The rise of artificial intelligence solutions has opened up new avenues for developers and engineers. Many are looking for careers in artificial intelligence and its related fields. While there is much scope for AI engineers, one cannot ignore the lucrative career option of machine learning. Many young graduates aspire to be machine learning developer. But that does not come easy to everyone. There are qualifications and certifications along with skills that merit you a status of a machine learning developer. This job or role requires developers to design machine learning applications, systems, and platforms. A machine learning developer assesses and organizes data, execute different tests and experiments, and optimizes the learning process with the aim of developing high-performing ML systems and applications.
Being a machine learning engineer, your work depends on ML algorithms for different codebases. ML engineers usually have experience working in the software development department, which acts as a bonus for securing a good salary package. In short, machine learning engineers possess a perfect blend of math, statistics, and web development. Having a solid foundation in these subjects and experience will bode well for landing good machine-learning jobs.
Machine learning focuses on the application of AI principles and solutions to learn and automate. It focuses on delivering results based on experience without being explicitly trained or programmed. ML relies heavily on a machine’s capability to learn over time with the availability of data. Machines learn, obtain information, and use it to further the learning process and deliver results for complex problems. Now, the real question is how to kickstart a career in ML or become a machine learning developer. What does it take to become an ML engineer? In this article, we will explain the basic skills and tips that a person may require to become a Machine Learning developer. Some of these skills and practices are listed below.
1. Learn Python
Proficiency in Python is essential to qualify for the role of a machine learning developer. You must have a working and theoretical knowledge of this programming language along with C++ and its associated libraries. These two are wildly popular programming languages in the world of machine learning and are very helpful for data scientists and ML engineers. You are required to know SQL and GitHub primarily for working with ML models and work tasks. In addition to Python, aspiring developers should be comfortable with
- • R
- • SQL
- • Java
- • Scala
- • Julia
- • MATLAB
2. Enroll in Machine Learning Courses and Certifications
Anybody aspiring to find a lucrative career in machine learning requires learning basic ML concepts and algorithms. It may not require extensive and laborious school training, but it does need online courses, certifications, or boot camps for some valuable experience and learning.
3. Work on a personal ML Project for Experience
To kickstart a career in machine learning, you need to get into the basics. For this purpose, you should work on your personal project by reviewing and recreating standard and basic ML projects. These are easily available through Scikit-learn, Awesome Machine Learning, and other resources. You need a solid grasp of ML projects in practice to qualify for a job. Moreover, you can also come up with your projects and list them on your resume for a good impression.
4. Learn the Art of Gathering the Right Data
Machine learning is all about working with data and building relationships based on previous experience and learning with data. Any ML software is incomplete without proper data collection. You must learn how to collect the right data for data-driven tasks.
5. Join Online ML Communities
Online machine learning communities are a great step forward in the world of ML developers. Such platforms enable aspiring graduates and candidates to find jobs, new vacancies, and data sets. You can also communicate with others and learn from their experience in the field.
To sum it up,
Since it is a relatively new concept, not everybody is well-versed in the actual value and benefits of machine learning. Machine learning is more than just science or a subset of artificial intelligence. Different ML techniques can solve complicated issues pertaining to business and personal life. The capability and human-like qualities of a computer system can be useful for face recognition, data analytics, and forecasting. ML algorithms are so powerful that they can potentially solve any issue subject to the availability of sufficient data. All you need is a good mindset, thirst for knowledge, passion for ML, and related skills.