How To Learn Natural Language Processing

Natural Language Processing or NLP is the field of Artificial Intelligence that empowers a computer system to read and understand human languages. In this article, I will take you through a complete roadmap on how to learn Natural Language Processing.

What is Natural Language Processing?

When we train a computer system to understand human languages is what Natural Language Processing is. With the use of machine learning algorithms and appropriate datasets, we can train models for the tasks of human-computer interaction.

NLP is now being used in almost all applications developed by big tech companies. One thing you should know here is that the concept of Natural Language Processing is not new, it has been for a long time. For example, the popular virtual assistant “Siri” found in iPhones is one of the most successful applications where NLP is used.

How To Learn Natural Language Processing?

There is no scope of learning about any field of Artificial Intelligence if you don’t know how to work with data. So your first step towards Natural Language Processing is to get started with data science. After learning the fundamentals of data science you can more towards learning about some special types of techniques that are used to prepare textual data from natural language processing which are:

  1. Tokenization: It means converting a piece of text into a set of tokens.
  2. Removing Stopwords: It means to remove the prepositions from a piece of text.
  3. Stemming: It is used to remove all the affixes from a piece of text.

Without using these techniques you cannot train a model for any task of natural language processing. After learning the concept behind all the three techniques mentioned above, you have to learn some modelling techniques which are:

  1. Bag of words
  2. TF-IDF Vectorization
  3. Word Embeddings

Then your next step is to learn the fundamental Python libraries for Natural Language Processing which are:

  1. NLTK
  2. SpaCy

Both NLTK and spaCy are equally important while working on the applications of Natural Language Processing. After these two Python libraries, you need to learn the concept of Neural Networks which will help you while working with a large textual dataset with too many useful features and speech datasets. So the Python libraries that you need to learn for Neural networks are:

  1. TensorFlow
  2. PyTorch


So below is the complete roadmap on how to learn Natural Language Processing:

  1. Prerequisites:
    1. Tokenization
    2. Stopwords
    3. Stemming
  2. Language Modelling Techniques:
    1. Bag of Words
    2. Tf-Idf Vectorization
    3. Word Embeddings
  3. Python Libraries:
    1. NLTK
    2. spaCy
    3. TensorFlow
    4. PyTorch

I hope you liked this article on how to learn natural language processing. Feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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