Now that you are in this page, it's understood that you want to prepare yourself for Machine Learning.
Many experts may suggest you that you need to master MATHEMATICS before you dive into machine learning. You might have heard that for Machine Learning(ML) you must know
This is enough to PARALYZE a beginner's motivation. if you are not good at Math, I have a good news for you. Practically in order to get started and deploy machine learning models (in contradiction to doing machine learning theory), you need very much less math than you think.
BUT you are not entirely out of the math-zone.
In an academic environment, individuals are recognized for producing novel research, and in the context of ML, that truly does require a deep understanding of the mathematics that underlies machine learning and statistics.
In industry though, in most cases, the primary rewards aren’t for innovation and research. In industry, you’re appreciated for creating business value. In most cases, particularly at entry levels, this means applying existing, “off the shelf” tools. The critical fact here, is that existing tools almost all take care of the math for you.
Most data scientists spend a huge amount of their time getting data, cleaning data, and exploring data. It particularly applies to beginners.
Hence, It's Data Analysis that you need to learn to get things done. 90% of your work will be Data Preparation, Data Visualization. Data Preparation means prepare a dataset to make Machine Learning algorithms work in a practical environment.
Language that Industry Understands:-
To achieve good results in initial period of being a Machine Learning / Data Scientist Engineer you need to know how to gather data, explore it, and prepare it. That means you should be able to perform data analysis and visualize data for the ease of implementation.
If you master data analysis, you’ll be well prepared to start building machine learning models that work.
Now Understand that:
And also Understand that:
"In industry, math is also important for a small subset of more advanced data scientists. There are people in industry at high levels who are also using advanced math on a regular basis. In particular, there are people at companies like Google and Facebook who are pushing the boundaries of machine learning – people working on bleeding edge tools. These people almost certainly employ calculus, linear algebra, and more advanced math routinely in their work." Academics and theorists for developing the techniques that we use on a daily basis.
From an Academician's angel of view, follow the following Topics, which will help you go with the Course smoothly: (All topics are required as per Intermediate / +2 / 12th Std. level)
But Do Not Worry, Even If you fail to brush up the above topics, you will learn by the course of action.
- Deepak Kumar Pradhan