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.

Advanced

46 Lessons

Certificate of Completion

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.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

40 Playgrounds

33 Challenges

This course includes

40 Playgrounds

33 Challenges

Course Overview

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

TAKEAWAY SKILLS

Python

Machine Learning

Natural Language Processing

Tensorflow

Course Content

What you'll learn from this course

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

Overview

Word Embeddings

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

Introduction

Vocabulary Embeddings Skip-gram Embedding Matrix Candidate Sampling Embedding Loss Cosine Similarity K-Nearest Neighbors Quiz

Language Model

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

Introduction

Language Model Padding RNN/LSTM Dropout Multiple Layers LSTM Output Calculating Loss Predictions Tensor Indexing Quiz

Text Classification

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

Introduction

Sentiment Analysis Embeddings Bidirectional LSTM Logits Loss Classification Improving the Model Quiz

Seq2Seq Model

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

Introduction Training Data Final States Combined State Encoder-Decoder Attention Training Sampler Decoder Object Decoding Output Calculating Loss Inference Decoding Model Improvement Datasets Quiz Course Conclusion

Course Author

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Part of the Machine Learning Path

Become a Machine Learning Engineer

This Skill Path covers Python fundamentals, machine learning techniques and algorithms through practical exercises, preparing you as a machine learning engineer for real-world applications.

12 Modules

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