There is a different kind of futuristic appeal attached to machine learning and artificial intelligence roles. As these technologies become more imperative in various industries, many sought-after career paths open up. One of these is that of an ML engineer. But who are they and what is a machine learning engineer salary? This blog has all the answers!
A machine learning engineer has prowess in implementing, maintaining and designing ML systems. These professionals apply multiple techniques and algorithms for developing predictive models, analyzing humongous data sets and extracting insights.
ML engineers usually have great statistics, mathematics and programming skills. Their understanding of ML frameworks and algorithms is noteworthy. They work in close proximity with software engineers, stakeholders and data scientists. Deploying ML solutions to address key business needs is the motive behind implementing machine learning. There are many roles and responsibilities undertaken by these experts. But how much do they earn?
Explore igmGuru's Machine Learning training program to accelerate your career in the most growing or demanding field.
Take these points into consideration to completely understand the aspect of a machine learning engineer salary:
You May Also Read- Machine Learning Vs AI: Important Differences Between Them
Becoming an ML engineer is going to be a highly beneficial career option. There is a great salary package associated with the role of an ML engineer, which means the competition is soon going to be high. This is hence the best time to get started by learning the essential skills.
Learn the key concepts of this technology like supervised & unsupervised learning and semi-supervised & reinforcement learning. Additionally, learn about cross-validation & data clearing techniques, exploratory data analysis, feature engineering, model deployment, etc.
It's important to know how to read, edit and create codes. Top programming languages today are Python, C++, R, Java and JavaScript.
Knowledge about showcasing the findings in easy-to-understand manner is crucial. Learn tools like Seaborn, matplotlib, etc.
Understand DL concepts to enhance the resume. These include Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, YOLO V4, Long Short Term Memory Networks, etc.
Knowledge of cloud platforms like IBM Watson, AWS SageMaker, Azure ML and Google Cloud AI is important.
These two are the driving force behind ML. Calculus, discrete math, linear algebra, statistics, probability, differential calculus, etc. are the key topics.
SAS Business Intelligence, MicroStrategy, Tableau, Oracle Business Intelligence, etc.
Course Schedule
| Course Name | Batch Type | Details |
| Machine Learning Training | Every Weekday | View Details |
| Machine Learning Training | Every Weekend | View Details |
