Machine Learning Engineer Required Skills

Interest in machine learning is at an all-time high. Colleges and universities are offering new and exciting specializations to attract people. Machine learning skills have become a top priority for most employers.

Many job advertisements specifically ask for highly skilled machine learning engineers. This article will summarize important machine learning skills to help you catch up with this exciting field's latest developments.

Mathematics, Probability, and Statistics

A deeper understanding of Mathematics and statistics is essential for engineers because it lets them comprehend the algorithm's basic concepts, neural networks, and data. Data comes in different shapes and forms, and the mathematical expression helps you choose an appropriate algorithm for a specific dataset.

Many machine learning algorithms are statistical modeling applications, so a good knowledge of mathematical concepts such as linear algebra, probability, statistics, calculus, and distributions would let an engineer fine-tune the parameters efficiently. Probability helps an engineer understand the effects of physical world uncertainty.

Concepts such as conditionals, Bayes rule, dependence/independence, Bayes Nets, and Markov Models are vital for machine learning algorithms.

Fundamentals of Computer Science and Programming

Various computer science basics make it easy to grasp machine learning concepts. Data structures such as graph, stack, tree, queue, search, sort, space, and time complexity such as P, NP, and big O play an important role in machine learning.

Similarly, software engineering and computer architecture knowledge is a very useful skill to have for a machine learning engineer. Machine learning extensively uses memory management, cache, distributed processing, microprocessor, and FPGA programming, and an engineer must apply and implement these ideas during day-to-day work.

Besides, the knowledge of programming languages and libraries such as Python, R, NumPy, Pandas, TensorFlow, Matplotlib, Scikit-learn for statistical modeling, Hadoop, and Spark for distributed computing, and SQL for database management is a plus for machine learning engineers.

Data Analysis and Modeling

The world is inundated with data, and extracting useful information from unorganized data is crucial for designing an effective machine learning algorithm. Data analysis and modeling comprises organizing data, understanding its structure, identifying correlations, visualizing data, and presenting insights. Also, the available data must be classified before being used for the machine learning model.

The data organization is carried out using classification, regression, clustering, and neural networks. Similarly, an algorithm needs to be tested before its deployment in the real world. Testing is done using the data-enabled test. In this process, real-world data is used to predict the effectiveness of the algorithm. The testing is carried out using anomaly detection, cross-validation, etc. A machine learning engineer must be proficient in testing because it is the most crucial development stage.

Apart from the above core skills, there are numerous useful skills that machine learning engineers can learn to boost their productivity and enhance career prospects. Some of these skills include Adopting software engineering best practices, employing secure coding principles, and familiarity with existing machine learning libraries, packages, and APIs such as scikit-learn, Spark’s MLlib, H2O, TensorFlow, Theano, etc.

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