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>A. Closing thoughts</h2>
Natural language processing is one of the hottest fields in industry, since nearly every company needs to provide meaningful results and insights from raw text data. From social media and finance to housing and retail, many companies across a variety of different industries are leveraging ML-based natural language processing techniques to get ahead.

<h2>B. Course recap</h2>
This course has provided an overview of how to use machine learning to perform various natural language processing tasks. You’ll be able to take the practical lessons and actionable insights from this course and apply them to your projects.

<h2>C. Topics covered</h2>

* Text processing
* Word embeddings
* sentiment analysis
* machine translation
* text generation.


<h2>D. Industry standard frameworks used in this course</h2>

* NumPy
* TensorFlow
* Keras

What you'll learn from this course

Word Embeddings

Language Model

Text Classification

Seq2Seq Model

Course Conclusion

A. Closing thoughts

B. Course recap

C. Topics covered

D. Industry standard frameworks used in this course