The road to become a machine learning engineer is long yet rewarding. As the need for ML expertise grows, more engineers are pursuing certifications in this discipline. However, the intimidating bottom-up curriculum proposed by most ML specialists is enough to deter many newbies. This article helps to create an easy and efficient learning path for anyone interested in pursuing a career as an ML engineer.
Are you a software engineer, programmer, developer, data scientist or a computer engineer looking to enter the lucrative field of machine learning? If you succeed, you will be in great company. The Top 10 Tech Job Skills Predicted to Grow the Fastest in 2021 report stated that the demand for AI and ML skills will grow at a compounded rate of 71% through 2025. According to Glassdoor, Machine Learning engineers with two to four years of experience in the field earn an average salary of $124,422 per year.
But before embarking on a career transition to ML engineering, certain things must be taken into consideration. First, ML engineering is not an entry-level position. It requires an undergraduate degree in mathematics, data science, computer science, computer programming or a related field. Many ML engineering positions also require a master’s degree or Ph.D. in one of those disciplines. After attaining the appropriate degree, it typically takes several months or even years before one becomes proficient in the field. So, where should you begin? When learning any new field, begin with the basics.
Machine Learning Engineer Knowledge Map
Those who work as ML engineers recommend that one should follow a set learning path for mastering the basics of machine learning before setting out to find one’s first ML Engineering position. On average, if one spends four or five hours a day mastering the steps along the ML learning path, it should take six months to a year to complete.
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Here are six steps toward mastering ML basics:
Step 1. Learn advanced mathematics
Machine learning and related algorithms need a thorough understanding of advanced mathematics, notably linear algebra, calculus, probability, and statistics. The goal of this first step in the ML learning path is to gain knowledge of advanced mathematics concepts as it applies to ML. Here are some resources to get started:
This website provides videos, examples and practice problems for learning linear algebra, calculus, statistics and probability.
This program teaches mathematics concepts as used in machine learning, including linear algebra and multivariate calculus.
This book will help one understand the mathematics of machine learning, especially as it pertains to the R programming language.
This graduate-level textbook introduces linear algebra and optimization in the context of machine learning.
This graduate-level textbook presents the mathematics behind machine learning techniques, especially probability/statistics.
Step 2. Obtain proficiency in ML programming
To obtain a machine learning engineering job, experience in computer programming is a must. Python is the most widely used programming language among data scientists; R is a close second, especially for ML projects that involve statistical operations. Here are two resources for mastering ML programming:
This module introduces basic programming concepts such as data structures, networked application programming, and …….