What will you learn in this Natural Language Processing with Classification and Vector Spaces Course
Sentiment Analysis: Implement logistic regression and naïve Bayes classifiers to analyze the sentiment of textual data, such as tweets
Vector Space Models: Understand and apply vector space models to capture semantic relationships between words, utilizing techniques like Principal Component Analysis (PCA) for dimensionality reduction and visualization
Machine Translation: Develop a simple English-to-French translation algorithm using pre-computed word embeddings and locality-sensitive hashing for approximate nearest neighbor search.
Program Overview
1. Sentiment Analysis with Logistic Regression
⏳ 9 hours
Extract features from text into numerical vectors.
Build a binary classifier for tweets using logistic regression.
Understand preprocessing steps and feature extraction techniques.
2. Sentiment Analysis with Naïve Bayes
⏳ 8 hours
Learn the theory behind Bayes’ rule and conditional probabilities.
Apply these concepts to build a Naïve Bayes tweet classifier.
Compare performance with logistic regression models.
3. Vector Space Models
⏳ 8 hours
Create word vectors that capture dependencies between words.
Use PCA to reduce dimensionality and visualize word relationships.
Explore semantic meaning and relationships in vector spaces.
4. Machine Translation and Document Search
⏳ 8 hours
Transform word vectors and assign them to subsets using locality-sensitive hashing.
Implement a simple English-to-French translation algorithm.
Apply techniques to perform document search based on semantic similarity.
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Job Outlook
Proficiency in NLP techniques is increasingly sought after in roles such as Data Scientist, NLP Engineer, and Machine Learning Engineer.
Understanding foundational NLP concepts is essential for developing applications like chatbots, sentiment analysis tools, and translation services.
Completing this course can enhance your qualifications and visibility to potential employers in the AI and data science fields.
Specification: Natural Language Processing with Classification and Vector Spaces
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