Gain insights into processing text data, creating word embeddings, and using LSTMs for semantic analysis and machine translation. Explore industry-relevant NLP techniques with Python and TensorFlow.

nlp_docker.tar.gz

mnist-fashion

In this course you'll learn techniques for processing text data, creating word embeddings, and using long short-term memory networks (LSTM) for tasks such as semantic analysis and machine translation. After completing this course, you will be able to solve the important day-to-day NLP problems faced in industry, which is incredibly useful given the prevalence of text data.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

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

Natural Language Processing with Machine Learning

<h2 class="section-title">Chapter Goals:</h2>
<ul>
	<li>Understand how a language model works</li>
	<li>Learn how to set up the training data for a language model</li>
</ul>


<h2>A. Word probabilities</h2>

<div id="section1_l1" class="collapse show">
<p>As mentioned in the introduction, the purpose of a language model is to assign probabilities to words in sequences of text. The probability for each word is conditioned on the words that appear before it in the sequence. Though it may not seem like it at first glance, the task of calculating a word probability based on the previous words in a sequence is essentially multiclass classification.</p>
<p>Since each word in a sequence must come from the text corpus' vocabulary, we consider each vocabulary word as a separate class. We then use the previous sequence words as input into our language model to calculate probabilities for each vocabulary class.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Understand how a language model works</li>
	<li>Learn how to set up the training data for a language model</li>
</ul>


<h2>A. Word probabilities</h2>

<div id="section1_l1">
<p>As mentioned in the introduction, the purpose of a language model is to assign probabilities to words in sequences of text. The probability for each word is conditioned on the words that appear before it in the sequence. Though it may not seem like it at first glance, the task of calculating a word probability based on the previous words in a sequence is essentially multiclass classification.</p>
<p>Since each word in a sequence must come from the text corpus' vocabulary, we consider each vocabulary word as a separate class. We then use the previous sequence words as input into our language model to calculate probabilities for each vocabulary class.</p>
</div>


Learn how language models are trained to calculate word probabilities.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Language Model

Chapter Goals:

A. Word probabilities