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Natural Language Processing with Machine Learning - AI-Powered Course
4.4
Advanced
9h
Updated 2 months ago
Natural Language Processing with Machine Learning
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 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.
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
1.
What you'll learn from this course
1 Lessons
Get familiar with NLP using ML for text processing and building LSTM models.
2.
Word Embeddings
10 Lessons
Grasp the fundamentals of word embeddings, their structures, and methods for effective NLP applications.
3.
Language Model
11 Lessons
Examine LSTM-based language modeling, sequence padding, RNNs, dropout, multilayer architectures, loss calculation, and tensor indexing.
4.
Text Classification
9 Lessons
Enhance your skills in BiLSTM text classification, embeddings, and sentiment analysis using TensorFlow.
5.
Seq2Seq Model
15 Lessons
Map out the steps for understanding and implementing Seq2Seq models in NLP tasks.
Certificate of Completion
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