• Stars
    star
    721
  • Rank 62,814 (Top 2 %)
  • Language
    Jupyter Notebook
  • Created over 4 years ago
  • Updated about 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

This repo contains my coursework, assignments, and Slides for Natural Language Processing Specialization by deeplearning.ai on Coursera

Natural Language Processing Specialization

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. This Specialization will equip you with the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots.

This Specialization is for students of machine learning or artificial intelligence as well as software engineers looking for a deeper understanding of how NLP models work and how to apply them. Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. If you would like to brush up on these skills, we recommend the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng.

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Course 1: Classification and Vector Spaces in NLP

This is the first course of the Natural Language Processing Specialization.

Week 1: Logistic Regression for Sentiment Analysis of Tweets

  • Use a simple method to classify positive or negative sentiment in tweets

Week 2: Naïve Bayes for Sentiment Analysis of Tweets

  • Use a more advanced model for sentiment analysis

Week 3: Vector Space Models

  • Use vector space models to discover relationships between words and use principal component analysis (PCA) to reduce the dimensionality of the vector space and visualize those relationships

Week 4: Word Embeddings and Locality Sensitive Hashing for Machine Translation

  • Write a simple English-to-French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbors search

Course 2: Probabilistic Models in NLP

This is the second course of the Natural Language Processing Specialization.

Week 1: Auto-correct using Minimum Edit Distance

  • Create a simple auto-correct algorithm using minimum edit distance and dynamic programming

Week 2: Part-of-Speech (POS) Tagging

  • Apply the Viterbi algorithm for POS tagging, which is important for computational linguistics

Week 3: N-gram Language Models

  • Write a better auto-complete algorithm using an N-gram model (similar models are used for translation, determining the author of a text, and speech recognition)

Week 4: Word2Vec and Stochastic Gradient Descent

  • Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model

Course 3: Sequence Models in NLP

This is the third course in the Natural Language Processing Specialization.

Week 1: Sentiment with Neural Nets

  • Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets

Week 2: Language Generation Models

  • Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model

Week 3: Named Entity Recognition (NER)

  • Train a recurrent neural network to perform NER using LSTMs with linear layers

Week 4: Siamese Networks

  • Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning

Course 4: Attention Models in NLP

This is the fourth course in the Natural Language Processing Specialization.

Week 1: Neural Machine Translation with Attention

  • Translate complete English sentences into French using an encoder/decoder attention model

Week 2: Summarization with Transformer Models

  • Build a transformer model to summarize text

Week 3: Question-Answering with Transformer Models

  • Use T5 and BERT models to perform question answering

Week 4: Chatbots with a Reformer Model

  • Build a chatbot using a reformer model

Specialization Completion Certificate

Certificate

More Repositories

1

AI-for-Healthcare-Nanodegree

Learn to build, evaluate, and integrate predictive models that have the power to transform patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis and then move on to modeling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion.
Jupyter Notebook
44
star
2

Time-Series-Analysis-and-Forecasting

End To End Tutorial on Time Series Analysis and Forcasting
Jupyter Notebook
18
star
3

AI-For-Medicine-Specialization

This repo contains the coursework and Assignments for AI for Medicine Specialization by deeplearning.ai on Coursera
Jupyter Notebook
11
star
4

Recommender-Systems-Using-Python

This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.
Python
7
star
5

Generative-Adversarial-Networks-GANs-Specialization

This repo contains my coursework, assignments, Slides and Notes for Generative Adversarial Networks (GANs) Specialization by deeplearning.ai on Coursera
6
star
6

Python-Search-Spider-Page-Ranker-and-Visualizer

This is a set of programs that emulate some of the functions of a search engine. This program crawls a web site and pulls a series of pages into the database, recording the links between pages. Then It calculates the Page Rank of Each site using PageRank Algorithm and stores it in the Database
Python
4
star
7

Apache-Spark-Tutorials

This repo contains my learnings and practice notebooks on Spark using PySpark (Python Language API on Spark). All the notebooks in the repo can be used as template code for most of the ML algorithms and can be built upon it for more complex problems.
Jupyter Notebook
4
star
8

Machine-Translation-using-seq-2-seq-RNNs

In this repo, I have built a deep neural network that functions as part of an end-to-end machine translation pipeline. This complete pipeline will accept English text as input and return the French translation.
HTML
2
star
9

Patient-Selection-for-Diabetes-Drug-Testing

In this repo, I have build a regression model that can predict the estimated hospitalization time for a patient and also provide an uncertainty estimate range for that prediction so that you can rank the predictions based off of the uncertainty range.
Jupyter Notebook
2
star
10

Machine-Learning-Engineering-for-Production-MLOps-Specialization

This repo contains my coursework, assignments, and Slides for Machine Learning Engineering for Production (MLOps) Specialization by deeplearning.ai on Coursera
2
star
11

Pneumonia-Detection-using-chest-X-Rays

In this repo, I have analyzed 2D medical imaging data from the NIH Chest X-ray Dataset and train a CNN to classify a given chest x-ray for the presence or absence of pneumonia
Jupyter Notebook
2
star
12

Parts-of-Speech-Tagging-Using-Hidden-Markov-Models

In this repo I have used Pomegranate library to build a hidden Markov model for part of speech tagging with a universal tagset.
HTML
1
star
13

MovieLens-Data-Analysis

Jupyter Notebook
1
star
14

Deep-Learning

Jupyter Notebook
1
star
15

Tensorflow-Notebooks

Jupyter Notebook
1
star
16

Time-Series-Data-Visualisation

Creating and Customizing Plots using Pandas Built-In Charts
Jupyter Notebook
1
star
17

Human-Activity-Recognition-with-Smartphones-Dataset

Jupyter Notebook
1
star
18

Time-Series-With-Pandas

How to work with and Manipulate TIme Series Data using Pandas
Jupyter Notebook
1
star
19

Predicting-Bike-Sharing-Patterns

Predicting Bike Sharing Patterns using a Deep Neural Network Build from Scratch
Jupyter Notebook
1
star
20

Image-Captioning-using-Deep-Learning

In this repo I have built an CNN-RNN model which can automatically generate captions from images. I have the Microsoft Common Objects in COntext (MS COCO) dataset to train my network and tested it on novel images.
Jupyter Notebook
1
star
21

Motion-Compensated-Pulse-Rate-Estimation

In this repo, I have developed a Pulse Rate Algorithm which estimates pulse rate from the PPG signal and a 3-axis accelerometer and applied the Pulse Rate Algorithm on a Clinical Application and compute more clinically meaningful features and discover healthcare trends.
Jupyter Notebook
1
star
22

Deep-Neural-Network-Speech-Recognizer

In this repo, I have built a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline.
HTML
1
star
23

Self-Driving-Car-Engineer-Projects

Self-driving cars are transformational technology, on the cutting-edge of robotics, machine learning, and engineering. Learn the skills and techniques used by self-driving car teams at the most advanced technology companies in the world.
Jupyter Notebook
1
star
24

Dog-Breed-Classifier

Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.
Jupyter Notebook
1
star