BERT stands for Bidirectional Encoder Representations from Transformers, and it is a state-of-the-art machine learning m

ELMo, short for Embeddings from Language Models, is a word embedding system for representing words and phrases as vectors. ELMo models the syntax and semantic of words as well as their linguistic context, and it was developed by the Allen Institute for Brain Science. There several variations of ELMo, and the most complex ELMo model (ELMo 5.5B) was trained on a dataset of 5.5B tokens consisting of Wikipedia (1.9B) and all of the monolingual news crawl data from WMT 2008–2012 (3.6B). While both BERT and GPT models are based on transformation networks, ELMo models are based on bi-directional LSTM networks.

In conclusion, if you are in a career transition to Data Science, probably in your 30s, then you don’t want to waste time on questions that already have a clear answer. For this reason, I have outlined what I wish someone told me before starting my career change:

I hope this post has helped you in some way. If you want to give DataQuest a try, then just check their website and start their free trial. As a disclaimer, I don’t receive any compensation from DataQuest or any other platform mentioned in this post in exchange for my recommendation. So, let’s get to work.

Even though BERT seems more inferior to GPT-3, the availability of source code to the public makes the model much more popular among developers. You can easily load a BERT variation for your NLP task using the Hugging Face’s Transformers library. Besides, there are several BERT variations, such as original BERT, RoBERTa (by Facebook), DistilBERT, and XLNet. Here is a helpful TDS post on their comparison:

I’m going to quickly run through your CV to look at your previous positions and see which are marked as ‘Data Scientist’. There are some other adjacent terms (depending on the role I’m hiring for), such as ‘Machine Learning Engineer’, ‘Research Scientist’ or ‘Algorithm Engineer’. I don’t include ‘Data Analyst’ in this bucket as the day-to-day work is typically different from that of a Data Scientist and the Data Analyst title is an extremely broad term. If you’re doing data science work at your present job and you have some other creative job description, it’ll probably be in your best interest to have your title changed to a Data Scientist. This can be very true for Data Analysts who are de facto Data Scientists. Remember, even if the CV contains descriptions of the projects you’ve worked on (and they include machine learning), a title other than Data Scientist will add unnecessary ambiguity. Additionally, if you’ve undergone a data science bootcamp or full-time masters in the field, this will probably be considered the beginning of your data science experience (unless you worked in a similar role earlier, which will warrant questions at a later stage).

- Monthly membership and a bit expensive: in this category, you can choose from three platforms (DataCamp, Codecademy or DataQuest). All of those platforms offer a Data Science with Python track.

Last but not least, the DataQuest curriculum is created based on real-life data, such as Android