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DeepLearning.AI

Natural Language Processing Specialization

  • up to 4 months
  • Intermediate

The Natural Language Processing Specialization teaches you how to design NLP applications that perform tasks such as sentiment analysis, language translation, text summarization, and chatbot creation. This specialization is critical for analyzing massive quantities of unstructured, text-heavy data and is essential for the AI-powered future.

  • Sentiment Analysis
  • Machine Translation
  • Text Summarization
  • Chatbots
  • Named Entity Recognition

Overview

In the Natural Language Processing Specialization, you will learn to use logistic regression, naïve Bayes, and word vectors for sentiment analysis, analogies, and translations. You will also explore dynamic programming, hidden Markov models, and word embeddings for autocorrect, autocomplete, and part-of-speech tagging. Advanced topics include dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks for sentiment analysis, text generation, named entity recognition, and question identification. Finally, you will delve into encoder-decoder, causal, and self-attention models for machine translation, text summarization, question-answering, and chatbot creation.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Data Scientists

Professionals looking to enhance their skills in natural language processing and machine learning.

AI Enthusiasts

Individuals interested in learning how to build NLP applications and models.

Software Engineers

Engineers aiming to integrate NLP capabilities into their applications and systems.

Why should you take this course?

Artificial Intelligence

This specialization will equip you with the skills to build NLP applications for sentiment analysis, language translation, text summarization, and chatbots. It is ideal for data scientists, AI enthusiasts, and software engineers looking to advance their careers in the AI-powered future.

Pre-Requisites

1 / 3

  • Basic understanding of machine learning concepts

  • Familiarity with Python programming

  • Knowledge of linear algebra and probability

What will you learn?

Course 1: Natural Language Processing with Classification and Vector Spaces
Perform sentiment analysis of tweets using logistic regression and naïve Bayes, use vector space models to discover relationships between words, and write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing.
Week 1: Sentiment Analysis with Logistic Regression
Learn how to extract features from text into numerical vectors, then build a binary classifier for tweets using logistic regression.
Week 2: Sentiment Analysis with Naïve Bayes
Understand the theory behind Bayes’ rule for conditional probabilities, then apply it toward building a Naive Bayes tweet classifier of your own.
Week 3: Vector Space Models
Learn how to create word vectors that capture dependencies between words, then visualize their relationships in two dimensions using PCA.
Week 4: Machine Translation and Document Search
Learn how to transform word vectors and assign them to subsets using locality-sensitive hashing to perform machine translation and document search.
Course 2: Natural Language Processing with Probabilistic Models
Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, apply the Viterbi Algorithm for part-of-speech (POS) tagging, write a better auto-complete algorithm using an N-gram language model, and write your own Word2Vec model using a neural network.
Week 1: Auto-correct
Learn about autocorrect, minimum edit distance, and dynamic programming, then build your own spellchecker to correct misspelled words.
Week 2: Part-of-Speech (POS) Tagging and Hidden Markov Models
Learn about Markov chains and Hidden Markov models, then use them to create part-of-speech tags for a Wall Street Journal text corpus.
Week 3: Auto-complete and Language Models
Learn about how N-gram language models work by calculating sequence probabilities, then build your own autocomplete language model using a text corpus from Twitter.
Week 4: Word Embeddings with Neural Networks
Learn how word embeddings carry the semantic meaning of words, making them more powerful for NLP tasks. Then build your own Continuous bag-of-words model to create word embeddings from Shakespeare text.
Course 3: Natural Language Processing with Sequence Models
Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and use Siamese LSTM models to compare questions in a corpus.
Week 1: Neural Network for Sentiment Analysis
Learn about neural networks for deep learning, then build a sophisticated tweet classifier that places tweets into positive or negative sentiment categories using a deep neural network.
Week 2: Recurrent Neural Networks for Language Modeling
Learn about the limitations of traditional language models and see how RNNs and GRUs use sequential data for text prediction. Then build your own next-word generator using a simple RNN on Shakespeare text data.
Week 3: LSTMs and Named Entity Recognition (NER)
Learn how long short-term memory units (LSTMs) solve the vanishing gradient problem and how Named Entity Recognition systems quickly extract essential information from text. Then build your own Named Entity Recognition system using an LSTM and data from Kaggle.
Week 4: Siamese Networks
Learn about Siamese networks, a special type of neural network made of two identical networks that are eventually merged, then build your own Siamese network that identifies question duplicates in a dataset from Quora.
Course 4: Natural Language Processing with Attention Models
Translate complete English sentences into German using an encoder-decoder attention model, build a Transformer model to summarize text, use T5 and BERT models to perform question-answering, and build a chatbot using a Reformer model.
Week 1: Neural Machine Translation with Attention models
Discover some of the shortcomings of a traditional seq2seq model and how to solve for them by adding an attention mechanism, then build a Neural Machine Translation model with Attention that translates English sentences into German.
Week 2: Text Summarization with Transformer models
Compare RNNs and other sequential models to the more modern Transformer architecture, then create a tool that generates text summaries.
Week 3: Question-Answering
Explore transfer learning with state-of-the-art models like T5 and BERT, then build a model that can answer questions.
Week 4: Chatbots with Reformer models
Examine some unique challenges Transformer models face and their solutions, then build a chatbot using a Reformer model.

Meet your instructors

  • Younes Bensouda Mourri

    Founder, LiveTech.AI

    Younes Bensouda Mourri is a Stanford University lecturer and the founder of LiveTech.AI, an organization that democratizes technical education through personalized learning and makes companies more AI-technologically advanced.

  • Łukasz Kaiser

    Member of Technical Staff, OpenAI

    Łukasz Kaiser has extensive experience in machine learning, having worked on the TensorFlow system and demonstrating how neural networks can acquire complex discrete algorithms. He currently works on natural language processing for OpenAI, developing state-of-the-art NLP systems for translation and summarization.

  • Eddy Shyu

    Curriculum Product Manager, DeepLearning.AI

    Eddy Shyu led the creation of Andrew Ng’s Machine Learning Specialization, 14 AI/ML courses with over 500,000 unique learners and thousands of new enrollments daily.

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