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

They're used to represent a sequence of words as a single continuous vector

Word embeddings are easier to look up than regular tokenized IDs

It makes training models for NLP tasks quicker and more efficient

As a more meaningful way to capture the connections between vocabulary words

The former skips OOV words while the latter maps OOV words to a unique ID

The former uses a constrained range of values for the embeddings

The latter uses all the training data while the former randomly chooses a subset

The former predicts context from the target, the latter predicts target from context

What's the difference between the skip-gram and CBOW models?

What’s the difference between the skip-gram and CBOW models?

The similarity between sentences of word IDs

The distance from the zero vector for a particular embedding

The distance from the mean embedding vector for a particular embedding

What is cosine similarity used for with respect to embeddings?

What is cosine similarity used for with respect to embeddings?

Using a larger window size results in faster training

Larger window sizes capture more connections between words

Which of the following is an accurate statement about window size?

Which of the following is an accurate statement about window size?

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Quiz