- Video : Welcome to the NLP Specialization
- Video : Welcome to course 1
- Reading : Acknowledgment - Ken Church
- Video : Week Introduction
- Video : Supervised ML & Sentiment Analysis
- Reading : Supervised ML & Sentiment Analysis
- Video : Vocabulary & Feature Extraction
- Reading : Vocabulary & Feature Extraction
- Video : Negative and Positive Frequencies
- Video : Feature Extraction with Frequencies
- Reading : Feature Extraction with Frequencies
- Video : Preprocessing
- Reading : Preprocessing
- Lab : Natural Language preprocessing
- Video : Putting it All Together
- Reading : Putting it all together
- Lab : Visualizing word frequencies
- Video : Logistic Regression Overview
- Reading : Logistic Regression Overview
- Video : Logistic Regression: Training
- Reading : Logistic Regression: Training
- Lab : Visualizing tweets and Logistic Regression models
- Video : Logistic Regression: Testing
- Reading : Logistic Regression: Testing
- Video : Logistic Regression: Cost Function
- Reading : Optional Logistic Regression: Cost Function
- Video : Week Conclusion
- Reading : Optional Logistic Regression: Gradient
- Ungraded App Item : Intake Survey)
- Have questions, issues or ideas? Join our Community!
- Reading : Lecture Notes W1
- Practice Quiz : Logistic Regression
- Reading : (Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace
- Programming Assignment : Logistic Regression
- Video : Andrew Ng with Chris Manning
- Video : Week Introduction
- Video : Probability and Bayes’ Rule
- Reading : Probability and Bayes’ Rule
- Video : Bayes’ Rule
- Reading : Bayes' Rule
- Video : Naïve Bayes Introduction
- Reading : Naive Bayes Introduction
- Video : Laplacian Smoothing
- Reading : Laplacian Smoothing
- Video : Log Likelihood, Part 1
- Reading : Log Likelihood, Part 1
- Video : Log Likelihood, Part 2
- Reading : Log Likelihood Part 2
- Video : Training Naïve Bayes
- Reading : Training naïve Bayes
- Lab : Visualizing likelihoods and confidence ellipses
- Video : Testing Naïve Bayes
- Reading : Testing naïve Bayes
- Video : Applications of Naïve Bayes
- Reading : Applications of Naive Bayes
- Video : Naïve Bayes Assumptions
- Reading : Naïve Bayes Assumptions
- Video : Error Analysis
- Reading : Error Analysis
- Video : Week Conclusion
- Lecture Notes W2
- Naive Bayes
- Programming Assignment : Naive Bayes
- Video : Week Introduction
- Video : Vector Space Models
- Reading : Vector Space Models
- Video : Word by Word and Word by Doc.
- Reading : Word by Word and Word by Doc.
- Lab : Linear algebra in Python with Numpy
- Video : Euclidean Distance
- Reading : Euclidian Distance
- Video : Cosine Similarity: Intuition
- Reading : Cosine Similarity: Intuition
- Video : Cosine Similarity
- Reading : Cosine Similarity
- Video : Manipulating Words in Vector Spaces
- Reading : Manipulating Words in Vector Spaces
- Lab : Manipulating word embeddings
- Video : Visualization and PCA
- Reading : Visualization and PCA
- Video : PCA Algorithm
- Reading : PCA algorithm
- Lab : Another explanation about PCA
- Reading : The Rotation Matrix (Optional Reading)
- Video : Week Conclusion
- Reading : Lecture Notes W3
- Practice Quiz : Vector Space Models
- Programming Assignment : Assignment: Vector Space Models
- Video : Week Introduction
- Video : Overview
- Video : Transforming word vectors
- Reading : Transforming word vectors
- Lab : Rotation matrices in R2
- Video : K-nearest neighbors
- Reading : K-nearest neighbors
- Video : Hash tables and hash functions
- Reading : Hash tables and hash functions
- Video : Locality sensitive hashing
- Reading : Locality sensitive hashing
- Video : Multiple Planes
- Reading : Multiple Planes
- Lab : Hash tables
- Video : Approximate nearest neighbors
- Reading : Approximate nearest neighbors
- Video : Searching documents
- Reading : Searching documents
- Video : Week Conclusion
- Reading : Lecture Notes W4
- Practice Quiz : Hashing and Machine Translation
- Programming Assignment : Word Translation
- Reading : Acknowledgements
- Reading : Bibliography
- Video : Andrew Ng with Kathleen McKeown
- Video : Intro to Course 2
- Video : Week Introduction
- Video : Overview
- Reading : Overview
- Video : Autocorrect
- Reading : Autocorrect
- Video : Building the model
- Reading : Building the model
- Lab : Lecture notebook: Building the vocabulary
- Video : Building the model II
- Reading : Building the model II
- Lab : Lecture notebook: Candidates from edits
- Video : Minimum edit distance
- Reading : Minimum edit distance
- Video : Minimum edit distance algorithm
- Reading : Minimum edit distance algorithm
- Video : Minimum edit distance algorithm II
- Reading : Minimum edit distance algorithm II
- Video : Minimum edit distance algorithm III
- Reading : Minimum edit distance III
- Video : Week Conclusion
- Ungraded App Item : [IMPORTANT] Have questions, issues or ideas? Join our Community!
