When beginning in a new domain it is typical to feel like you’re drowning. 

You might not be confident or have the feeling that you are not adequate or that you are lacking in some prerequisite. 

You will look into these issues in this blog article and come to learn that such emotions can cause actions that can take up a lot of time and resources and leave you feeling dejected with yourself. 

You will come to know that there are several paths through the domain of machine learning and that much like programming, it is a meritocracy. 

Dangerous Thinking 

Having the feeling that you are not adequate or that you do not possess some ability that you think you must have prior to making a beginning within machine learning is a dangerous road. 

It is dangerous as it can lead you down roads that take up a lot of your time resources, money resources and that might not be needed. For instance, you might feel like you require to have a grounding in mathematics or in computer science or some other topic that you don’t really have a passion for. You might decide that you merely must: 

• Obtain a degree: An undergraduate or postgraduate degree providing you a formal education within machine learning which includes any prerequisites that have been given definition to by the institution. 
• Enlist in a math course: A course, free, or otherwise that will teach you linear algebra or calculus.  
• Read a textbook: A post-graduate level textbook on machine learning that presupposes a level of training beforehand in the domains of probability theory and linear algebra. 

The risk is that you feel like you require to accomplish some minimal level of skill level prior to getting started. 

You push away beginning in machine learning to begin learning that skill you believe is required. It is tough. Really tough. 

As it is difficult and you don’t have the passion for it, you are more probable to throw in the towel, implying you continue to deny yourself the permission to begin in machine learning. 

This road can function for some, but it’s very difficult, filled with blood, sweat, and tears. However, as the saying goes, no pain, no gain. 

It does not have to be so difficult. 

Several Paths 

There are several paths through the domain of machine learning. 

There is a location for the credential, the mathematics course, and the textbook, and they could be further along the path for you, or on a different path. 

Machine learning is a multidisciplinary domain, implying that you have individuals coming to it with across backgrounds across all domains of science and engineering. 

It also implies that there is no archetype for the “machine learning practitioner”, even though we do believe programmers have an amazing opportunity in the domain. 

It is a comparatively new domain and a lot of the documentation is in the form of research papers and textbooks generated by academics. As such this sets the tone for the perception of the field as highly academic. This is the purpose why there is a concentration on theory over application and the perceived requirement for training needed by academics such as research strategies and better educational credentials. 

The technicians path is applied and to begin with experimentation and processes, and perhaps some programming. Be confident with this strategy, it holds good, is effective and is a pathway followed by countless fellow programmers. Be aware of your restrictions and play to your strengths and the skills you already possess. 

Meritocracy 

Like the domain of software development, the application of machine learning is a meritocracy. A meritocracy is a structure under which participants are valued on the basis of their contributions or demonstrated accomplishment (merit). 

Business, clients, and employers are bothered about your credentials, but just as much as the outcomes you have illustrated you can accomplish and that you can accomplish for them. Degrees, other awards, and being employed for Fortune 500 companies are symbols that can be leveraged by others to short cut this determination, but that is all. 

As a meritocracy, you must illustrate that you possess merit. If you are seeking for your skills to be lauded by others, then you must demonstrate and promote it. You can do that by taking part in projects, competitions and finishing small open projects and leveraging outputs from such efforts as adverts of your capability to your own self and others. 

In this blog post, you came to know that feeling buried is a natural feeling when beginning a new  technical discipline. You learned that these emotions of inadequacy can cause hazardous thoughts like expenditure of large time and monetary resources going after a degree or education you believe you need to have prior to getting started.  

You learned that there are several pathways through the domain of machine learning and that the empirical path of the programmer as the technician is valued. You also came to know that machine learning much like programming is a meritocracy and that if you persist and do a good job, it will be lauded and you will receive recognition.