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3 IMPORTANT SKILLS FOR BECOMING A MACHINE LEARNING ENGINEER
Machine learning engineers feed data on models described by data scientists. They are also responsible for taking data science models and helping to measure them in production-level models that can handle terabytes of real-time data.
Computer Science Foundation and Program
This is requirement for being a good mechanical engineer. You need to be familiar with different CS concepts such as data structures (stack, line, tree, graph), algorithms (search, filtering, dynamic and greedy programs), complex space and time, etc. The good thing is that you probably know all of this when you do your bachelor’s degree in computer science!
You should be familiar with different programming languages such as Python and R for ML and statistics, Spark and Hadoop computer distribution, SQL data management, Apache Kafka data processing, etc. Learning Science and Data so it is good to be familiar with libraries.
Machine learning algorithms
It is very important to know all the common machine learning algorithms so you know where to use the algorithms. Basically ML algorithms are divided into 3 common types namely, Supervised, Unchecked, and Reinforcement Machine Learning Algorithms. In detail, some of the most common ones include Na Classve Bayes Classifier, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc. So it is good to have a good knowledge of all these skills before embarking on your journey as an ML engineer.
Data Processing and Evaluation
Data modeling involves understanding the basic structure of data and finding patterns that are invisible to the naked eye. You also need to scan the data using the appropriate data algorithm. For example, the type of machine learning algorithms you can use such as reversing, splitting, merging, reducing size, etc. Depends on the data. The sorting algorithm suitable for big data and speed can be pointless, or the retrospective algorithm can be a random forest. Similarly, the algorithm for the integration of classification variations is mode k while the potential for methods k. You need to know all these details about the various algorithms to contribute to data modeling and testing.