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

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



# 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 course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Introduction

A. Language modeling