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

The LSTM cell has a gated cell state for retaining information

RNN cells utilize the ReLU activation function instead of tanh

The LSTM cell has 2 internal layers, while the RNN cell has 3

The RNN cell is a more complex version of the LSTM cell

What is the difference between RNN and LSTM cells?

What is the difference between RNN and LSTM cells?

It improves each LSTM cell's ability to retain information

It significantly boosts the speed of training by decreasing the model size

It mitigates the risk of overfitting by randomly dropping units from the model

It increases the number of hidden units per LSTM cell

Why does dropout help during training?

To rank the words in a text corpus from most to least important

To calculate conditional word probabilities in a given text sequence

To translate text between different human languages

To calculate the probability of a word based on its index in a sequence

What is the main purpose of a language model?

What is the main purpose of a language model?

They consist of exactly two recurrent cell layers

They can only be used on text sequences of a fixed length

They're usually much faster to train than traditional neural networks

They can capture text dependencies better than traditional neural networks

Which of the following is a true statement about LSTMs?

Which of the following is a true statement about LSTMs?

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Quiz