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>Learn about sentiment analysis and how it's related to NLP</li>
	<li>Create sentiment analysis training pairs from a text corpus</li>



<h2>A. Classifying sentiment</h2>

<div id="section1_l1" class="collapse show">
<p>Any time you read an opinionated text passage (e.g. social media post, editorial, etc.), the text contains <b>sentiment</b>. Sentiment refers to the writer's attitude in the text, so <b>sentiment analysis</b> is the task of analyzing or deducing what the writer's attitude is based on the text.</p>
<p>Oftentimes, the writer's attitude can be classified into one of several categories. For example, online user product reviews are normally given on a 1-5 star scale. In this case, we could separate the reviews based on how many stars they have.</p>
<p>An even simpler classification would be to separate the reviews into two categories: positive and negative. This is just regular binary classification, while the stars example is multiclass classification.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn about sentiment analysis and how it's related to NLP</li>
	<li>Create sentiment analysis training pairs from a text corpus</li>



<h2>A. Classifying sentiment</h2>

<div id="section1_l1">
<p>Any time you read an opinionated text passage (e.g. social media post, editorial, etc.), the text contains <b>sentiment</b>. Sentiment refers to the writer's attitude in the text, so <b>sentiment analysis</b> is the task of analyzing or deducing what the writer's attitude is based on the text.</p>
<p>Oftentimes, the writer's attitude can be classified into one of several categories. For example, online user product reviews are normally given on a 1-5 star scale. In this case, we could separate the reviews based on how many stars they have.</p>
<p>An even simpler classification would be to separate the reviews into two categories: positive and negative. This is just regular binary classification, while the stars example is multiclass classification.</p>
</div>


Learn about sentiment analysis and how it relates to NLP.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Sentiment Analysis

Chapter Goals:

A. Classifying sentiment