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 a sequence to sequence (seq2seq) model. Seq2Seq models are used for tasks that involve reading in a sequence of text and generating an output text sequence based on the input.</p>



# A. Sequence to sequence
<div id="section1_l0" class="collapse show">
<p>One of the most important frameworks in NLP is the sequence to sequence (seq2seq) framework. This framework encompasses any task that involes taking in some text and returning some generated text. Dialog systems (e.g. chatbots), text summarization, and machine translation are all seq2seq applications.</p>
</div>


# A. Sequence to sequence
<div id="section1_l0">
<p>One of the most important frameworks in NLP is the sequence to sequence (seq2seq) framework. This framework encompasses any task that involes taking in some text and returning some generated text. Dialog systems (e.g. chatbots), text summarization, and machine translation are all seq2seq applications.</p>
</div>


What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Introduction

A. Sequence to sequence