Here’s Why You Need Python Skills as a Machine Learning Engineer

Python is one of the most popular programming languages used in the field of machine learning. According to Kaggle’s annual survey of machine learning engineers, about 90% of respondents reported using Python in 2020.

Tech giants like Spotify, Amazon, and more rely heavily on Python to power their machine learning operations and build more effective products. Netflix uses Python to create and manage recommendation algorithms, personalization algorithms, and marketing algorithms. From robotics to machine learning, many of Google’s AI investments depend on Python as well.

If you want to learn how to apply Python programming skills in the context of AI applications, the UC San Diego Extension Machine Learning Engineering Bootcamp can help. Through hands-on, project-based learning, you’ll explore the key Python-based tools and libraries that comprise the machine learning engineering stack and use them to deploy machine learning systems to production.

Read on to find out more about how machine learning engineers use Python, and why the language dominates today’s machine learning landscape.

The Basics of Python

 
 
Python is an object-oriented programming language with a simple, concise syntax that prioritizes readability. In fact, Python’s syntax is based on that of the English language, meaning that English speakers may find Python to be more straightforward and accessible than other programming languages.

Python’s simplicity allows developers to swiftly implement changes to a program. Because the language does not require recompilation of source code, Python programmers enjoy a rapid edit-test-debug cycle and can quickly assess the results of their work.

Python is an open-source software that is free to download and use. Python documentation, educational resources, and general support are also widely available in the Python community, which includes beginners and experts alike. Python is also a highly scalable language, and can be used to code small and large-scale projects—including machine learning algorithms.

How Machine Learning Engineers Use Python

 
 
Python is used to implement machine learning models and systems. In the context of AI development, Python’s simplicity is a major plus. Its clarity and succinct structure allows machine learning engineers to focus on the content of ML problems over writing code, which speeds up development. With Python, machine learning engineers can quickly test algorithms prior to deployment.

Machine learning engineers also use a variety of Python frameworks and libraries, including:

• Matplotlib and Seaborn. Machine learning engineers frequently need to execute exploratory data analysis to evaluate which algorithm to apply to a data set. These Python libraries help machine learning engineers visualize and identify trends in data.
• Pandas. Machine learning engineers use this library for data manipulation and analysis. Data fuels machine learning, and every machine learning engineer must clean, process, and transform data in order to produce high-quality insights.
• Scikit-learn. This Python package helps machine learning engineers to implement supervised and unsupervised algorithms. Scikit-learn includes classification, clustering, and regression algorithms. Machine learning engineers also use this tool to score algorithms for functionality and split modelling data into testing and training sets.
• Keras and TensorFlow. Machine learning engineers use Keras and TensorFlow to build, train, and deploy machine learning models and deep neural networks.

Machine learning engineers rely on Python’s vast library ecosystem to manage and understand their data—and to deploy AI solutions in production.

Machine Learning Engineering Job Outlook

 
 
Demand is on the rise for both machine learning and Python programming skills across industries. Over the past year, employees at LinkedIn’s top-rated companies grew those skills by 23% and 25% respectively. In its 2021 Jobs on the Rise report, LinkedIn also listed Python as a key skill for AI practitioners.

From healthcare to e-commerce and finance, machine learning is driving growth in a diverse array of sectors—so much so that Indeed ranked artificial intelligence as the second most in-demand skill of 2021.

Companies rely on machine learning engineers to harness and leverage the power of big data to optimize their operations, reduce expenditures, and solve complicated business problems. During the coronavirus pandemic, businesses leaned into AI to meet rising consumer demands and insulate from future disruption—which resulted in a 32% increase in AI hiring from 2019 to 2020.

Today, there are 44,000+ available entry-level machine learning engineering jobs listed on LinkedIn. According to Indeed, the average base salary for a machine learning engineer is $140,278, and common benefits include stock options, a flexible schedule, commuter assistance, and unlimited paid time off.

Ready to launch your machine learning engineering career?

 
 
If you have prior experience in software engineering/data science or advanced knowledge of Python and want to pivot into machine learning engineering, the UC San Diego Extension Machine Learning Engineering Bootcamp can help.

Through 100% online, mentor-led learning, you’ll master the applied skills you need to get hired in six months. As a UC San Diego Extension Machine Learning Engineering Bootcamp student, you’ll gain hands-on experience through 15+ mini projects designed to reinforce technical concepts—plus a capstone project to showcase in your professional portfolio.

Once you complete the bootcamp, you’ll be able to deploy machine learning algorithms and build a complete machine learning application.

Take the first step towards your machine learning engineering career and explore the UC San Diego Extension Machine Learning Engineering Bootcamp today. Those with prior software engineering or data science experience are encouraged to apply.