Want a career in machine learning engineering but unsure if you should enroll in a bootcamp or attempt self-learning through an online course? This guide will help you choose the right path as you begin your ML engineering journey.
Here’s what we’ll cover:
Machine learning engineering is currently one of the most in-demand skills across all industries—a 2020 LinkedIn Emerging Jobs Report found that artificial intelligence specialists beat out all other professions for the top spot, with 74% annual growth over the past four years. With this growth has come high salaries, diverse opportunities, and a chance for more machine learning engineers to make a difference at some of the world’s most influential organizations.
The education industry has risen to meet this demand for ML engineers, with online bootcamps developing detailed curricula to prepare prospective students for the workforce; massive open online courses (MOOCs) spinning up programs that offer certifications; and troves of free online resources promising to teach prospective students everything they need to know—from Python and TensorFlow to natural language processing and computer vision—through free videos and blog posts.
With so many free and paid resources available, the process of choosing between self-taught and mentor-guided courses can raise a lot of questions and become overwhelming. Are paid courses always better? Should you start with self-learning? Is it even possible to successfully train yourself to become a machine learning engineer?
The following guide offers answers to some of these burning questions.
Machine learning engineering courses, which often take the form of MOOCs (massive open online courses) typically offer a hands-off approach to teaching and learning and can focus on specific elements of the discipline, such as introducing prospective students to the basics of machine learning, offering a crash course in Python or SQL, or unpacking the differences between unsupervised learning and reinforcement learning. These courses, offered by e-learning platforms such as Coursera, Udacity, Udemy, Codecademy, DataCamp, Khan Academy, EdX, and Simplilearn allow students to go on their own pace, often have an element of self-teaching, and many also offer certifications and can count toward college credits.
Bootcamps, on the other hand, are short-term programs that tend to offer a more hands-on and holistic learning experience. Instead of simply introducing students to the basics of a discipline or focusing on one element, many bootcamps use a range of resources such as video lectures and tutorials, readings, exercises and assignments, case studies, capstone projects, and some degree of mentorship to prepare students for everything from being able to perform the job of a machine learning engineer to acing a job interview. An instructor is often on hand to answer questions, and mentors and counselors are available to offer professional and academic guidance. The cost of bootcamps can range from $1,000 to $10,000.
Not all bootcamps are made the same, though. When choosing a bootcamp, it’s important to consider the comprehensiveness of the curriculum, the time commitment, whether you will get to work on real-world projects and portfolio development, and what career guidance and counseling are included. A good bootcamp shouldn’t simply teach you the skills required to perform the job of a machine learning engineer—it should also prepare you to land the job you want.
Most machine learning engineering bootcamps will teach you the most in-demand machine learning models and algorithms, including logistic regression, linear regression, and classification modeling. They will also teach regularization; recommendation engines; deep learning; calculus and linear algebra; neural networks; data science, machine learning, and AI tools such as Pandas, TensorFlow, Scikit-learn, NumPy, Matplotlib, Jupyter Notebook, and Spark/PySpark; and deploying machine learning systems into production.
In addition to teaching the highly technical skills required of ML engineers, the most successful bootcamps also provide mentorship from industry experts to help graduates navigate both the projects they’re working on and the job market.
Given the advanced nature of machine learning engineering, many courses and bootcamps have prerequisites, like a bachelor’s degree in computer science, math, statistics, physics, or economics; a background in data science or software engineering; or a high level of proficiency in programming languages such as Python and SQL.
Make sure you carefully read the course’s expectations before signing up.
Machine learning engineering courses can range from 6-18 months. Bootcamps that offer a condensed 6-month course typically require a time commitment of 15-20 hours a week. The process will take longer if you need to complete prerequisites such as learning programming languages.
In order to get the most out of a machine learning engineering course or bootcamp, you want to have a strong foundation in software engineering, data science, or statistics. If you have some industry experience in software engineering or development, or a degree in STEM, then you are in a good position to jump into a machine learning course or bootcamp.If you are a complete beginner, it’s important to learn the fundamentals of software engineering and data science—on the software engineering front, you should know at least one programming language (Python, Java, and C++ are the most popular) so that you can write the code required of ML engineers. On the data science front, make sure you know how to clean, optimize, and query datasets, understand data models, a
Is machine learning engineering the right career for you?
Knowing machine learning and deep learning concepts is important—but not enough to get you hired. According to hiring managers, most job seekers lack the engineering skills to perform the job. This is why more than 50% of Springboard's Machine Learning Career Track curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service. No other bootcamp does this.
Our machine learning training will teach you linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.
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