Have you wondered how autocorrect work when we misspelled a word on our smartphone or writing an email? In this post, I will go over two topics about how autocorrect system works:

- How to build a simple autocorrect system.
- What is minimum edit distance and what does it do.

This post is my continue development path on Deep Learning NLP, based on week 1 of Natural Language Processing with Probabilistic Models course on Coursera.

**Check out my final project here**: Click Link

In week 1 of the Natural Language Processing with Probabilistic Models, I learned and built a simple but…

Language is built on grammar. The parts of speech are important because they show us how the words relate to each other. Knowing whether a word is a noun or a verb tells us a lot about likely neighboring words and about the syntactic structure around the word. Therefore, Parts-of-speech tagging serves as a very important building block in NLP application.

In this post, I’m going to talk about

- What is part of speech tagging (POS)
- What are Markov Chains Models
- Markov Chains Models and POS tagging
- Hidden Markov Chains and calculate transition and emission probabilities
- Use Viterbi algorithm in…

Sentiment analysis is extremely useful in current days as it allows us to gain an overview of the wider opinion behind certain topics. For example, analyzing customer review can helps us see how positive or negative our customer feeling our product. Human being can understand the feeling of a text quite easily. However, if we rely on human to manually classify the sentiment of a large given text, it’s clearly not efficient. Instead, we can apply NLP technique to do sentiment analysis at scale.

For this topic, I’m going to talk about:

- Part 1: Sentiment Analysis with Logistic Regression
- Part…

In Sentiment Analysis with Logistic Regression (Part 1), we talk about the overall approach on how to do sentiment analysis with Logistic Regression. In this post, we are going to talk about how we can do sentiment analysis with Naive Bayes.

For this topic, I’m going to talk about:

- Introduce probability and the Bayes’ Rule
- What is the Bayes’ Rule
- Naive Bayes for sentiment analysis
- Log Likelihood for dealing with numerical underflow
- Training Naive Bayes
- Inference and Testing Naive Bayes Model
- Naive Bayes Assumptions
- Optional: Naive Bayes Applications
- Optional: Error Analysis

*Disclaimer: This post is based on week 2 of…*

In Sentiment Analysis with Logistic Regression (Part 1), we talk about the overall approach on how to do sentiment analysis with Logistic Regression. In this post, we are going to review what’s Logistic Regression.

This post is the part 2 of “Sentiment Analysis with Logistic Regression”. In this post, I’m going to talk about:

- Overview of Logistic Regression
- Training Logistic Regression
- Testing Logistic Regression
- Logistic Regression Cost Function

*Disclaimer: This post is based on week 1 of **Natural Language Processing with Classification and Vector Spaces** course on Coursera. Most of the figures below credits goes to the course copy right.*

Machine learning data scientist, blogger and course facilitator who help organization to unpack the value of data in business