Machine Learning - Data set contains much more values for one class

I would like to use machine learning to determine the race of an individual based on things like the first letter of his/her name, my problem is that the data I have is distributed in the following way:

COUNT RACE
92742 African
12    Asian
43349 Mixed race
327   Foreign National
4588  Indian
11179 White


So the data is very skewed in terms of volume towards African and Mixed race. When training my learner will this affect the learning and what should I do to combat this? Also instead of the first letter of the name (A-Z) should I use the number representation of the alphabet letter (1-26) or even a representative value between 0 and 1?

• you should try to collect more data for the underrepresented classes. right now the imbalance is too severe (especially for the Asian class). For mild imbalance you could use algorithms such as SMOTE – Antoine Mar 17 '17 at 9:13