Machine learning is a flourishing field in the data world, which attracts new engineers every year. Just like any other specialization, the best way to become a junior ML specialist is to learn the fundamentals and invest your time in several sample projects for your portfolio. Young professionals frequently struggle to come up with their first projects, that is why I wish to share the top 10 ideas for your ML project, which can be useful in this case.. And you may want to consider the following six steps to become a machine learning engineer.
- Learn the idea of ML
It is hard to progress in a field that you know nothing about. Commit to the full-scale research of what machine learning is and what applications it finds in the modern world. At this stage, you can visualize your findings on a map. Make this map illustrate all elements of machine learning from industries of application to the math behind it.
- Be excited about the next lesson
Artificial intelligence is very dynamic and innovative. Try to follow the latest machine learning agenda in different industries to monitor the most in-demand ML and DL projects and necessary skills. It will help you decide which of them you feel most curious about and build a solid portfolio for the specific area.
- Find a problem, think about the relevant solution, and translate it into ML language
Once you are familiar with machine learning and its application across industries, you can try to define a business problem on your own. Think of any difficulties that contemporary businesses face every day, reveal the mathematics behind them, and propose a relevant ML tool to solve them. If the B2B and B2C problems do not attract you, you can take a look at the machine learning application in social sciences, biotechnologies, earth sciences, and more.
- Don’t forget about the soft skills
As an ML engineer, you will more likely need to work in an advanced ML team. Get ready to be open to different approaches, perspectives, and opinions. Teamwork can be a burden for some, but being curious and excited about new learning opportunities is the key to success in machine learning.
- Take a data analysis course
Data analysts easily move to the machine learning industry, because they know how to pick the right data for the problem, analyze it, and visualize the results. Learning data analysis will help you focus on both the technical side and interpretation of the results to make them clear and meaningful for any stakeholders.
- Learn a programming language, preferably Python
Python is very popular among machine learning specialists, and it is worth learning. Try to approach Python and its most frequently-used machine learning libraries, such as Scikit-learn and Tensor, and this will help you understand the basics as well as link them to the hands-on experience.
This guide offers the most universal approach to starting a new ML career. You can always have access to a variety of open educational platforms, such as Kaggle, to get the best training from top professionals in machine learning. Use these free resources to the fullest because the industries need you!