If you have any recommended study methods, textbooks, materials, etc., I would appreciate it if you could comment.
Courra's Machine Learning by Stanford University is easy to understand. In the case of books, Manabu Okumura "Introduction to Machine Learning for Language Processing" Since the range is quite wide, if you know what you want to do and when you want to use it, you can study from the field for that and it is efficient.
(Thank you: smile :)
Then, when I was googled by Manabu Okumura "Introduction to Machine Learning for Language Processing", Tokyo Metropolitan University Natural Language Processing Laboratory (Komachi Lab.) Natural Language Processing For those who want to learn by themselves, I will summarize the specific skills that should be acquired.
Knowledge of calculus and linear algebra in the first year of science and engineering
"Vector (inner product)" "Simultaneous equations" "Probability (simultaneous probability / conditional probability, random variable / probability distribution)" "Sequence sequence (arithmetic progression / limit)" "Differentiation (derivative of logarithmic / exponential function, differentiation of composite function, maximum / minimum and maximum / minimum of function)" "Matrix (eigenvalue, inverse matrix)" I often use the area.
TOEIC 700 points Reading comprehension (ability to understand what is written in a dissertation to others and drop it into a program)
Languages that are often used in the field of natural language processing are Python, Java, and C ++. Any language is fine, so create one language that allows you to write what you want to do without looking at the reference. (It's much better to be able to master one language than to reach out to multiple languages and get half-hearted.)
For the time being, this isn't it. I hope it helps people who want to learn: shaved_ice:
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