What will you learn in this Natural Language Processing with Classification and Vector Spaces Course
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Sentiment Analysis: Implement logistic regression and naïve Bayes classifiers to analyze the sentiment of textual data, such as tweets
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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
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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
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Extract features from text into numerical vectors.
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Build a binary classifier for tweets using logistic regression.
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Understand preprocessing steps and feature extraction techniques.
2. Sentiment Analysis with Naïve Bayes
⏳ 8 hours
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Learn the theory behind Bayes’ rule and conditional probabilities.
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Apply these concepts to build a Naïve Bayes tweet classifier.
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Compare performance with logistic regression models.
3. Vector Space Models
⏳ 8 hours
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Create word vectors that capture dependencies between words.
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Use PCA to reduce dimensionality and visualize word relationships.
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Explore semantic meaning and relationships in vector spaces.
4. Machine Translation and Document Search
⏳ 8 hours
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Transform word vectors and assign them to subsets using locality-sensitive hashing.
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Implement a simple English-to-French translation algorithm.
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Apply techniques to perform document search based on semantic similarity.
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Job Outlook
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Proficiency in NLP techniques is increasingly sought after in roles such as Data Scientist, NLP Engineer, and Machine Learning Engineer.
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Understanding foundational NLP concepts is essential for developing applications like chatbots, sentiment analysis tools, and translation services.
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Completing this course can enhance your qualifications and visibility to potential employers in the AI and data science fields.
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What Is Python Used For? – Understand how Python serves as the backbone for NLP development, including text processing, machine learning, and model deployment.
