This learning path offers foundational knowledge in Natural Language Processing (NLP). It covers data exploration, preprocessing, text vectorization, and machine learning for text classification. Gain proficiency in transforming text into insights and implementing models to classify text.
This introductory course guides you through the initial yet critical steps of any data science project: data exploration. By utilizing Python and specifically pandas, you'll learn to load, inspect, and analyze datasets to gain fundamental insights. These steps are crucial for preparing you for Natural Language Processing explorations.
Building and Evaluating Text Classifiers in Python
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21 practices
Learn to clean and prepare textual data for machine learning models using Python. This course teaches you to apply basic preprocessing tasks such as text lowercasing, removing stopwords, tokenization, and stemming on the SMS Spam Collection dataset. By the end of this course, you’ll have the skills to transform raw text into a format that's ready for NLP tasks.
Venture into the world of text vectorization with a focus on TF-IDF (Term Frequency-Inverse Document Frequency) in Python. Through this course, you'll learn how to convert text into numerical features that machine learning models can work with. Using the SMS Spam Collection dataset, you will understand how to apply TF-IDF to prepare text data for predictive modeling.
Progress from preprocessing text data to building predictive models with this practical course. You'll learn how to leverage machine learning algorithms, such as Naive Bayes and logistic regression, to classify text into categories. Using the preprocessed SMS Spam Collection dataset, the course guides you through training classifiers, making predictions, and evaluating their performance.
