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Natural Language Processing with Machine Learning
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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.

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Overview

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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...Show More

In this course you'll learn techniques for processing text data, creating word embeddings, and using long short-term memory netw...Show More

TAKEAWAY SKILLS

Python

Machine Learning

Natural Language Processing

TensorFlow

Content

46 Lessons4 Quizzes33 Challenges

What you'll learn from this course

1 Lessons

Get familiar with NLP using ML for text processing and building LSTM models.

Overview

Word Embeddings

10 Lessons

Grasp the fundamentals of word embeddings, their structures, and methods for effective NLP applications.

Language Model

11 Lessons

Examine LSTM-based language modeling, sequence padding, RNNs, dropout, multilayer architectures, loss calculation, and tensor indexing.

Text Classification

9 Lessons

Enhance your skills in BiLSTM text classification, embeddings, and sentiment analysis using TensorFlow.

Seq2Seq Model

15 Lessons

Map out the steps for understanding and implementing Seq2Seq models in NLP tasks.

Certificate of Completion

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Course Author:

Adaptilab

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