I just finished lesson 4 of Part 1 (v3) where Jeremy talks about learning a language model. Is this the same as learning word embeddings? If yes, then can someone point me to resources which will help me learn character embeddings using fast ai?
No, it is not exactly the same. Even though learning embeddings is part of training an AWD-LSTM, the power of the language model comes from your ability to get representations for a whole sentence or text using the hidden state of the encoder. With plain word embeddings the usual way is to use a linear combination of word embeddings to represent a sentence/text, which has far less representational power.
As to the second part of your question, you can change the processor object to a char tokenizer instead of word tokenizer (this will require some coding), but the training process remains the same. Though the original AWD-LSTM paper describes a word-level model, I don’t see why this should not work on the char level.