled with captivating and intriguing themselves while being true to their skills and abilities can be the firmest of the steps in the process of Machine Learning.