Natural Language Processing

4 courses

Introduction to Natural Language Processing

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.

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This learning path includes:

4 courses with bite-sized lessons and practices

18 engaging lessons in text and video formats

78 hands-on practices

One-on-one guidance from Cosmo, our AI tutor

Verified skills you'll gain

DEVELOPING

Text Data Collection and Preparation

DEVELOPING

Feature Engineering and Text Representation

DEVELOPING

Machine Learning Modeling for NLP

Tools you'll use

MatPlotLib

Pandas

Python

Scikit-learn

Seaborn

Trusted by learners working at top companies

4 lessons

18 practices

Introduction to Text Data Exploration in Python

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.

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5 lessons

21 practices

Text Data Preprocessing in Python

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.

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4 lessons

19 practices

Introduction to TF-IDF Vectorization in Python

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.

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5 lessons

20 practices

Building and Evaluating Text Classifiers in Python

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.

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Meet Cosmo: The smartest AI guide in the universe

Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.

Whether you’re leveling up or just starting out, find your next step here.