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 K-nearest neighbors in terms of word similarity</li>
	<li>Create a function that computes the K-nearest neighbors for a given word</li>
</ul>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn about K-nearest neighbors in terms of word similarity</li>
	<li>Create a function that computes the K-nearest neighbors for a given word</li>
</ul>


In this chapter, we will be learning how to calculate K-nearest neighbors based on cosine similarity. 


What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

K-Nearest Neighbors

Chapter Goals: