Machine Learning is a branch of Artificial Intelligence that gives a machine the ability to automatically learn and improve from an experience, rather than being explicitly programmed. ML is amongst the hottest career choices in 2020. Companies like Google, Amazon, Microsoft, Oracle and many others are making a transition towards Machine Learning and Artificial Intelligence.
There are various career options in the field of Machine Learning such as Machine Learning Engineer, Machine Learning Analyst, NLP Data Scientist, Data Sciences Lead, and Machine Learning Scientist.
To help the aspiring Machine Learning enthusiasts, here are some tips which will help you build a career in Machine Learning. So, without any further ado, let us jump on to the prerequisites of Machine Learning.
Understanding the Prerequisites
The realm of machine learning comes with its own sets of requirements as well as qualifications. A career in machine learning is a steep one and requires you to have basic skill sets to start your journey into the industry.
To advance your career in Machine Learning, you must have exposure to the following skills:
- Stats and probability
- Algorithms
- Applied mathematics
- Hands-on experience in programming languages like Python, C++, Java or R.
- And, Distributed Computing
To kick start your career, start working on the skills you lack. Get a book on probability, brush up your coding skills, and work on your weaker areas. Along with that, you can also take up a substantial course on artificial intelligence and machine learning.
Take online courses
To advance your career in machine learning, you have to broaden your skill-set as much as possible. And as a heads up, you can start with the online courses and then to hone your skills and then brush your knowledge, you can participate in various competitions in this field such as Kaggle, Analytics Vidhya
There is another popular approach available, you can accelerate your learning with the help of bootcamps
Practice as much as possible
If you want to master the Machine Learning skills, you need to practice on datasets. You must know the process of working on a problem end-to-end. Learn to map that process onto a tool and practice the process on a dataset in a targeted way.
Work on real-world machine learning problems to see how it can actually be used in fields like education, science, technology, and medicine. You can take the machine learning problems from Kaggle.com right now to build your machine learning model.
Working on different projects will give you the required experience for a job as it will make your resume stand out. The certificate from your institution and the number of projects you have worked on matters a lot. Now that you have worked on building basic knowledge and are able to understand what you are learning, it is time to start working on some machine learning projects that can help you furnish your skills.
Try collaborating with your mates for projects to constantly upskill yourself as it will help in giving you practical exposure and which will help you in meeting industry requirements.
Build a machine learning portfolio
Designers and artists use a portfolio to showcase examples of prior work to prospective clients and employers. Your machine learning portfolio should be a compilation of your completed independent projects, each of which uses machine learning in some way. The portfolio should be accessible, small, completed, independent, and most importantly, understandable.
A set of your finished projects will offer a knowledge base for you to reflect on and leverage as you push into projects further from your comfort zones. You can have a code repository such as GitHub or BitBucket as well to list your projects.
Such sites encourage you to provide a readme file to clearly describe the purpose and findings for each project. You can even add images, graphs, videos, and links in your code repositories. Give instructions to download the project and recreate the results as well. Want people to re-run your work? Try making it as accessible and easy as possible.
Bottom line
Now that you have an idea of how to build a career in Machine Learning, it is time for you to pull up your socks. Know your strengths and work towards the weaknesses to stay ahead of your competitors. Enrol yourself in a Machine Learning course and build a strong foundation for a rewarding career in Machine Learning.
