Introduction to Natural Language Machine Learning using PyTorch

In this lesson, we will be learning how to use PyTorch to create a basic natural language machine learning system. We will be building a simple system that is capable of understanding basic English language input and providing an appropriate response. By the end of this lesson, you will have created a “Hello World” type project to introduce yourself to the basic concepts of machine learning.

Prerequisites

This lesson is geared toward experienced programmers but ML beginners. We assume that you have some basic knowledge of basic programing such as object orientated programming, linear algebra and calculus. Additionally, some familiarity with the Python programming language is also necessary.

What is Natural Language Machine Learning?

Natural language machine learning is a subset of artificial intelligence-related technology used to enable computer systems to understand and process human language. The goal of natural language machine learning is to teach computer systems to interpret and produce language in a way that is close to human-like accuracy.

What is PyTorch?

PyTorch is an open-source machine learning library developed by Facebook. It provides a wide range of models and tutorials for machine learning, including natural language processing.

Setup

Before we begin writing our code, we need to install and setup the necessary dependencies. We will be using the PyTorch library to create our machine learning system.

1. First, we need to install PyTorch. We can download and install the PyTorch library from PyTorch’s website.

2. Once PyTorch is installed and running, we can import the module we need by typing the following command in your terminal:

   import torch

3. We also need to install Scikit-learn and NLTK (Natural Language Toolkit). We can download and install both of these libraries from the Python Package Index.

4. We will also be using Jupyter Notebook to write our code, so make sure it is installed and running.

Once all of these components are installed, we are ready to start writing our code.

Writing our Code

1. First, we will be creating a data set which consists of input strings (questions) and the corresponding outcomes (responses). We can use the Scikit-learn and NLTK libraries to create our data set.

2. Once we have our data set ready, we can create our PyTorch model. We can use the PyTorch library to define the structure of our model and the functions necessary for our model to work.

3. We can then feed our data into the model and train it, so that the model can start learning from our data set and create a mapping from the input strings to the corresponding outcomes.

4. Finally, we can test our model by feeding it input strings and checking if the model gives us the correct responses.

Conclusion

By completing this lesson, you have created a basic natural language machine learning system using PyTorch. Now that you are familiar with the basic concepts and processes of machine learning, you can continue to explore further possibilities with more complex machine learning systems.