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>Classify input text using the BiLSTM model</li>
</ul>


<h2>A. Calculating predictions</h2>

<div id="section1_l6" class="collapse show">
<p>our BiLSTM is used to classify sentiment in text as either positive or negative. Since there are only two classes, this is binary classification. For binary classification, we apply the sigmoid function on the model's logits to obtain a probability for the input text. The probability refers to the likelihood that the input text is labeled "positive" (i.e. label <code>1</code>), and we can simply round the probability to the nearest integer to obtain the model's prediction.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Classify input text using the BiLSTM model</li>
</ul>


<h2>A. Calculating predictions</h2>

<div id="section1_l6">
<p>our BiLSTM is used to classify sentiment in text as either positive or negative. Since there are only two classes, this is binary classification. For binary classification, we apply the sigmoid function on the model's logits to obtain a probability for the input text. The probability refers to the likelihood that the input text is labeled "positive" (i.e. label <code>1</code>), and we can simply round the probability to the nearest integer to obtain the model's prediction.</p>
</div>


Use the model to classify a given text sequence.

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Classification

Chapter Goals:

A. Calculating predictions