- Reading : Lecture Notes W1
- Practice Quiz : Auto-correct and Minimum Edit Distance
- Reading : (Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace
- Programming Assignment : Autocorrect
- Video : Week Introduction
- Video : Part of Speech Tagging
- Reading : Part of Speech Tagging
- Lab : Lecture Notebook - Working with text files
- Video : Markov Chains
- Reading : Markov Chains
- Video : Markov Chains and POS Tags
- Reading : Markov Chains and POS Tags
- Video : Hidden Markov Models
- Reading : Hidden Markov Models
- Video : Calculating Probabilities
- Reading : Calculating Probabilities
- Video : Populating the Transition Matrix
- Reading : Populating the Transition Matrix
- Video : Populating the Emission Matrix
- Reading : Populating the Emission Matrix
- Lab : Lecture Notebook - Working with tags and Numpy
- Video : The Viterbi Algorithm
- Reading : The Viterbi Algorithm
- Video : Viterbi: Initialization
- Reading : Viterbi: Initialization
- Video : Viterbi: Forward Pass
- Reading : Viterbi: Forward Pass
- Video : Viterbi: Backward Pass
- Reading : Viterbi: Backward Pass
- Video : Week Conclusion
- Reading : Lecture Notes W2
- Practice Quiz : Part of Speech Tagging
- Programming Assignment : Part of Speech Tagging
- Video : Week Introduction
- Video : N-Grams: Overview
- Reading : N-Grams: Overview
- Video : N-grams and Probabilities
- Reading : N-grams and Probabilities
- Video : Sequence Probabilities
- Reading : Sequence Probabilities
- Video : Starting and Ending Sentences
- Reading : Starting and Ending Sentences
- Lab : Lecture notebook: Corpus preprocessing for N-grams
- Video : The N-gram Language Model
- Reading : The N-gram Language Model
- Video : Language Model Evaluation
- Lab : Lecture notebook: Building the language model
- Reading : Language Model Evaluation
- Video : Out of Vocabulary Words
- Reading : Out of Vocabulary Words
- Video : Smoothing
- Reading : Smoothing
- Lab : Lecture notebook: Language model generalization
- Video : Week Summary
- Reading : Week Summary
- Video : Week Conclusion
- Reading : Lecture Notes W3
- Practice Quiz : Autocomplete
- Programming Assignment : Autocomplete
- Video : Week Introduction
- Video : Overview
- Reading : Overview
- Video : Basic Word Representations
- Reading : Basic Word Representations
- Video : Word Embeddings
- Reading : Word Embeddings
- Video : How to Create Word Embeddings
- Reading : How to Create Word Embeddings?
- Video : Word Embedding Methods
- Reading : Word Embedding Methods
- Video : Continuous Bag-of-Words Model
- Reading : Continuous Bag-of-Words Model
- Video : Cleaning and Tokenization
- Reading : Cleaning and Tokenization
- Video : Sliding Window of Words in Python
- Reading : Sliding Window of Words in Python
- Video : Transforming Words into Vectors
- Reading : Transforming Words into Vectors
- Lab : Lecture Notebook - Data Preparation
- Video : Architecture of the CBOW Model
- Reading : Architecture of the CBOW Model
- Video : Architecture of the CBOW Model: Dimensions
- Reading : Architecture of the CBOW Model: Dimensions
- Video : Architecture of the CBOW Model: Dimensions 2
- Reading : Architecture of the CBOW Model: Dimensions 2
- Video : Architecture of the CBOW Model: Activation Functions
- Reading : Architecture of the CBOW Model: Activation Functions
- Lab : Lecture Notebook - Intro to CBOW model
- Video : Training a CBOW Model: Cost Function
- Reading : Training a CBOW Model: Cost Function
- Video : Training a CBOW Model: Forward Propagation
- Reading : Training a CBOW Model: Forward Propagation
- Video : Training a CBOW Model: Backpropagation and Gradient Descent
- Reading : Training a CBOW Model: Backpropagation and Gradient Descent
- Lab : Lecture Notebook - Training the CBOW model
- Video : Extracting Word Embedding Vectors
- Reading : Extracting Word Embedding Vectors
- Lab : Lecture Notebook - Word Embeddings
- Video : Evaluating Word Embeddings: Intrinsic Evaluation
- Reading : Evaluating Word Embeddings: Intrinsic Evaluation
- Video : Evaluating Word Embeddings: Extrinsic Evaluation
- Reading : Evaluating Word Embeddings: Extrinsic Evaluation
- Lab : Lecture notebook: Word embeddings step by step
- Video : Conclusion
- Reading : Conclusion
- Video : Week Conclusion
- Reading : Lecture Notes W4
- Practice Quiz : Word Embeddings
- Reading : [IMPORTANT] Reminder about end of access to Lab Notebooks
- Programming Assignment : Word Embeddings
- Reading : Acknowledgments