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

## A. Industry applications of natural language processing

Natural language processing (NLP) is one of the most important tasks in industry, and it makes heavy use of machine learning. NLP deals with anything related to using machines to process and understand human text/speech (i.e. “natural language”). Many companies, including giants like Google and Amazon, have teams dedicated to NLP research and application in products.

## B. How NLP works
In this course, we focus more on the text aspect of NLP, since speech-related tasks will utilize many disciplines outside of machine learning (e.g. signal processing, linguistics, etc.). With written text, it is much simpler to process the data into a feasible input for a machine learning model.

Text data is normally processed with relation to a *vocabulary*. The vocabulary just is the set of unique words that appear across all the text in the *corpus* (the set of documents used to train the model). There are many different ways to process text data, but in this course you’ll learn how to convert sentences/documents into *embedding vectors*.

After processing the text data, we feed it into a particular type of neural network called a *recurrent neural network* (RNN). RNNs are great for dealing with sequential data like text, and in this course you’ll be using the long short-term memory (LSTM) variation of RNNs.

The LSTM model can be adjusted to perform various NLP tasks, ranging from text classification to text generation. For different NLP tasks, the model will have different outputs.


## C. What will this course provide?

After taking this course, you’ll be able to process text data, train different LSTM models on the data, and use models to perform a variety of NLP tasks. Specifically, you will be able to:
* Process documents of text into embedding vectors
* Build a variety of different LSTm models for tasks ranging from text classification to text generation and machine translation

# A. Industry applications of natural language processing

Natural language processing (NLP) is one of the most important tasks in industry, and it makes heavy use of machine learning. NLP deals with anything related to using machines to process and understand human text/speech (i.e. “natural language”). Many companies, including giants like Google and Amazon, have teams dedicated to NLP research and application in products.

# B. How NLP works
In this course, we focus more on the text aspect of NLP, since speech-related tasks will utilize many disciplines outside of machine learning (e.g. signal processing, linguistics, etc.). With written text, it is much simpler to process the data into a feasible input for a machine learning model.

Text data is normally processed with relation to a *vocabulary*. The vocabulary just is the set of unique words that appear across all the text in the *corpus* (the set of documents used to train the model). There are many different ways to process text data, but in this course you’ll learn how to convert sentences/documents into *embedding vectors*.

After processing the text data, we feed it into a particular type of neural network called a *recurrent neural network* (RNN). RNNs are great for dealing with sequential data like text, and in this course you’ll be using the long short-term memory (LSTM) variation of RNNs.

The LSTM model can be adjusted to perform various NLP tasks, ranging from text classification to text generation. For different NLP tasks, the model will have different outputs.


# C. What will this course provide?

After taking this course, you’ll be able to process text data, train different LSTM models on the data, and use models to perform a variety of NLP tasks. Specifically, you will be able to:
* Process documents of text into embedding vectors
* Build a variety of different LSTm models for tasks ranging from text classification to text generation and machine translation

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Overview

A. Industry applications of natural language processing

B. How NLP works

C. What will this course provide?