Get familiar with the basic machine learning techniques used in natural language processing.

Mastering Natural Language Processing

This module was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine-learning talent. It’s built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

The code for this module is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. A knowledge of Python and TensorFlow is required for this module.

Natural Language Processing with Machine Learning

<p>In this section you will be building an LSTM (long short-term memory) network for language modeling.</p>



<h4>A. Language modeling</h4>

<div id="section1_l0" class="collapse show">
<p>The main idea of machine learning in NLP is to train a model to understand a text corpus well enough so that it can automatically perform tasks such as text classification or text generation. In order to do this, a machine learning model must be able to quantify what counts as a "good" or "bad" sequence of words.</p>
<p>Like every machine learning task, the task of quantifying text quality is centered around calculating probabilities. In this case, to calculate how "good" a sentence is, we would need to calculate how "good" each word in the sentence is. We can perform this task using a <i>language model</i>.</p>
<p>A language model can tell us the likelihood of each word in a given sentence or text passage based on the words that came before it. We can then determine how likely a sentence or text passage is by aggregating its individual word probabilities.</p>
</div>


# A. Language modeling

<div id="section1_l0" class="collapse show">
<p>The main idea of machine learning in NLP is to train a model to understand a text corpus well enough so that it can automatically perform tasks such as text classification or text generation. In order to do this, a machine learning model must be able to quantify what counts as a "good" or "bad" sequence of words.</p>
<p>Like every machine learning task, the task of quantifying text quality is centered around calculating probabilities. In this case, to calculate how "good" a sentence is, we would need to calculate how "good" each word in the sentence is. We can perform this task using a <i>language model</i>.</p>
<p>A language model can tell us the likelihood of each word in a given sentence or text passage based on the words that came before it. We can then determine how likely a sentence or text passage is by aggregating its individual word probabilities.</p>
</div>


# A. Language modeling

<div id="section1_l0">
<p>The main idea of machine learning in NLP is to train a model to understand a text corpus well enough so that it can automatically perform tasks such as text classification or text generation. In order to do this, a machine learning model must be able to quantify what counts as a "good" or "bad" sequence of words.</p>
<p>Like every machine learning task, the task of quantifying text quality is centered around calculating probabilities. In this case, to calculate how "good" a sentence is, we would need to calculate how "good" each word in the sentence is. We can perform this task using a <i>language model</i>.</p>
<p>A language model can tell us the likelihood of each word in a given sentence or text passage based on the words that came before it. We can then determine how likely a sentence or text passage is by aggregating its individual word probabilities.</p>
</div>


What you'll learn from this module

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Conclusion

Introduction

A. Language modeling