Becoming a successful machine learning/artificial intelligence engineer, there are certain numbers of skills required to stir a prosperous career in the field of ML/AI. Having adept about the subject of matter, understanding essential requirements and the fundamentals necessary to build a successful career in the field of artificial intelligence or machine learning.
Machine learning is an innovative technology that employs logical algorithm and statistical models to carry out specific tasks, such tasks are being completely achieved through system self-learning of complex and important trends from a given dataset and drawing an inference for useful insights and conclusion. Whereas, artificial intelligence is a wider aspect of machine learning that employs human cleverness to machines in other to be able to autonomously process, learn, think and lastly make intelligent adjustments or corrections. Artificial intelligence was built to work autonomously without engaging the computer programmer for explicit coding of each part of the AI system.
Skills Required By Machine Learning Engineers
A quality and qualified engineer looking for an amazing career in the field of ML/AI needs to possess an in-depth knowledge of data science and statistical implementations; also, some fundamental know-how on software engineering and data handling. Some of the important skills required by aspiring ML/AI engineer includes:
Basic computer programming knowledge
A fundamental knowledge about software building and implementation ranging from linear programming, computer structural design, data architecture, to algorithm optimization as all needed as a ML/AI engineer. This engineer will also engage in simulating machines to carry out activities same way human beings does. An engineer without this basic programming knowledge will find it difficult.
Data handling and validation
Data handling skills is very important in ML/AI building and implementation. These skills is highly required by the intending engineers to perfectly identify different trends and patterns available of gathered datasets. Without this skills, proper data handling and validations can’t carry out.
Statistics and Probability
Data science was based on statistical modelling, a very crucial part of ML. An intending ML/AI engineer must be skilled in carrying out probability theories like Markov models, Bayesian principles, amongst others. Also, such an engineer must be capable of performing univariate and multivariate statistical analysis that forms the fundamental of ML models.
Software design
Long-term product of building a ML/AI system is incorporation into software either on PC or Smart phones. An engineer of ML/AI requires to have these skills for final representation and deployment of the developed model into software, this will provide a user-friendly interface for proper application.
Signal processing skills
Knowledge of signal processing is required to extract features and solve complex problems. This is a crucial aspect of ML that includes feature mining that will enable algorithms like Shearlets, Bandlets, and Wavelets perform theoretical analysis in solving multifaceted scenarios.
Architectures of Neural Network
Tasks that are beyond human capabilities are execute by Neural Networks. This network has demonstrated to be one of the most accurate ways of deciphering problems like Object recognition and classification, speech recognition, natural language processing, and many more.
Good understanding of Algebra and Calculus
Having an in-depth knowledge of Calculus, Matrices, Vector, Algebra, and others are very vital in understanding and representing some ML/AL concepts.
Physics
This is one of those basic knowledges required by ML engineer. It aids in the representation of complex situation and inferring of the situation.
Domain Knowledge
Knowledge of the field of subject matter, a machine learning engineer must be vast in of problem identification and solving to be able to design a nearly perfect model for useful insights and interpretation.
Fast Prototyping
The available for building and implementing an ML system is expected to be reduced to the barest minimum. This can only be achieved an engineer that knows how to select right model in carrying out certain excellently. It also includes weighing up different methods to achieve a faster model for implementation.
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