Natural Language Processing School of Artificial Intelligence

Natural Language Processing Specialization Bootcamp

Bootcamp AI

About This Course

This program will enhance learners’ existing machine learning and deep learning skills with the addition of natural language processing and speech recognition techniques. These skills can be used in various applications such as part of speech tagging and machine translation, among others. Learners will develop the skills they need to start applying natural language processing techniques to real-world challenges and applications.

Certificate

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Requirements

A well-prepared learner should have significant experience with Python and entry-level experience with probability, statistics, and deep learning architectures. Learners should also have the ability to write a class in Python and add comments to their code for others to read. Lastly, learners should have familiarity with the term “neural networks” and the differential math that drives backpropagation.
Learners need access to a 64-bit operating system with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.5 and supporting packages. The network should allow secure connections to remote hosts (like SSH).

Curriculum

257 Lessons

Intro to NLP

NLP Overview1:12Preview

03. Structured Languages1:31

03. Structured Languages

04. Grammar0:35

04. Grammar

05. Unstructured Text1:17

05. Unstructured Text

06. Counting Words00:00:00

07. Context Is Everything1:34

08. NLP and Pipelines0:48

09. How NLP Pipelines Work1:11

10. Text Processing1:56

11. Feature Extraction2:48

12. Modeling0:54

Text Processing

Spam Classifier with Naive Bayes

Part of Speech Tagging with HMMs

Project Part of Speech Tagging

Feature extraction and embeddings

Topic Modeling

01. Intro00:00:00

02. References

03. Bag of Words00:00:00

03. Bag of Words

04. Latent Variables00:00:00

04. Latent Variables

05. Matrix Multiplication00:00:00

06. Matrices00:00:00

07. Quiz Picking Topics00:00:00

07. Quiz Picking Topics

08. Solution Picking Topics00:00:00

09. Beta Distributions00:00:00

10. Dirichlet Distributions00:00:00

11. Latent Dirichlet Allocation00:00:00

12. Sample a Topic00:00:00

12.2 Sample a Topic00:00:00

13. Sample a Word00:00:00

13.2 Sample a Word00:00:00

14. Combining the Models00:00:00

15. Outro00:00:00

16. Notebook Topic Modeling

17. [SOLUTION] Topic Modeling

18. Next Steps

Sentiment Analysis

Sequence to Sequence

Deep Learning Attention

RNN Keras Lab

Cloud Computing Setup Instructions

Project Machine Translation

Intro to Voice User Interfaces

(Optional) Alexa History Skill

Speech Recognition

01. Intro00:00:00

02. Challenges in ASR00:00:00

03. Signal Analysis00:00:00

04. References: Signal Analysis

05. Quiz: FFT00:00:00

06. Feature Extraction with MFCC00:00:00

07. References: Feature Extraction

08. Quiz: MFCC00:00:00

09. Phonetics00:00:00

10. References: Phonetics

12. Voice Data Lab Introduction00:00:00

13. Lab: Voice Data

14. Acoustic Models and the Trouble with Time00:00:00

15. HMMs in Speech Recognition00:00:00

16. Language Models00:00:00

17. N-Grams00:00:00

18. Quiz: N-Grams

19. References: Traditional ASR

20. A New Paradigm00:00:00

21. Deep Neural Networks as Speech Models00:00:00

22. Connectionist Tempora Classification (CTC)

23. References: Deep Neural Network ASR

24. Outro00:00:00

11. Quiz: Phonetics

Project DNN Speech Recognizer

Recurrent Neural Networks

Long Short-Term Memory Networks (LSTM)

Keras

Sentiment Analysis with Andrew Trask

Sentiment Prediction RNN

Embeddings and Word2Vec

Project Part of Speech Tagging

LLM architecture

Pre-training models

Supervised Fine-Tuning

Reinforcement Learning from Human Feedback

Evaluation

Quantization

Inference optimization

Level

Intermediate

Lectures

257 lectures

Subject

Natural Language Processing School of Artificial Intelligence

Material Includes

Workspaces
Hands-on Projects
Quizzes
Progress Tracker

Natural Language Processing Specialization Bootcamp

About This Course

Certificate

Requirements

Curriculum

Intro to NLP

NLP Overview1:12Preview

03. Structured Languages1:31

03. Structured Languages

04. Grammar0:35

04. Grammar

05. Unstructured Text1:17

05. Unstructured Text

06. Counting Words00:00:00

07. Context Is Everything1:34

08. NLP and Pipelines0:48

09. How NLP Pipelines Work1:11

10. Text Processing1:56

11. Feature Extraction2:48

12. Modeling0:54

Text Processing

01. Text Processing00:00:46Preview

02. Coding Exercises

03. Introduction to GPU Workspaces

04. Workspaces Best Practices

05. Workspace

06. Capturing Text Data00:1:18

07. Cleaning6:24

08. Normalization2:19

09. Tokenization00:00:00

10. Stop Word Removal00:00:00

11. Part-of-Speech Tagging00:00:00

12. Named Entity Recognition00:00:00

13. Stemming and Lemmatization00:00:00

14. Summary00:00:00

08. Normalization

Spam Classifier with Naive Bayes

01. Intro0:22

02. Guess the Person2:01

03. Known and Inferred00:00:00

04. Guess the Person Now00:00:00

05. Bayes Theorem00:00:00

06. Quiz False Positives00:00:00

06. Quiz: False Positives

07. Solution False Positives00:00:00

08. Bayesian Learning 100:00:00

08.2 Bayesian Learning 1

08.2 Bayesian Learning 1

09. Bayesian Learning 200:00:00

10. Bayesian Learning 300:00:00

11. Naive Bayes Algorithm 100:00:00

11. Naive Bayes Algorithm 1

12. Naive Bayes Algorithm 200:00:00

13. Building a Spam Classifier

14. Project

15. Spam Classifier – Workspace

16. Outro

Part of Speech Tagging with HMMs

01. Intro00:00:00

02. Part of Speech Tagging00:00:00

03. Lookup Table00:00:00

04. Bigrams00:00:00

05. When bigrams won’t work00:00:00

06. Hidden Markov Models3:07

07. Quiz How many paths00:00:00

08. Solution How many paths1:24

09. Quiz How many paths now00:00:00

10. Quiz Which path is more likely00:00:00

10. Quiz Which path is more likely

11. Solution Which path is more likely00:00:00

12. Viterbi Algorithm Idea00:00:00

13. Viterbi Algorithm00:00:00

14. Further Reading

15. Outro00:00:00

Project Part of Speech Tagging

01. Lesson Plan – Week 3

02. Introduction

03. Workspace Part of Speech Tagging

Project Description – Part of Speech Tagging

Project Rubric – Part of Speech Tagging