Computer Science is an important skill that is necessary for a Machine Learning Engineer. In fact, Computer Science isn’t limited to Machine Learning engineers alone. Every programmer must be equipped with computer science knowledge. The important fundamentals of computer science include-

- Data structures- stacks, queues, multi-dimensionalarrays, trees, graphs, etc.
- Computability and complexity- P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.
- Algorithms -searching, sorting, optimization, dynamic programming, etc.
- Computer architecture- memory, cache, bandwidth, deadlocks, distributed processing, etc.

Machine learning algorithms are a very essential requirement for machine learning jobs. The stand implementation of ML algorithms can be availed from libraries/packages/APIs such as TensorFlow, Theano, etc. These algorithms must be applied with a model that is most suitable. Learn about the advantage and disadvantage of the approaches by practicing on platforms like Kaggle, Tunedit, HackerRank, etc.

The probability and techniques of Machine Learning derived from algorithms like Markov Decision Processes, Hidden Markov Models, Bayes Nets, etc., is a skill one must possess/enhance for Machine Learning job opportunities. Statistics and analysis methods like ANOVA, hypothesis testing, etc., are also an extremely important part of ML probability and statistics.

Software and system designing is essential for organizing the program modules for changes, development, and forming programs. Hence, having a strong grasp in software engineering and system design is one crucial skill to land attractive Machine Learning jobs. Documentation, system design, modularity, etc., are some of the software and system design practices one must be familiar with.