Learn to extract features from text into numerical vectors, then build a binary classifier for tweets using a logistic regression!
- Sentiment analysis
- Logistic regression
- Data pre-processing
- Calculating word frequencies
- Feature extraction
- Vocabulary creation
- Supervised learning
- Visualising Tweets
Sentiment Analysis with Logistic Regression
Learn the theory behind Bayes' rule for conditional probabilities, then apply it toward building a Naive Bayes tweet classifier of your own!
- Error analysis
- Naive Bayes inference
- Log likelihood
- Laplacian smoothing
- conditional probabilities
- Bayes rule
- Sentiment analysis
- Vocabulary creation
- Supervised learning
Sentiment Analysis with Naïve Bayes
Vector space models capture semantic meaning and relationships between words. You'll learn how to create word vectors that capture dependencies between words, then visualize their relationships in two dimensions using PCA.
- Covariance matrices
- Dimensionality reduction
- Principal component analysis
- Cosine similarity
- Euclidean distance
- Co-occurrence matrices
- Vector representations
- Vector space models
- Gradient descent
- Approximate nearest neighbors
- Locality sensitive hashing
- Hash functions
- Hash tables
- K nearest neighbors
- Document search
- Machine translation
- Frobenius norm