Machine translation is considered the holy grail of Natural Language Processing (NLP). The hope of that some day we would be able to automatically translate a foreign language into our native language. The wish that we can have a babelfish device that can interpret live. The day have come where we would be able to perform machine translation in python, within our favorite NLP toolkit, Natural Language ToolKit (`NLTK`) We introduce some basic knowledge of Statistical Machine Translation (SMT) with (i) the _phrase-based machine translation_ paradigm, (ii) _noisy channel alignment model_, (iii) _ngram language models_ and (iii) _log-linear decoding model_. Thereafter, we guide the users step-by-step through the SMT processes with code-snippets from the `align` and `translate` module in the NLTK toolkit. Firstly, we present the word-alignment models available in the NLTK `align` module and then we show how to output the phrase-table with the phrases' probabilities. Finally, we introduce the stack-based decoder that produces the final translated output. Before concluding, we compare the current performance (in speed and accuracy) of the `NLTK` stack decoder with the reigning machine translation toolkit, `Moses`