I would comment but I don't have enough reputation. It's a bit unclear exactly what you are trying to achieve here and how representative your example is - please edit your question to make it clearer.
Anyhow, like Guy Coder says, if you know exactly the words you are looking for, you don't really need machine learning or NLP libraries at all. However, if this is not the case, and you don't know have every example of what you are looking for, the below might help:
It seems like what you are trying to do is perform Named Entity Recognition (NER) i.e. identify the named entities (e.g. countries) in your sentences. If so, the short answer is: you don't need to use any machine learning algorithms. You can just use a python library such as spaCy
which comes out of the box with a pretrained language model that can already perform a bunch of tasks, for instance NER, to high degree of performance. The following snippet should get you started:
import spacy
nlp = spacy.load('en')
doc = nlp("Mary Lives in France")
for entity in doc.ents:
if (entity.label_ == "GPE"):
print(entity.text)
The output of the above snippet is "France". Named entities cover a wide range of possible things. In the snippet above I have filtered for Geopolitical entities (GPE).
Learn more about spaCy
here: https://spacy.io/usage/spacy-101