Techjury statistics show that people created 1.7 MB data per second in 2020, according to Techjury statistics. The entire digital universe occupied 44 zettabytes at the end of 2020. This has since increased substantially.
Machine learning is useful in analyzing and generating meaningful insights from all this data. This data is the foundation for a machine-learning engineer’s job.
Continue reading to learn how you can become a machine-learning engineer and get into this exciting career.
r eight-step guide. We will also introduce you to Coding Dojo Data Science Bootcamp which will give you the practical skills to use machine-learning algorithms in Python.
Let’s first define machine learning.
What is Machine Learning?
Machine learning (ML), a subset AI, focuses on understanding and building methods that use data to optimize tasks. ML algorithms are used to predict (or infer new output values) using historical data.
Machine learning engineers design, build, and maintain self-running AI systems that automate predictive modeling.
What does a Machine Learning Engineer do?
Although similar to data scientists, ML engineers are focused on building and managing predictive models and AI systems. Data scientists, on the other hand, are skilled in generating meaningful insights from large amounts of data.
These are the main responsibilities of ML engineers.
Collaborate with the data science team, including data scientists, engineers, architects, and administrators, as well as external stakeholders.
Analyze, organize, and evaluate large amounts of data.
Perform tests to optimize ML models and algorithms; train and retrain these system as needed.

8 Steps to Become a Machine Learning Engineer
1. Learn the Machine Learning Basics
To become a Machine Learning Engineer, you will need to have a lot of knowledge. It is important to start with the basics to understand machine learning.
You don’t need to have any experience in Machine Learning. Learn the basics first.
Machine learning books
Listen to podcasts about machine learning
Machine learning tutorials

2. Register for a Machine Learning Bootcamp
Machine learning bootcamps are short-term, intensive programs that provide students with the data science, programming, and machine learning skills they need in order to become ML engineers.
If you are looking to learn the basics of data science and machine-learning with Python, the Coding Dojo Data Science Bootcamp will be a great option.
3. Learn Essential Programming Languages
You will need to be proficient in a variety of programming languages to become a Machine Learning Engineer. Machine Learning Engineers are most familiar with C++ and Python.
These are some other programming languages you might consider learning to make a career in machine learning.

4. Complete Machine Learning Projects
You will gain a solid understanding of machine learning by creating your own projects. It is a great way to learn machine-learning and to work on projects. You can add the completed projects to your coding portfolio, or list them on your resume.
Start by creating basic projects such as those on Awesome Machine Learning or PredictionIO. Data sets can be found on Quandl, UCI Machine Learning Repository and similar resources.
5. Join Online Machine Learning Communities
Joining online machine-learning communities will allow you to connect with experts and learn from them. Kaggle is a great place for beginners. It hosts members from the data science and ML communities. This resource contains other useful online ML communities.
6. Make a portfolio of Machine Learning Engineers
A machine learning portfolio is essential for all ML engineers to be considered in the job market. These are also known as coding portfolios and can be used to showcase your best coding projects. You could also use GitHub to host your portfolio.
This portfolio is a great example for a machine-learning portfolio.
7. Create a Resume for a Machine Learning Engineer
A strong machine learning resume is another key component in your journey to becoming a bonafide ML engineer. Add your contact details and links to your ML profile or GitHub profile.
8. Apply for a Machine Learning Internship, or Job
Bootcamps and student projects can be great for learning the basics of machine learning. However, applying for an internship or entry level job is the best way to expand your skill set, gain work experience, and learn about the business side. To get started, check out reputable job websites and word-of mouth referrals.