A foundational understanding of statistics is necessary if you want to become a machine learning engineer, as it allows you to interpret data and extract relevant insights. This involves knowledge of statistical tests, distributions, and probability theories.

This is a core skill set of a data scientist that you must master so that the models you build are theoretically sound and provide consumers with a great user experience.

After building a machine learning model, you must evaluate its performance to ensure its effectiveness and reliability. This involves utilizing appropriate metrics like accuracy, precision, and recall to assess if the model is on par with expectations.

To deploy machine learning models and continuously monitor their performance over time, you need to learn DevOps, a combination of software development and IT operations.

Cloud platforms like AWS, Azure, and Google Cloud Platform provide services specifically designed to build, train, and deploy machine learning models.

After deploying machine-learning models in real-world applications, you will end up having to update the data used to train these algorithms or create different model versions as time goes by.

Python is a popular choice for machine learning engineering because of its simplicity, along with an extensive choice of libraries like Tensorflow and Pytorch.

Natural Language Processing is naturally quite important and a fundamental part of Machine Learning. In essence, NLP aims to teach the human language with all its complexities to computers.