One of the most critical aspects of working in the AI field is implementing machine learning to make the AI adapt to its role over time. The primary issue for machine learning is the steep learning curve. When it comes to machine learning, the prospect of coding is a massive hurdle to get over.
Building a career takes time and effort but the path is often unclear. Thankfully, we have narrowed down the process to four distinct steps and got your future machine learning career kickstarted.
Build a Portfolio
When looking to start a career you have nothing to your name, you effectively don’t exist and you need to find a way to become more seen. The best way to do that is to get up and do it.
Start with learning your program of choice. There are many different choices to pick from in the AI field, but the most commonly chosen tool is Python. Use this or other programs like R or Java and build up a portfolio to present prospective companies with a plethora of work. The more work you have on file, the better you will look to employers.
There are a few reasons for Python being the go-to choice for coding. For starters, it’s the oldest with the initial model being conceived in the 1980s meaning it has had time to work out any kinks that would be problematic for it. Python is also generally better at handling larger amounts of data meaning you get better results the more you test the data. Though R is better for giving better visuals of the data and Java is good at getting results and processing quickly, choosing Python is the far easier and most likely most efficient choice.
Develop a Personal Project
When you have a sample size of information, it’s time for a big project to push yourself further into the coding world. The best start for this is to find something that you like and devise a plan to implement AI into said idea. Since it is a topic that you would be interested in, it gives you more of an incentive to work harder on it and make sure it is successful.
There are a few different easy topics for these projects, such as stock projections and movie recommendations based on past searches, but anything is on the table with AI. AI is such a broad but dense topic that you can genuinely do whatever you please. As long as you gain data and the program can learn from that data, the project would be worthwhile.
Practice Getting Proper Data
Now that you have completed your passion project, it’s time to make sure that everything is in order. The best way of doing this is to ensure that the data that you have collected is “correct“.
Now, even though all data is essentially good to see, there is data that would be preferable to others. For example, we never want to see a large amount of outlier data that can lead to a full restructuring of the project. The major tenements for good data that you want to follow are
These ensure that the data is not only of good quality but that the data is valuable for the project you are conducting.
Making sure that the data is giving you good signs and that the project is going well will make sure your projects are far more presentable and that you know what you’re doing. At the same time, don’t feel like having bad data is a bad thing, the most likely problem is that there are one or two errors that just need correcting in order to get the data you seek.
Apply to Machine Learning Jobs
Now that you have a sizable amount of work to your name it’s time for the best part. It is time to show your work to prospective employers. Using your hopefully large back catalog of work you can start to apply to machine learning jobs.
Looking for a job is always the hardest part, followed by the interview itself so optimizing your time is of the utmost importance. Try doing freelance work or more independent projects while searching for a job to extend that portfolio or even replace some work that would be considered lesser in comparison to your up-to-date work. Hopefully, through these means, an employer will see that work that gives you your dream job.