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How to Become a Machine Learning Engineer?

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Written by Rajesh Tamada

Machine learning engineers design machine learning systems and applications. It includes assessing data, performing tests, and monitoring and optimizing learning processes for developing strong performing machine learning systems.

Qualifications required to become a Machine Learning engineer

Experience in software development is a great plus point to becoming a machine learning engineer as you’ll be required to apply algorithms to different codebases. Machine learning requires you to have a background in web development, math and statistics to easily understand the subjects and apply for machine learning jobs. If you don’t have the relevant experience, you can still go for this field but will have to start by understanding basic ML tools and methods to implement and optimize machine learning algorithms.

Steps to becoming a Machine Learning engineer

Here are six steps to becoming a machine learning engineer.

1. Learn coding with Python

To become a Machine Learning Engineer, you need to be proficient in C++  and/or Python and their associated libraries. These two are the most widely used languages by machine learning engineers and data scientists. You should also be comfortable with Github and SQL to be able to work in collaboration with a team. Understanding Google’s TensorFlow software library can help you write in Python, Java, C++, and Swift. It can be used for a huge range of deep-learning tasks like image and speech recognition. Beginners should get familiarized with PyTorch which is a framework that enables developers and ML engineers to use standard Python statements.

2. Get yourself enrolled in a Machine Learning course

Machine Learning engineer is one of the most high-paying jobs you can get without a formal and extensive degree. However, it can be difficult to break in without having a basic understanding of the concepts. Get yourself enrolled in a good Machine Learning course to get a comprehensive understanding of the subjects and learn how to apply ML algorithms to real-world problems.

3. Try a personal ML project

When starting out, it can be really helpful to practice by reviewing and recreating basic projects. Practicing these projects can help you better understand how machine learning works. After this, you can also come up with your own projects to share.

4. Learn how to collect the right data

AI can effectively process huge amounts of data. When thinking of creating AI software, consider the tasks requiring data points such as marketing and customer service and work on software that makes tasks involving heavy data easy and fast.

5. Join online communities for Machine Learning

Joining communities can be incredibly helpful when you’re trying to gain a deeper understanding of machine learning. Kaggle, an online community for machine learners and data scientists allows users to search and publish data sets, communicate with other ML engineers, build models, and more. It also hosts a number of ML challenges including official competitions with cash prizes.

6. Apply for Machine Learning internships and jobs

While competitions and personal projects can help you gain a better understanding, they may not be enough to get you a good job. Apply for ML internships or entry-level jobs to gain business-specific skills.